Volume modeling of soils using GRASS GIS 3D-Tools presented at Second Italian GRASS Users M

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An-interdisciplinary-approach-towards-improved-understanding-of-soil-deformation-during-compaction

ReviewAn interdisciplinary approach towards improved understanding of soil deformation during compactionT.Keller a,b,1,*,mande´c,1,S.Peth d,e,M.Berli f,J.-Y.Delenne g,W.Baumgarten d,W.Rabbel h,F.Radjaı¨g,J.Rajchenbach i,A.P.S.Selvadurai j,D.Or ka Agroscope Reckenholz-Ta¨nikon Research Station ART,Department of Natural Resources and Agriculture,Reckenholzstrasse191,CH-8046Zu¨rich,Switzerlandb Swedish University of Agricultural Sciences,Department of Soil&Environment,Box7014,SE-75007Uppsala,Swedenc Aarhus University,Department of Agroecology&Environment,Research Centre Foulum,P.O.Box50,DK-8830Tjele,Denmarkd Christian-Albrechts-University Kiel,Institute for Plant Nutrition and Soil Science,Hermann-Rodewaldstrasse2,D-24118Kiel,Germanye University of Kassel,Faculty11,Organic Agricultural Sciences,Group of Soil Science,Nordbahnhofstrasse1a,D-37213Witzenhausen,Germanyf Desert Research Institute,Division of Hydrologic Sciences,755E.Flamingo Road,Las Vegas,NV,USAg Universite´Montpellier2,Physics and Mechanics of Granular Media,Cc048,F-34095Montpellier Cedex5,Franceh Christian-Albrechts-University Kiel,Institute of Geosciences,Otto-Hahn-Platz1,D-24118Kiel,Germanyi Universite´de Nice,Laboratoire de Physique de la Matie`re Condense´e,28Avenue Valrose,F-06108Nice Cedex2,Francej McGill University,Department of Civil Engineering and Applied Mechanics,817Sherbrooke Street West,Montre´al,QC,H3A2K6,Canadak Swiss Federal Institute of Technology,Soil and Terrestrial Environmental Physics,Universita¨tstrasse16,CH-8092Zu¨rich,SwitzerlandContents1.Introduction (62)2.Deformation of porous media:theory,approaches and applications of different researchﬁelds (63)2.1.Soil physics and soil mechanics (63)2.1.1.Soil physics and soil mechanics–similar subject,different approaches (63)Soil&Tillage Research128(2013)61–80A R T I C L E I N F OArticle history:Received22June2012Received in revised form28September2012Accepted8October2012Keywords:Soil compactionContinuum mechanicsGranular mediaSeismic methodsModellingX-ray computed tomographyA B S T R A C TSoil compaction not only reduces available pore volume in whichﬂuids are stored,but it alters thearrangement of soil constituents and pore geometry,thereby adversely impactingﬂuid transport and arange of soil ecological functions.Quantitative understanding of stress transmission and deformationprocesses in arable soils remains limited.Yet such knowledge is essential for better predictions of effectsof soil management practices such as agriculturalﬁeld trafﬁc on soil functioning.Concepts and theoryused in agricultural soil mechanics(soil compaction and soil tillage)are often adopted from conventionalsoil mechanics(e.g.foundation engineering).However,in contrast with standard geotechnicalapplications,undesired stresses applied by agricultural tyres/tracks are highly dynamic and last forvery short times.Moreover,arable soils are typically unsaturated and contain important secondarystructures(e.g.aggregates),factors important for affecting their soil mechanical behaviour.Mechanicalprocesses in porous media are not only of concern in soil mechanics,but also in otherﬁelds includinggeophysics and granular material science.Despite similarity of basic mechanical processes,theoreticalframeworks often differ and reﬂect disciplinary focus.We review concepts from different butcomplementaryﬁelds concerned with porous media mechanics and highlight opportunities forsynergistic advances in understanding deformation and compaction of arable soils.We highlight theimportant role of technological advances in non-destructive measurement methods at pore(X-raytomography)and soil proﬁle(seismic)scales that not only offer new insights into soil architecture andenable visualization of soil deformation,but are becoming instrumental in the development andvalidation of new soil compaction models.The integration of concepts underlying dynamic processesthat modify soil pore spaces and bulk properties will improve the understanding of how soilmanagement affect vital soil mechanical,hydraulic and ecological functions supporting plant growth.ß2012Elsevier B.V.All rights reserved.*Corresponding author.Tel.:+41443777605;fax:+41443777201.E-mail addresses:Thomas.Keller@slu.se,thomas.keller@art.admin.ch(T.Keller).1These authors contributed equally to this work.Contents lists available at SciVerse ScienceDirectSoil&Tillage Researchj ou r n a l h o m e p a g e:w w w.e l s e v i e r.co m/l o c a t e/s t i l l0167-1987/$–see front matterß2012Elsevier B.V.All rights reserved./10.1016/j.still.2012.10.0042.1.2.Mechanics of agricultural and forest soils (63)2.1.3.Soil rheology (63)2.2.Geomechanics (64)2.2.1.Mechanical behaviour of an isotropic elasto-plastic saturated material (65)2.2.2.Poroelasto-plastic behaviour of a one-dimensional column (65)2.3.Geophysics (66)2.3.1.Electrical conductivity (67)2.3.2.Electrical permittivity (67)2.3.3.Seismic methods (68)2.4.Physics of granular media (68)3.Modelling approaches (69)3.1.Analytical solutions for stress transmission (69)3.1.1.Inﬂuence of geomaterial inhomogeneity on stress transmission (70)3.2.Theﬁnite element method (70)3.2.1.Essential ingredients of the FEM (70)3.2.2.Potential and limitations of modelling soil compaction with FEM (71)3.3.The discrete element method (71)4.Non-destructive measurement techniques for soil structure and deformation (72)puted tomography (72)4.1.1.Background (72)4.1.2.Potential and limitations of CT and m CT in soil compaction research (73)4.2.Scanning electron microscopy (73)4.3.Seismic methods (74)5.Synthesis:potentials and challenges (74)5.1.Scale-dependent soil structural organization and how it inﬂuences soil strength,stress transmission and soil deformation (74)5.2.Mechanical deformation as a time(rate)-dependent process (75)5.3.Visualization of soil structure and soil deformation (76)5.4.Linking seismic measurements to soil mechanical properties (76)6.Conclusions (77)Acknowledgements (77)References (77)1.IntroductionSoil compaction(i.e.reduction of soil porosity)is one of the main threats to sustaining soil quality in Europe(COM,2006).A range of important ecological functions is affected when soil is compacted(e.g.van Ouwerkerk and Soane,1995;Alaoui et al., 2011):compaction reduces saturated hydraulic conductivity and thus triggers surface runoff and soil erosion by water;it may induce preferentialﬂow in macropores,and hence facilitates transport of colloid-adsorbed nutrients and pesticides to deeper horizons and water bodies;compaction reduces soil aeration,and hence reduces root growth and induces loss of nitrogen and production of greenhouse gases through denitriﬁcation by anaerobic processes.Consequently,soil compaction is one of the causes of a number of environmental and agronomic problems (ﬂooding,erosion,leaching of chemicals to water bodies,crop yield losses)resulting in signiﬁcant economic damage to the society and agriculture.Our understanding of deformation processes in arable soil is still limited.One reason might be that in soil compaction research, the major focus has been on the agronomic(and,more recently, environmental)impacts of compaction,rather than on the soil deformation process itself.That is,the externally applied mechanical stress(e.g.by agricultural machinery)is related to (physical)soil functions or crop response.While such studies are undoubtedly valuable,they do not directly add to the knowledge of the soil compaction process itself.In order to understand the impacts of soil compaction on soil functions(reaction),knowledge of the soil deformation process(cause)is needed.Soil deformation is the response of a soil to an applied stress, which could be either mechanical or hydraulic.Stress and deformation are coupled processes:soil deformation is a function of soil stress and soil strength,and the propagation of stress in soil is a function of soil strength and soil deformation.A better mechanistic understanding of stress transmission in structured soil and the deformation behaviour of unsaturated soil will improve models for prediction of soil compaction.Such models are needed to develop strategies and guidelines for the prevention of soil compaction.Furthermore,improved under-standing of the soil deformation processes will promote better predictions of the impact of soil management practices on physical soil functions and their effect on processes listed above.Concepts and theory used in agricultural soil mechanics(soil compaction and soil tillage)are generally adopted from conven-tional soil mechanics(e.g.foundation engineering).Examples are analytical solutions for stress propagation in soil based on the works of Boussinesq(1885)and Fro¨hlich(1934),soil compressive strength characterized by the precompression stress that is based on Casagrande(1936),or models for predicting draught force of tillage implements that are based on early studies of Coulomb (1776)and Rankine(1858).Nevertheless,agricultural soil mechanics differs from geo-technical applications in a range of aspects,most notably:(i) stresses applied by running tyres,tracks,and tillage implements are dynamic and the loading time is very short($1s);(ii) agricultural soil is typically unsaturated,which makes interac-tions between hydraulics and mechanics very important,but also complex(Horn et al.,1998;Richards and Peth,2009);and(iii) arable soil is structured with compound particles of different sizes and shapes and a network of voids with various morphologies and orientation(e.g.biopores,shear fractures,desiccation cracks). Consequently,models for calculating stress transmission and deformation in arable soil that are based on foundation engineering concepts suffer from insufﬁcient knowledge of soil structure(fabric)and soil moisture effects on stress transmission, and poor characterization of soil mechanical properties relevant for short-term dynamic loading occurring on agricultural soils (Keller and Lamande´,2010).T.Keller et al./Soil&Tillage Research128(2013)61–80 62Stress transmission and deformation processes in porous media are not only a topic in soil mechanics and geomechanics,but also in other researchﬁelds including geophysics,granular material science,and snow/avalanche research.Although dealing with similar processes,the theoretical frameworks used are often research-ﬁeld speciﬁc.We believe that a combination of different approaches could advance our understanding of deformation processes in arable soils.An International Exploratory Workshop held during the autumn of2010at the Agroscope Research Station ART in Zu¨rich(Switzerland)brought together scientists dealing with porous media mechanics from different perspectives ranging from classical soil physics including soil mechanics to geomecha-nics,geophysics,and physics of granular mixtures.The aim of the workshop was to introduce and jointly discuss theoretical frameworks and modelling approaches applied in differentﬁelds to porous media mechanics,and to explore possible new approaches to quantitative description of soil compaction.The primary objectives of this paper were to:(i)review theory, modelling approaches and non-destructive measurement techni-ques applied in different researchﬁelds that deal with deformation of porous media,and(ii)delineate new approaches and pathways for improving the theoretical and experimental basis for modern soil compaction research.2.Deformation of porous media:theory,approaches and applications of different researchﬁeldsSeveral disciplines(such as soil mechanics,geotechnics, geophysics,granular material science,etc.)deal with deformation processes in porous media.However,the theoretical approaches that describe the physical nature of deformation and the frameworks to solve applications differ between disciplines.This can be attributed to differences in material properties,loading characteristics and boundary conditions of the relevant speciﬁc processes.Nevertheless,some of the distinctions may simply be due to historical reasons.2.1.Soil physics and soil mechanics2.1.1.Soil physics and soil mechanics–similar subject,different approachesIn one of the earliest textbooks on soil physics,Baver(1940) deﬁnes soil physical properties as:‘‘the mechanical behaviour of the soil mass’’.Around the same time,Terzaghi(1943)deﬁnes soil mechanics as:‘‘the application of the laws of mechanics and hydraulics to engineering problems dealing with sediments and other unconsolidated accumulations of solid particles produced by the mechanical and chemical disintegration of rocks’’.Although the two deﬁnitions address closely related topics,soil physics and mechanics evolved parallel with little interactions for a good part of the20th century.What happened?Soil physics was developed within the disciplines of agronomy and forestry,driven by the need for land reclamation,irrigation, and drainage as well as related topics such as groundwater recharge,salinization andﬂood control.Soil physics primarily focused on quantifying hydraulic properties and processes of partially saturated soils.In contrast,starting from the early work of Coulomb(1776),Rankine(1857)and Boussinesq(1885),soil mechanics was addressing foundation and slope stability problems related to fortiﬁcation,road and dam construction.As an engineering discipline,soil mechanics developed strongly in the early20th century with Fillunger(1913),Terzaghi(1923,1925) and Fro¨hlich(1934).With the development of more advanced mechanical models for soil such as Critical State Soil Mechanics (CSSM)(Roscoe et al.,1958;Schoﬁeld and Wroth,1968)and extending from saturated to unsaturated soils(e.g.Bishop,1959;Bailey and VandenBerg,1968;Alonso et al.,1990),frameworks became available that explicitly consider changes in void ratio(or porosity)as a function of applied stress as well as the impact of moisture conditions on the mechanical behaviour of soil.However,it is clear thatﬂuid transport through soil(primary focus of soil physics)and stability and deformation processes (main focus of soil mechanics)cannot be separated from each other,because any deformation results in a change inﬂuid transport properties,while any hydraulic process inﬂuences the deformation behaviour of soil.The emerging need to solve environmental issues related to deformable soils(e.g.contaminant transport through engineered clay liners,geologic sequestration of greenhouse gases,impact of vehicle trafﬁc on soil hydraulic properties)ﬁnally brought soil physics and soil mechanics closer together(Vulliet et al.,2002).The protection of agricultural and forest soils from compaction is one of these needs.2.1.2.Mechanics of agricultural and forest soilsSince its beginning in the19th century,the mechanics of agricultural and forest soils was linked to problems related to trafﬁcability and soil–vehicle interactions.Soil compaction did not receive much attention before increased mechanization of post World War II agriculture sparked concerns about decreasing soil fertility due to vehicle-induced compaction.Although the focus of attention moved away from the problem of pure trafﬁcability towards preserving soil fertility,military needs remained the main driver to study soil–vehicle interaction until the late1960s (Bekker,1956,1969).To describe and predict soil compaction due to vehicle trafﬁc, the work by So¨hne(1951,1953,1958)was certainly pioneering and addressed the main issues of modern day compaction research such as stress distribution at the tyre–soil interface,stress transfer into the soil as well as impact of these stresses on the degree of soil compaction.Both Bekker(1956)and So¨hne(1951,1953)employed mechanical concepts developed by Rankine(1857),Boussinesq (1885),Terzaghi(1925,1943)and Fro¨hlich(1934)to derive their stress–strain models for soils under vehicular trafﬁc.The strength of these models lay in their simplicity,which makes them hugely popular to this day for applications in agriculture and forestry.With the introduction of concepts like CSSM and unsaturated soil mechanics,more advanced frameworks became available to describe soil deformation.Although some of these models were adopted to describe the mechanics of agricultural soils as early as in the1960s(e.g.Bailey and VandenBerg,1968)and have been further developed ever since(e.g.Hettiaratchi,1987;Gra¨sle et al., 1995;Kirby,1994;O’Sullivan and Robertson,1996),no compre-hensive mechanical theory for agricultural and forest soils is currently available.One reason is that agricultural and forest soils feature various forms of secondary structure that affect the mechanical as well as hydraulic properties of the soil(e.g.Hartge and Sommer,1980; Horn,1993)but are hard to quantify.Only recently,advances in imaging using X-ray(micro-)tomography have allowed non-destructive visualization of the soil structure,a key step towards quantifying the impact of structure on soil hydraulic and mechanical properties.Driven by the increasing possibilities of soil structure visualization,structure-based micro-scale soil mechanics models were developed,which allow the consideration of the interaction of mechanics and hydraulics of the soil at the pore scale(e.g.Or and Ghezzehei,2002;Eggers et al.,2006).2.1.3.Soil rheologyThe extent of soil compaction and subsequent structural recovery are greatly inﬂuenced by loading and deformation rates that in turn are determined by soil rheological properties.In contrast with motion and mechanics of rigid bodies,rheology dealsT.Keller et al./Soil&Tillage Research128(2013)61–8063with relative motions of the parts of a body relatively to each another(Reiner,1960).Under the action of a force,a body may deform elastically(deformation is fully recoverable when the force is removed),plastically(permanent deformation even when the force is removed),or the material mayﬂow at a certain rate (continuous deformation without limit under the action of a force). Reiner(1960)deﬁnes‘‘rheology in the narrower sense’’as the science that deals with the deformation andﬂow of materials that are classiﬁed between the extremes of solid(Euclid-solid:rigid body;and Hooke-solid:elastic body)and liquid(Newtonian liquid: ideal viscous liquid;and Pascalian liquid:inviscid liquid).We may distinguish macro-rheology from micro-rheology (Reiner,1960):macro-rheology considers materials as homoge-neous,whereas micro-rheology considers material structure. Macro-rheology is partly treated within‘‘classical’’(continuum) soil mechanics,dealing primarily with strain and stress(e.g. stress–strain curves obtained from uniaxial compression tests, triaxial tests or shear tests).Nevertheless,mechanical behaviour of soil is also dependent upon strain and stress rates,which have received little attention in classical soil mechanics(perhaps due to preoccupation with foundations and static structures).Micro-rheology has two main tasks(Reiner,1960):it aims at either(i) obtaining a picture of the structure of the material giving rise to measuredﬂow curves,or(ii)at explaining the rheological behaviour of a complex material from the known rheological behaviour of its constituents.In practice,soil rheology deals with dynamic soil deformation processes that consider soil structure,rates of load application and resulting deformation.According to Or(1996),traditional stress–strain approaches fail to capture salient features of soil structure dynamics because these approaches(i)are based on equilibrium state while deformation in agricultural soils are dynamic processes that rarely reach equilibrium,and(ii)often describe bulk volume changes only but cannot describe the evolution of the soil structure at the pore scale that is crucial forﬂow and transport processes central to hydrological and agronomic applications.Recent developments in soil rheology address the two main tasks of micro-rheology as described by Reiner(1960).The work of Markgraf,Horn and co-workers(see Markgraf et al.,2006)aims at characterizing the structure of soil from observed rheological test curves.Or and co-workers(see Or and Ghezzehei,2002)developed models for soil structure dynamics at the pore scale based on rheological properties.Ghezzehei and Or(2000,2001)developed models for soil aggregate deformation and coalescence due to wetting–drying cycles as well as due to external static and cyclic stresses.These models were further extended to include closure of isolated pores (Ghezzehei and Or,2003;Berli and Or,2006;Berli et al.,2006), which can then be used,for example,to investigate the evolution of hydraulic conductivity upon compression(Eggers et al.,2006;Berli et al.,2008).The approaches are based on(i)geometrical representations of aggregates,pore space and liquid menisci,(ii) energy considerations,and(iii)rheological properties of unsatu-rated soil.It was shown that soil under steady state stress behaves as a viscoplastic material that can be described as a Bingham body (Vyalov,1986;Ghezzehei and Or,2000,2001)as illustrated in Fig.1a,which in general terms can be given as(Reiner,1960;Or and Ghezzehei,2002):˙g¼0t<t yðtÀt yÞ/h pl t!t y&(1)where t is the shear stress,t y is the yield stress,˙g is the strain rate, and h pl is the plastic viscosity.It is seen from Fig.1that the rheological properties t y and h pl(the inverse slope of the straight line of the Bingham model)are functions of soil water content: both t y and h pl decrease with increasing water content.Energy generally appears under three forms in rheological phenomena(Reiner,1960):kinematic energy,elastic(potential) energy,and dissipated(thermal)energy.Elastic energy is stored (conserved)during deformation and fully recovered upon stress release,while energy is dissipated during viscousﬂow is permanent(if elastic energy is not permanently conserved but dissipates with time,this is referred to as dissipation by relaxation.)Hence,total strain can be divided into an elastic and a viscous component.The ratio of elastic to viscous strain is dependent on the stress rate and the loading time,as shown by Ghezzehei and Or(2001):the shorter the loading rate,the larger the elastic strain and the smaller the viscous(permanent) component of the total strain.Consequently,storage of elastic energy(and therefore elastic strain)is of importance especially during deformation under cyclic or transient stresses as occur e.g. during the passage of agricultural machinery.The behaviour of the soil can then be described as‘‘visco-elastic’’(Ghezzehei and Or, 2001).It is convenient to express the visco-elastic properties in a complex plane system.The shear stress,t,and strain,g,can be related by(Ghezzehei and Or,2001):t¼GÃg(2) where G*is the complex shear modulus;similarly,t and strain rate,˙g,can be related byt¼hÃ˙g(3) where h*is the complex viscosity.The real components of G*and h* indicate the storage(elastic)shear modulus,G0,and elastic viscosity,h0,respectively,while the imaginary components of G*and h*indicate the loss(viscous)shear modulus,G00,and loss viscosity,h00,respectively(Ghezzehei and Or,2001).The relative proportion of the elastic and viscous component can be obtained from the phase shift angle(also termed the mechanical loss angle), d,that describes the delay in strain(peak)due to an applied stress (peak)as a result of the time dependence of viscous strain(d=0for ideal elastic material;d=p/2for ideal viscous material)(Ghezze-hei and Or,2001).Markgraf et al.(2006)measured the stress–strain rate relation-ship in a rotational rheometer in order to derive G0,G00and the‘‘loss factor’’tan d(=G00/G0),as well as the linear visco-elastic(LVE) deformation range and the deformation limiting value,g L.Three phases of material behaviour can be identiﬁed(Markgraf et al.,2006; Markgraf,2011),as illustrated in Fig.2.In phase I(initial or plateau phase)G0>G00,a quasi-elastic behaviour can be observed.The quasi-elastic stage is deﬁned by the LVE range and the deriving deformation limit g L.At this stage of low strain,a full recovery of the microstructure can be assumed.In phase II.1,a stage of pre-yielding occurs due to higher strain;soil particles are re-oriented, microstructural stability is given,but decreasing.At the end of phase II.2,an intersection of G0and G00indicates the yield-point.In comparison,the intersection of tan d with the tan d=1-line also indicates a complete loss of stability of the soil microstructure (Markgraf and Horn,2009).By calculating the integral z(Fig.2b),the structural strength,which includes quasi-elasticity and pre-yielding behaviour,can be quantiﬁed.Hence,phase III deﬁnes theﬁnal stage of structural collapse,G0<G00;a viscous character predominates,and substances are creeping orﬂowing.2.2.GeomechanicsApplications of classical continuum mechanics range from the use of elasticity theory(e.g.Davis and Selvadurai,1996)to micromechanical approaches based on particulate mechanicsT.Keller et al./Soil&Tillage Research128(2013)61–80 64(e.g.Misra and Huang,2009;see also Section 2.4).The subject ofgeomechanics has made important advances in terms of describ-ing the mechanics of porous geomaterials by taking into consideration their multi-phase nature,especially pertaining to the coupled behaviour involving ﬂuid ﬂow through the pore space,mechanical deformations (both reversible and irreversible)and thermal deformations of the separate phases (e.g.Desai and Siriwardane,1984;Selvadurai and Nguyen,1995;Pietruszczak,2010).The objective of Section 2.2is to examine two problems,which demonstrate the ability of advanced theories of continuum poromechanics (continuum mechanics that studies the behaviour of ﬂuid-saturated porous media)to provide explanations of soil compaction phenomena.The compaction of an array of soil aggregates (cf .Fig.3)is a more complex problem that cannot be obtained conveniently with continuum mechanics as discussed in Sections 2.1.3and 5.1.2.2.1.Mechanical behaviour of an isotropic elasto-plastic saturated materialThe mechanical behaviour of a ﬂuid-saturated porous medium undergoing inﬁnitesimal elastic strains was ﬁrst developed by Biot (1941),taking into consideration Darcy’s law to describe the ﬂow of the ﬂuid through the pore space and Hookean elastic behaviour of the porous skeleton to describe deformations.The basic constitutive equations governing the mechanical and ﬂuid transport behaviour of an isotropic poroelastic medium consisting of non-deformable solid matter,which is saturated with anincompressible ﬂuid,can be written in the formsv f ¼ÀK smr p(4)s ¼G ðr u þu rÞþðl r u ÞI þp I(5)where s is the total stress tensor,u is the displacement vector of the skeletal phase,v f refers to the velocities of the ﬂuid,p is the ﬂuid pressure in the pore space,K s is the saturated permeabilitymatrix,G and l are Lame´elastic constants of the porous skeleton,m is the dynamic viscosity of water,5is the gradient operator,and I is the unit matrix.In Eq.(4)the velocity of the porous matrix is neglected.In extending the studies to include poroelasto-plasticity effects,we need to select an appropriate constitutive response for saturated clay-type materials.A variety of constitutive relations have been proposed in the literature.For the purpose of illustration,an elasto-plastic skeletal response of the Modiﬁed Cam Clay type (e.g.Desai and Siriwardane,1984;Davis and Selvadurai,2002)is presented here.Attention is restricted to an isotropic elasto-plastic material deﬁned by the yield functionð˜sÀa Þ2þq /M ÀÁ2Àa 2¼0(6)where q is the von Mises stress,a is the radius of the yield surface,˜s is the mean effective stress,M is the slope of the critical state line and these are deﬁned byq ¼ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ3˜s i j ˜s i j /2;p ˜s ¼À˜s kk /3ÀÁ;˜s i j ¼˜s i j þ˜sd i j (7)The hardening rule is deﬁned by˜s ¼˜s ðe pl kk Þ¼˜s 0c þ˜sc ðe plkk Þ(8)and the incremental plastic strains are deﬁned by an associated ﬂow rule of the typed e pl i j ¼d l @G @si j ;G ¼ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ˜s À˜s o cÀ˜s c ðe pl kk Þ !2þq 2v u u t (9)where the hardening rule takes the form˜s c ¼˜s c ðe pl kk Þ¼H ðÀe plkk Þ(10)and H is a positive constant.In addition to the elasto-plastic constitutive response for the porous skeleton,it is assumed that the ﬂuid ﬂow through the porous skeleton remains unchanged during yield and subsequent hardening of the porous skeleton.However,it is recognized that deformation changes the perme-ability of geomaterials (e.g.Berli et al.,2008).Furthermore,the compaction process can induce stratiﬁcations at the macro-level of the fabric that can give rise to transversely isotropic properties in all mechanical and transport phenomena.2.2.2.Poroelasto-plastic behaviour of a one-dimensional columnFor the purpose of illustration,we consider the problem of a one-dimensional column,which is saturated with an incompress-ible ﬂuid and the porous skeleton can possess an elasto-plastic constitutive response described by Eqs.(6–10).The surface of the one-dimensional column is subjected to a quasi-static normal traction or in a cyclic loading–unloading mode in a time-dependent fashion as shown in Fig.4a.The upper surface of the column is maintained at zero pore ﬂuid pressure.The constitutive models are implemented in a general purpose computational multiphysics code and the initial boundary value problems are analysed via a ﬁnite element technique.Fig.4b shows the response of the poroelasto-plastic soil skeleton model.Unlike in poroelasticity (not shown),thecyclicFig.1.(a)Example of a strain rate versus stress curve (silt loam at two differentwater contents)(from Or and Ghezzehei,2002).(b)Coefﬁcient of plastic viscosity and yield stress as functions of soil matric potential for the same soil.From Ghezzehei and Or (2000).T.Keller et al./Soil &Tillage Research 128(2013)61–8065。

Lecture 11 Shear Strength of Soils

In general, soil shear strength has two components:

A simple model:

the stress-independent component: due to intrinsic cohesion of soil particles called cohesion (c) The stress-dependant component: due to friction between soil grains (similar to the sliding friction in solids) angle of internal friction or friction angle (f) depends on the effective stress on the plane
2012
Shear Strength of Soils
Shear Strength of Soils
3
The University of New South Wales School of Civil & Environmental Engineering
Soil Mechanics - CVEN2201 By Arman Khoshghalb
The University of New South Wales School of Civil & Environmental Engineering
Soil Mechanics - CVEN2201 By Arman Khoshghalb
Introduction

Soil strength needs to be evaluated in many problems.

“工程地质”主要术语(词汇)及用法slope failure,rockfall, ,landslide等

“工程地质”主要术语（词汇）及用法slope failure,rockfall, ,landslide等★landslide area e.g. We cannot be certain whether landslides did or did not occur in the regions outside of the mapped landslide area.★landslide dam★landslide distribution e.g. Empirical studies suggest that the bedrock lithology, slope, seismic intensity, topographical amplification of ground motion, fracture systems in the underlying bedrock, groundwater conditions, and also the distribution of pre existing landslides all have some impact on the landslide distribution, among factors.★landslide hazard modeling e.g. The main objective of landslide hazard modeling is to predict areas prone to landslides either spatially or temporally.★landslide inventories e.g. In order to apply this approach to a global data set, we use multiple landslide inventories to calibrate the model. Using the model formula previously determined (using the Wenchuan earthquake data), we use the four datasets discussed in Section 1.3.1 in our global database to determine the coefficients for the global model.★landslide probability model e.g. The resulting database is used to build a predicative model of the probability of landslide occurrence.★landslide susceptibility★landslide observation e.g. Cells are classified as landslides if any portion of that grid cell contains a landslide observation, in order to easily incorporate binary observations into the logistic regression.★landslides e.g. Substantial effort has been invested to understand where seismically inducedlandslides may occur in the future, as they are a costly and frequently fatal threat in mountainous regions; Performance of the regression model is assessed using statistical goodness-of-fit metrics and a qualitative review to determine which combination of the proxies provides both the optimum predication of landslide-affected areas and minimizes the false alarms in non-landslide zones; Approximately 5% of all earthquake-related fatalities are caused by seismically induced landslides, in some cases causing a majority of non-shaking deaths; Possible case histories of earthquake-triggered landslides to add to the global dataset include….★landslip★limit equilibrium methods★line slope profile★linearly e.g. In order to determine if such an increase in water levels could be the cause of increased down slope movement the bottom head boundary condition of both the Shetran and Flac-tp model was increased linearly by 0 to 4 m over the length of the lower slope and linearly by 4 to 5 m over the length of the upper slope.★low angle failure★lower slope★macroscopic indicators e.g. Unsaturated residual shear strength can also be used as a macroscopic indicator of the nature of micro-structural changes experienced by the soils when subjected to drying.★material parameters★mechanical analysis★mechanical landslide modeling e.g. These data were originally calculated for the purpose of mechanical landslide modeling, and are used here as a statistical constraint on landslide susceptibility.★mechanical parameters★mechanical propertied★mechanical response★mechanical strains★mechanism e.g. The output pore water pressure were coupled to a mechanical analysis using the Flac-tp flow program in an attempt to distinguish the mechanisms active within the slope which were likely to produce the recorded pore water pressure.★medium to low compressibility★mid height★mine tailings dams e.g. This paper reviews these factors, covering the characteristics, types and magnitudes, environmental impacts, and remediation of mine tailings dam failures.★minimal e.g. The brown sand and gravel at depth were also omitted from the model as their effects on the surface failure were assumed to be minimal.★minimum e.g. This conceptual model allowed the deformation of elements within the slope to be kept to a minimum.★moisture content e.g. We use the Compound Topographic Index (CTI) to represent moisture content of the area.★model output★moment inertia★monitoring campaign★movement e.g. At this time the measured displacement showed a sharp up slope movement followed by a steady but increasing down slope movement; …when a sudden down slope movement was measured; the nature of the event was uncertain yet it could be seen that the increase in down slope movement occurred after the water level increase.★movement rates★null hypothesis e.g. We also use the p-values (defined as the probability of finding a test statistic value as great as the observed test statistic value, assuming that the null hypothesis is true) in order to assess the significance of each regression coefficient. In this case, the null hypothesis is that the regression coefficient is equal to zero. We reject the null hypothesis if the p-value is less than the significance value (α) we choose; here, we useα=0.001, corresponding to a 99% confidence level. Therefore if p<α, we reject the null hypothesis, and thereby assume that the regression coefficient is not equal to zero, and equals the computed value (Peng et al., 2002).★numerical studies e.g. Those numerical studies mentioned above successfully validated the usage of supplemental means for the full scale tests and also contributed to develop and optimize new type of rockfall barrier system effectively. However, very little research has been devoted to the more practical analysis of the optimal rockfall barrier system over the various unfavorable impact conditions which can usually happen in actual field conditions.★overlying★parametric study★peak ground acceleration e.g. Estimates of the peak ground acceleration (PGA) and peak ground velocity (PGV) for each event are adapted from the USGS Shakemap Atlas 2.0 (Garcia et al., 2012)★peak ground velocity★peak strengths★peak values of movement★periodic surface erosion★periodic walkover surveys★permeability★perspective e.g. Despite the shortcomings in site data from a modelers' perspective, the situation was typical of current instrumentation practice for a problem slope.★phreatic surface e.g. The slope, however, was observed to remain largely saturated for most of the year with a phreatic surface near or at the surface.★plasticity★plasticity index★pore pressure★pore pressure fluctuations★pore pressure transfer★pore pressure variations★pore water pressure★predictor variables e.g. We begin modeling by assessing qualitative relationships within the data, moving forward by using logistic regression as a statistical method for establishing a functional form between the predictor variables and the outcomes (Figure 3). We iterate over combinations of predictor variables and outcomes within the model, focusing first on one training event (Wenchuan, China), with the ultimate goal of expanding the analysis to global landslide datasets.★preferential drainage paths★previously e.g. As discussed previously,…★probability of landslide occurrence★profile★progressive failure 渐进破坏 e.g. (Abstract of a paper entitled “Progressive Failure of Lined Waste Impoundments”) “Progressive failure can occur along geosynthetic interfaces (土工合成材料界面) in lined waste landfills when peak strengths are greater than residual strengths. A displacement-softening formulation for geosynthetic interfaces was used in finite-elementanalyses of lined waste impoundments to evaluate the significance of progressive failure effects. First, the Kettleman Hills landfill was analyzed, and good agreement was found between the calculated and observed failure heights. Next, parametric analyses of municipal solid waste landfills were performed. Progressive failure was significant in all cases. Limit equilibrium analyses were also performed, and recommendations are provided for incorporating progressive failure effects in limit equilibrium analyses of municipal solid waste landfills”.★range★reference★reference grid point e.g. Due to the different grids of the Flac-tv flow model and the Shetran model there was no reference grid point, for which readings could be taken, at the exact same depth for both models. The closest similar reference points were at 1.91m depth for the Flac-tv flow model and 1.5 m depth for the Shetran model.★reliability e.g. Full scale rockfall tests to assess the reliability of the structure and also to investigate the interactions of the rockfall catchfence subjected to the impacts were carried out by Peila et al.★residual failure surface★residual friction angles★residual shear strength parameters★residual slope failure★residual strengths★restitution coefficient★rigid body mechaics★rock mass★rockfall barrier system e.g. Since the impact response of the rockfall catchfence has complicated phenomena caused by materials elastic and plastic behaviors of each member (i.e. steel post, nets and cables, etc.) and also influenced by various factors; such as impact angle, impact energy, dimension of block, strength of each member, mechanical stiffness of rockfall catchfence, etc., many researchers have devoted efforts to make a more comprehensive understanding of various facets of rockfall barrier system.★rockfall catchfences e.g. For the mitigation measure of rockfall hazards, rockfall catchfences are widely adapted in the potential hazard area to intercept and hold the falling materials.★rockfall hazards e.g. The road has been exposed to high potential rockfall hazards as a result of the fractured columnar natural slope condition with post tectonic joints.★rockfall protection kits★rockfall protection mesh★root cause★root cause of elevated pore pressure★rooting depth★rotational slope failure★saturated soils★seasonal pore pressure conditions★seasonal affects★seasonal fluctuations in embankment pore water pressures★section★shallow angle★shallow slips★shear strength★shear strength parameters e.g. In the second phase of the simulation, the shear strength parameters (c,f) were input into the model.★shortcomings e.g. Despite the shortcomings in site data from a modelers' perspective, the situation was typical of current instrumentation practice for a problem slope.★significantly e.g. These failures were sufficiently shallow that they did not significantly affect the overall stability of the slope.★simulate e.g. Furthermore, a parametric study was conducted on the permeability to get the best fit between recorded and simulated data.★site walkover survey★slightly e.g. There was a drop in water levels with the simulation but this occurred slightly before the recorded drop and the magnitude was approximately half of that recorded. The water levels within the simulation recovered at approximately the same time as the recorded water levels but the water levels peaked at just below the previous high at slightly under 6 m AOD; "This showed that for the latter half of the simulation there was no significant increase in rainfall; there was actually a slight decrease."★slip indicator readings★slip mass e.g. Assuming an average failure depth of 6 m, the total estimated volume of the slipped mass was in excess 18,900m3.★slip movement★slope★slope angle★slope crest★slope failure 边坡破坏★slope geometry★slope material properties e.g. The dynamic interaction between falling blocks and slope of the CRSP is calculated by empirically driven functions incorporating velocity, friction and slope material properties.★slope stability analysis★slope stability assessments e.g. The RS unit is suitable for testing both fully-softened shear strength and residual shear strength parameters that can be used for slope stability assessments of various scenarios.★slope-stability methods★slope toe★slope value e.g. Median, minimum, and maximum slope values calculated from Shuttle Radar Topography Mission (SRTM) elevation data by Verdian et al. (2007) are used in tests of the model.★soft clay★soil bearing capacity e.g. Analysis of slopes, embankments, and soil bearing capacity, on the other hand, requires good estimations of shear strength from peak to residual.★soil slope★soil stiffness e.g. Calculation of foundation settlement, for instance, requires a good estimation of soil stiffness at relatively small strains.★soil water characteristic curve e.g. There was limited information regarding the soil water characteristic curve of the materials.★soil wetness★stability★steep slope e.g. The same was true for the steep slope entering the river.★steep topographic slope e.g. Areas of steep topographic slope are often associated with active faulting and hence, likely areas of strong ground shaking.★stiffness★spatial distribution e.g. The spatial distribution of seismically induced landslides is dependent on certain physical characteristics of the area in which they occur.★study area e.g. The study area is located along route 5 in the boroughs of Fort Lee and Edgewater, Bergen County, NJ where high level of cliff, 10 to 27 m high, exists along the road as shown in Fig.1; The paper discusses a fundamental geology and geomechanics of the study area first and then statistical rockfall analysis using Colorado Rock Fall Simulation Program has been performed to estimate the critical impact condition and the capacity of rockfall barrier system required. Finally, a series of three dimensional dynamic finite element analyes is performed to provide additional verification of the design criterion made by CRSP analysis and to suggest the detailed design parameters to accommodate specific field conditions.★summarize e.g. The realistic and modeled root depth distributions are summarized in Fig.11 and vegetation properties are summarized in Table 3.★surface boundary condition★surface geology★surface irregularity★surface pore water pressure★surface roughness★swell-induced soil movements e.g. The developed correlations, along with the existing models, were then used to predict vertical soil swell movements of four case studies where swell-induced soil movements were monitored.★swell-induced volume changes★tailings e.g. Extraction of the targeted resource results in the concurrent production of a significant volume of waste material, including tailings, which are mixtures of crushed rock and processing fluids from mills, washeries or concentrators that remain after the extraction of economic metals, minerals, mineral fuels or coal; The volume of tailings is normally far in excess of the liberated resource, and the tailings often contain potentially hazardous contaminants; A priority for a reasonable and responsible mining organization must be to proactively isolate the tailings so as to forestall them from entering groundwaters, rivers, lakes and the wind.★tailings dams e.g. It is therefore accepted practice for tailings to be stored in isolated impoundments under water and behind dams.★tension crack of the slip★tension cracks★threshold e.g. For example, if we define 20% probability of a landslide to be the threshold, any probability equal to or greater than 20% will then be defined as a landslide prediction; By evaluating the percentage of true positives and true negatives from a model, we can decide upon the optimum-probability threshold for classification as a landslide prediction; this optimum value is in turn dependent upon the balance between high values of true positives and true negatives with low values of false positives and false negatives.★time interval★top boundary★top of the slope★topographic slope★topographical survey★unit weight data★unsaturated hydrological properties★unsaturated soils e.g. To date, however, there is very limited experimental evidence of unsaturated soil behavior under large deformations, and the corresponding residual shear strength properties, while the soil is being subjected to controlled-suction states.★upper slope★value e.g. Therefore low CTI values result from higher slope values and small drainage areas, whereas high CTI values result from lower slope values and larger drainage areas. Note that this value does not consider wetness contributed from the climate of an area, but is purely dependent on the topographic influence on wetness.★variation★volume change properties★water level e.g. The Shetran simulation showed that there was no reason for such a largewater level drop mid simulation and again no reason for a new higher water table during the latter half of the simulation; the low water levels occurred during the summer months, when evapotranspiration is highest; from the measured results it could be seen that an event took place which resulted in elevated water levels in the upper part of the lower slope; from Fig.16 it can be seen that the water levels below the upper slope increase by almost 4 m and the water levels at the BH105 location increase by just less than 1 m; such an increase corresponds to water levels in the latter period of monitoring.★water level variance★water regime e.g. From these preliminary analyses it could be seen that the water regime within the slope was governed by more than the surface processes investigated; therefore, a fully coupled hydromechanical model of the slope was run to see if any light could be shed on the pore water pressure regime.★water table e.g. The report stated that this water table rise occurred as a result of heavy rainfall.。

土壤含水量、容重、光谱反射率之间相伴变化关系研究

土壤含水量、容重、光谱反射率之间相伴变化关系研究陈祯【摘要】[目的]研究土壤含水量变化对容重变化的定量化影响及其与先谱反射率的关系,构建相关模型,为近红外线光谱连续准确监测土壤含水量提供科学依据.[方法]以湖北省黄棕壤、水稻土、潮土为供试土壤,用环刀采样,在土壤脱水干燥过程中,用微型光纤光谱反射仪等连续同步测定土壤质量含水量、容重、土壤体积含水量和光谱反射率的变化.[结果]探讨了土壤含水量-容重变化的关系及相关模型,构建了反映土壤容重变化的土壤体积含水量与土壤光谱反射率的指数关系模型.[结论]所建模型拟合效果好,物理意义明确,形式简单,变量关系清晰.%[ Objective ] The quantification influence of soil moisture change on bulk density change and its relationship with spectrum reflectivity were studied. And the relevant model was constructed to provide scientific basis of near infrared spectrum continuous accurate monitoring soil water content. [Method] Yellow brown soil, rice soil, Chao soil in Hubei were taken as testing soils. Annulus knife was used to sample.During the process of soil dehydration, soil weight water content, soil bulk density, soil volume water content and spectralreflectivity changes were tested continuously using miniature fiber spectral reflection meter etc. [ Result] The soil water content-density change relation and related model were discussed. Exponent relation model reflecting soil volume water content and soil spectral reflectivity was constructed. [ Conclusion ] The models had good fitting effect, clear physical meaning, simple form, and clear variable relationship.【期刊名称】《安徽农业科学》【年(卷),期】2011(039)017【总页数】4页(P10313-10316)【关键词】土壤含水量;土壤容重;近红外光谱;土壤光谱反射率【作者】陈祯【作者单位】武汉理工大学机电工程学院,湖北,武汉,430070【正文语种】中文【中图分类】S123含有一定量黏粒的土壤都会明显地表现出土体湿胀干缩、容重变化的特点。

a-Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopy

Pedosphere22(5):640–649,2012 ISSN1002-0160/CN32-1315/Pc 2012Soil Science Society of China Published by Elsevier B.V.and SciencePressModel-Based Integrated Methods for Quantitative Estimationof Soil Salinity from Hyperspectral Remote Sensing Data:A Case Study of Selected South African Soils∗1Z.E.MASHIMBYE1,3,4,∗2,M.A.CHO2,5,J.P.NELL3,W.P.DE CLERCQ1,A.VAN NIEKERK4and D.P.TURNER31Department of Soil Science,Stellenbosch University,Private Bag X1,Matieland7602(South Africa)2Council for Scientiﬁc and Industrial Research,Natural Resources and the Environment,P.O.Box395,Pretoria0001(South Africa) 3Agricultural Research Council-Institute for Soil,Climate and Water,Private Bag X79,Pretoria0001(South Africa)4Department of Geography and Environmental Studies,Stellenbosch University,Private Bag X1,Matieland7602(South Africa)5School of Environmental Science,University of Kwazulu-Natal,Westville Campus,Westville3630(South Africa)(Received January19,2012;revised July24,2012)ABSTRACTSoil salinization is a land degradation process that leads to reduced agricultural yields.This study investigated the method that can best predict electrical conductivity(EC)in dry soils using individual bands,a normalized diﬀerence salinity index(NDSI),partial least squares regression(PLSR),and bagging PLSR.Soil spectral reﬂectance of dried,ground,and sieved soil samples containing varying amounts of EC was measured using an ASD FieldSpec spectrometer in a darkroom.Predictive models were computed using a training dataset.An independent validation dataset was used to validate the models.The results showed that good predictions could be made based on bagging PLSR usingﬁrst derivative reﬂectance(validation R2=0.85),PLSR using untransformed reﬂectance (validation R2=0.70),NDSI(validation R2=0.65),and the untransformed individual band at2257nm(validation R2=0.60) predictive models.These suggested the potential of mapping soil salinity using airborne and/or satellite hyperspectral data during dry seasons.Key Words:electrical conductivity,land degradation,partial least squares regression,salinity index,spectral reﬂectance Citation:Mashimbye,Z.E.,Cho,M.A.,Nell,J.P.,De Clercq,W.P.,Van Niekerk,A.and Turner,D.P.2012.Model-basedintegrated methods for quantitative estimation of soil salinity from hyperspectral remote sensing data:A case study of selected South African soils.Pedosphere.22(5):640–649.INTRODUCTIONSouth Africa is a vast lions of South African rands have been invested in building large ir-rigation infrastructure.Soil salinity often builds up in these schemes due to incorrect management prac-tices.It is very diﬃcult to monitor salinization in these schemes because current monitoring methods are ground based and the costs of laboratory analysis are high.Remote sensing is an attractive alternative to ground-based methods due to its relatively low costs and the ability to rapidly provide spatial information covering large areas.The use of remote sensing for soil salinity monitoring in South Africa is,however, not well established.Little is known about how South African conditions inﬂuence the spectra of salt-aﬀected soils.Soil salinization is a world-wide land degradation process that occurs in arid and semi-arid regions.Salts accumulate in the soil due to natural or man-made processes, e.g.,irrigation.Although statistics about the extent of salt-aﬀected soils diﬀer according to authors,Szabolcs(1994)and Metternicht and Zinck (2003)agree that about1billion hectares of land in the world are aﬀected by salts.According to Nell (2009),nearly60%of soils in South African are non-saline,23%slightly saline, 5.1%saline, 1.4%mode-rately saline,0.4%strongly saline,3.8%saline-sodic (non-alkaline),6.3%saline-sodic(alkaline),and0.4% can be considered as sodic.Nell(2009)used analyti-cal and morphological data derived from soil survey∗1Project supported by the Agricultural Research Council-Institute for Soil,Climate and Water(ARC-ISCW)of South Africa(No.GW 51/072),the National Research Foundation(NRF)of South Africa(No.GW51/083/01),and the Water Research Commission(WRC) of South Africa(No.K5/1849).∗2Corresponding author.E-mail:mashimbyee@arc.agric.za.ESTIMATION OF SOIL SALINITY FROM REMOTE SENSING DATA641reports,environmental planning and the land type database(LTD)survey undertaken by the Agricultural Research Council-Institute for Soil,Climate and Wa-ter(ARC-ISCW)to quantify primary salinity status of South Africa.He then used elementary statistical techniques to identify relationships between the soil, water,climate,topography,vegetation,and salt pa-rameters.Despite the awareness of the negative ef-fects that excess salts in the soil have on agricultural yields,it is reported that the problem is increasing rather than decreasing(Szabolcs,1994;Metternicht and Zinck,2003).According to Metternicht and Zinck(2003),a variety of remote sensing data, e.g.,aerial photo-graphs,video images,infrared thermography,visi-ble and infrared multispectral and microwave images, have been used for identifying and monitoring salt-aﬀected soils.Hitherto,broadband remote sensing data have been generally used for monitoring salt-aﬀected soils(Sharma and Bhargarva,1988;Rao et al., 1991;Dwivedi,1992;Verma et al.,1994;Mashimbye, 2005).However,because of their low spectral resolu-tion and the use of conventional classiﬁcation methods, these multispectral sensors(e.g.,Spot,Landsat MSS, and Landsat ETM+)are reported to have limited value for studying soil properties(Dehaan and Taylor,2003; Tamas and Lenart,2006).Notwithstanding,these sen-sors have been successful in distinguishing severely salt-aﬀected from non-aﬀected soils(Farifteh et al., 2006;Weng et al.,2010).Imaging spectroscopy(hyperspectral remote sen-sing)does provide near-laboratory quality reﬂectance spectra for each pixel.According to Bertel et al. (2006),each picture element contains a unique spec-trum which can be used for detecting earth’s surface materials.Hyperspectral remote sensing allows the dis-crimination of subtle diﬀerences between materials, permitting investigation of phenomena and concepts that greatly extend the scope of traditional remote sensing(Chang,2003;Lillesand et al.,2004;Campbell, 2007).This is achievable because of the contiguous na-ture of the spectral proﬁle of a hyperspectral signature.Hyperspectral remote sensing has been widely used to study salt-aﬀected soils(Ben-Dor and Banin,1994; Drake,1995;Ben-Dor et al.,2002;Dehaan and Tay-lor,2002,2003;Tamas and Lenart,2006;Farifteh, 2007).Al-Khaier(2003)achieved an accurate(R2= 0.86)detection of soil salinity by a normalized salinity index in bare agricultural soils using ASTER bands4 (near-infrared)and5(short-wave infrared).Additio-nally,Khan et al.(2005)successfully used a norma-lized diﬀerence salinity index(NDSI)(using the near-infrared and red bands of the Indian Remote Sensing LISS-II sensor)to map soil salinity.No studies that used a hyperspectral NDSI to map soil salinity could be found.Weng et al.(2010)developed a univariate regre-ssion model to estimate soil salt content using a soil salinity index.The index was constructed from continuum-removed reﬂectance at2052and2203nm. Their model was applied to a Hyperion reﬂectance image and was successfully validated(R2=0.627). Farifteh et al.(2007)used PLSR and obtained pre-diction R2values between0.78and0.98using ex-perimental soil sample data,which in each sample was treated with diﬀerent salts(namely,MgSO4,KCl, NaCl,and MgCl2).Viscarra Rossel(2007)showed that bagging partial least squares regression(PLSR)pre-dictive models provided more robust predictions of or-ganic carbon than PLSR predictive models alone.The aim of this study was to evaluate the utility of an NDSI,PLSR,and bagging PLSR,for predicting soil salinity.Predictive models were developed using a training dataset.An independent validation dataset which was not included in the training was used to validate the models.MATERIALS AND METHODSSoil samplesTwo South African soil databases,namely,the LTD and ad hoc data held by the Agricultural Research Council(ARC),were used as sources for establishing a suitable set of soil samples for this study.The LTD arose from the1:250000scale soil mapping program, carried out over a period of30years(1972–2002)by the ARC-ISCW.From the early1990s this information was systematically transferred to a geographical infor-mation system(GIS),along with the composition of each of the more than7000unique land type mapping units,as well as a supporting database containing the soil proﬁle information.The ARC soil samples selected were collected on a monthly or bi-monthly basis over a period of14years fromﬁxed sites southeast of Johan-nesburg in the Gauteng Province.More information about soils of South Africa can be found on the Agri-cultural Geo-Referenced Information Systems(AGIS) website at http://www.agis.agric.za.Most salts in South Africa are of sea origin imbed-ded in the geology.The LTD soil samples used in this study were from the following geological formations: Adelaide,Beaufort,Barbeton,Bokkeveld,Bushman-land,Drakensberg,Dwyka,Ecca,Kalahari,Meinhard-skraal,Nama,Soutpansberg,Table Mountain,Tarkas-642Z.E.MASHIMBYE et al .tad,and Zululand (Fig.1a).Natural organic carbon of the soil samples ranged from 0.01to 0.28g kg −1.The distribution of the samples with diﬀerent quantities of natural organic carbon is depicted in Fig.1b.The soils were found to be saline sodic,moderately saline,non-alkaline sodic,and slightly saline soils (Fig.1c).The soil and terrain digital database (SOTER)soil units covered by the samples are:A4,AR,C1,E1,G1,and H1(Fig.1d).In total,95soil samples were used for this study.The samples were selected from the two databases u-sing a stratiﬁed random sampling technique (Brus and Gruijter,1997;Christoﬁdes,2003;Kim et al .,2007)to ensure an even distribution within the ﬁve saline classes:non-saline (0–2dS m −1),slightly saline (2–4dS m −1),moderately saline (4–8dS m −1),strongly saline (8–16dS m −1),and extremely saline (>16dS m −1).All the samples were oven-dried,ground,andFig.1Simpliﬁed geology (a),natural organic carbon content of soils (b),saline and sodic soils (c),and soil and terrain digital database (SOTER)soil classiﬁcation (d)in South Africa.ESTIMATION OF SOIL SALINITY FROM REMOTE SENSING DATA643put through a2-mm sieve to remove large particles and plant remains.The samples were analyzed for electrical conductivity(EC),organic carbon,and tex-ture.EC was measured by a1:5saturated extract. The samples were spread throughout South Africa,and were of diverse soil colours.The samples were divided into two groups:training(n=63)and validation (n=32).Training samples were used for model de-velopment,while the remaining samples were used to independently validate the models.Spectral data collectionAn analytical spectral device(ASD)FieldSpec spe-ctrometer was used to acquire spectral signatures of the soil samples in a darkroom to ensure stable at-mospheric and uniform illumination conditions.The instrument covers the visible to short-wave infrared wavelength range(350to2500nm).The spectrome-ter has a sampling interval of1.4nm for the region 350to1000nm and2nm for the region1000to2500 nm with a spectral resolution of3and10nm,respec-tively.Darkroom conditions were used to eliminate dif-fuse light conditions and to ensure that light conditions are similar to allow comparison.Diﬀuse lighting condi-tions will be considered in a separate part of the study as the inﬂuence thereof is required for calibrating the remotely sensed information.A halogen lamp(Lowel Light Pro,JCV14.5V-50WC)was used as a source of light.The lamp wasﬁxed at a nadir position20cm above the tar-get.To prevent contamination of one sample by ano-ther,each sample was placed on a separate black plas-tic background before making spectral signature mea-surements.A suﬃcient amount of soil for each sam-ple was spread on the plate to completely cover the plate’s surface.The soil wasﬂattened on top to form an even surface.Reﬂectance calibration was done using a white reference.The white reference is a calibrated white Spectralon with a near100%diﬀuse(Lamber-tian)reference reﬂectance panel made from a sin-tered poly-tetra-ﬂourethylene based material.Calibra-tion was done before taking measurements of each of the samples.Spectral signatures were taken at a height of approximately15cm above the target at approxi-mately15◦oﬀnadir to minimize the eﬀect of bidirec-tional reﬂectance.Data analysisBecause EC is the major indicator of soil salini-ty(Farifteh,2007;Farifteh et al.,2007,2008;Yao and Yang,2010),analysis was conducted conside-ring EC only.Four techniques were evaluated in this study:individual bands analysis,NDSI,PLSR,and bagging PLSR.Salinity models were computed using untransformed individual reﬂectance,ﬁrst derivative individual reﬂectance(FDR),NDSI,PLSR,and bag-ging PLSR.Individual bands were selected based on the correlograms between EC and reﬂectance.Soil re-ﬂectance data in the wavelength range between400and 2500nm were used for the analysis.Calibration and validation R2were computed for each of the models.PLSR and bagging PLSR were computed using the ParLeS version3.1software(Viscarra Rossel,2007, 2008).PLSR is a method that speciﬁes a linear rela-tionship between a set of dependent variables,Y,and a set of predictor variables,X(Farifteh et al.,2007).The general idea of the PLSR is to extract the orthogonal or latent predictor variables,accounting for as much of the variation of the dependent variables as possi-ble.The bagging PLSR is a bootstrap technique that leaves out about37%of the data in the course of re-sampling(Viscarra Rossel,2007,2008).The bootstrap automatically calculates the R2,adjusted R2(R2adj), root mean square error(RMSE),mean error(ME), ratio of prediction to deviation(RPD),and standard deviation of the error distribution(SDE).The performance of each of the models was evalu-ated using the calibration R2and the validation R2.The R2values indicate the strength of statisti-cal correlation between measured and predicted values (Farifteh et al.,2007).Additionally,the PLSR models were evaluated using the RPD and R2adj.The R2adj mea-sures the proportion of the variation in the response that may be attributed to the model rather than to random error,which makes it more comparable across models with diﬀerent numbers of parameters(Viscarra Rossel,2007).The RPD measures the ratio of percen-tage deviation to the RMSE.RPD values of less than 1.5indicate very poor model predictions,between1.5 and2.0poor model predictions,between2.0and2.5 good model predictions,and greater than2.5very good model predictions(Viscarra Rossel,2007).Individual bands.A distributionﬁtting curve using untransformed EC values revealed that the trai-ning samples were not normally distributed(P<0.05, Shapiro-Wilk’s W test)(Fig.2a).A second distribution ﬁtting curve computed using the natural logarithmic values of EC resulted in a normal distributed(P< 0.05,Shapiro-Wilk’s W test)sample(Fig.2b).The analysis was consequently conducted using the natural logarithmic values of EC.Pearson’s correlation analy-ses of original soil spectra and FDR with EC were car-ried out and the bands that yielded the highest corre-lations with EC were identiﬁed.For individual band644Z.E.MASHIMBYE et al .analysis,only bands that occur outside the major wa-ter absorption bands (1340–1480and 1770–1970nm)(Herold et al .,2004)were considered for analysis.Con-sequently,regression models that explained the most degree of variation of EC using spectral reﬂectance were computed using these bands only.All 63training and 32validation samples wereused.Fig.2Training sample distribution ﬁtting curves of original electrical conductivity (EC)values (a)and logEC values (b).Normalized diﬀerence salinity index (NDSI).An analysis was carried out to develop an NDSI that pre-dicts EC in soils.Candidate NDSI for any two bands i and j for a sample n ,NDSI i,j,n ,was calculated ac-cording to the principle of the normalized diﬀerence vegetation index (NDVI)used in vegetation studies (Eq.1):NDSI i,j,n =(R i,n −R j,n )/(R i,n +R j,n )(1)where R i,n and R j,n are the reﬂectance of any two bands i and j for a sample n ,respectively.The candidate NDSI was derived from all possible two-band combinations involving the bands in the 400–2500nm range,sampled at 10nm resolution.Only the training sample set was used for this purpose.This re-sulted in 44100(i.e.,210×210)candidates.The NDSI was regressed with EC and the best bands were iden-tiﬁed.Partial least squares regression (PLSR).PLSR is a bilinear calibration method using data compression by reducing the large number of measured collinear spectral variables to a few non-correlated latent vari-ables or factors (Hansen and Schjoerring,2003;Cho et al .,2007).PLSR speciﬁes a linear relationship between a set of dependent variables (Y )and a set of predictor variables (X ),thereby extracting the orthogonal or la-tent predictor variables accounting for as much of the variation of the dependent variables as possible (Cho et al.,2007;Farifteh et al ,2007).The linear equation derived from the PLSR is:Y =Xb +E(2)where Y is the mean-centred matrix containing the response variables,X the mean-centred matrix con-taining the predictor variables (spectral bands in this study),b the matrix containing the regression coeﬃ-cients,and E the matrix of residuals.PLSR of un-transformed and ﬁrst derivative reﬂectance with EC was conducted using the ParLeS version 3.1software (Viscarra Rossel,2008).As with the other techniques evaluated,all 95samples were used for this analysis.Bagging PLSR.Bootstrapping performs sam-pling within a sample.It is a technique that may be used to estimate the cumulative distribution function (CDF)of a population,its moments and their un-certainty by re-sampling with replacement (Viscarra Rossel,2007).The bootstrap assumes that the CDF of the data is suﬃciently similar to that of the original population,and that multiple realizations of the popu-lation can be replicated from a single dataset.The bag-ging PLSR function of the ParLes version 3.1software was used to conduct automatic bootstrapping consis-ting of 50iterations for the bagging PLSR.Although a bootstrap may have duplicate data,it leaves out about 37%of the data in the course of re-sampling for valida-tion statistics (Viscarra Rossel,2007).These statistics were analyzed to assess the performance of the various models.RESULTSRegression between EC and individual bandsPearson correlation coeﬃcient values of EC with untransformed saline soil spectra increased from the visible through to the short-wave infrared region of the spectrum (Fig.3).The raw reﬂectance data at 2257nm and FDR at 991nm showed the highest Pearson correlation coeﬃcient (r =−0.59for 2257nm and r =ESTIMATION OF SOIL SALINITY FROM REMOTE SENSING DATA645Fig.3Relationships of electrical conductivity (EC)with un-transformed reﬂectance of dry saline soils.−0.73for FDR at 991nm)with EC among the spec-tral bands from 400to 2500nm.The above bands were subsequently used to derive predictive regression models for soil EC.Fig.4a indicated that for the un-transformed reﬂectance (at 2257nm),a quadratic re-gression model provided a better representation (R 2=0.31)of the EC of the training sample set than a linear model (R 2=0.25).Despite yielding a lower calibration R 2,the linear predictive model yielded aslightly higher prediction R 2than the quadratic pre-dictive model (Fig.4b,c)compared to the validation sample set.For the FDR (at 991nm),both the linear and quadratic models yielded similar calibration and prediction R 2values (Fig.4d,e,f).NDSI.Linear regression analyses were perfo-rmed comparing each candidate NDSI with EC.A con-tour plot of R 2of the results is shown in Fig.5.The 2040and 1410nm wavelengths were identiﬁed as the most promising for developing an NDSI.Consequently,an NDSI using the corresponding bands was created and subsequently assessed for its predictive capability using the independent validation dataset.Although the NDSI quadratic and linear regres-sion predictive models yielded similar calibration R 2(Fig.6a),the NDSI quadratic predictive model yielded a higher prediction R 2than the NDSI linear predic-tive model,with the prediction R 2being 0.65and 0.57for the NDSI quadratic predictive model and the NDSI linear predictive model,respectively (Fig.4b,c).Compared to the individual band predictive models (Fig.4b,c,e,f),the NDSI quadratic predictive model yielded higher calibration and prediction R 2values.PLSR.The results show that the R 2values for the untransformed and FDR PLSR predictivemodelsFig.4Untransformed individual band (at 2257nm)soil electrical conductivity (EC)predictive models (a),quadratic untransformed individual band soil EC predictive model validation (b),linear untransformed individual band soil EC predictive model validation (c),ﬁrst derivative reﬂectance (FDR)individual band (at 991nm)soil EC predictive models (d),quadratic FDR individual band soil EC predictive model validation (e),and linear FDR individual band soil EC predictive model validation (f).646Z.E.MASHIMBYE et al.Fig.5Contour plot of R 2with wavelength.were 0.68and 0.72,respectively (Table I),while the RPD values were less than 1.5in both cases.Accor-ding to Farifteh et al .(2007),predictive models with RPD values less than 1.5and calibration R 2values between 0.66and 0.81can be regarded as poor pre-dictive models.In addition,the high RMSE values (0.39and 0.41for untransformed spectra and FDR,respectively)were indicative of high prediction errors.Although the R 2value of the FDR PLSR predictive model was slightly higher than the untransformed re-ﬂectance value,the former yielded a signiﬁcantly lower prediction R 2(Fig.7a,b).The ﬁrst ﬁve factors of the untransformed reﬂectance PLSR predictive model con-tained about 68%of the information on soil EC,while the ﬁrst factor of the FDR PLSR predictive model con-tained almost 72%of the information on soil EC.Bagging PLSR .As with PLSR,the calibration R 2values were between 0.66and 0.81(R 2=0.69for the untransformed reﬂectance and R 2=0.67for the derivative reﬂectance).However,the RPD values were higher than 1.5(Table II).Additionally,the baggingTABLE ICalibration statistics for the partial least squares regression (PLSR)soil salinity models Statistics a)Untransformed First derivative reﬂectance reﬂectance R 0.680.72R 2adj0.470.41RMSE 0.390.41RPD1.35 1.27Number of factors51a)R 2adj=adjusted R 2;RMSE =root mean square error;RPD=ratio of prediction to deviation.TABLE IICalibration statistics for bagging partial least squares regression (PLSR)soil salinity models Statistics a)Untransformed First derivative reﬂectance reﬂectance R 0.690.67R 2adj0.690.66RMSE 0.290.29RPD1.81 1.73Number of factors 82Number of bootstraps5050a)R 2adj=adjusted R 2;RMSE =root mean square error;RPD=ratio of prediction to deviation.PLSR presented lower prediction errors when com-pared to PLSR (Tables I and II).Amongst all the predictive models evaluated in this study,the bagging PLSR model using FDR yielded the highest prediction R 2(Fig.8b).DISCUSSIONThis study found that there was potential for bag-ging PLSR predictive models to improve soil salinity prediction using remote sensing.Bagging PLSR predic-tive models produced higher prediction R 2thanPLSR,Fig.6Normalized diﬀerence salinity index (NDSI)soil salinity predictive models (a),quadratic NDSI soil salinity predictive model validation (b),and linear NDSI soil salinity predictive model validation (c).ESTIMATION OF SOIL SALINITY FROM REMOTE SENSING DATA647Fig.7Untransformed spectra partial least squares regression (PLSR)soil salinity predictive model validation (a)and the ﬁrst derivative reﬂectance (FDR)PLSR soil salinity predictive model validation(b).Fig.8Untransformed spectra bagging partial least squares regression (PLSR)soil salinity predictive model validation (a)and ﬁrst derivative reﬂectance (FDR)bagging PLSR soil sali-nity predictive model validation (b).NDSI,and individual band predictive models.Theseresults support the ﬁndings by Viscarra Rossel (2007)which showed that bagging PLSR predictive models provided more robust predictions of organic carbon than PLSR predictive models alone.This is because bagging PLSR incorporates a bootstrap sampling into the construction of the model,which stabilizes the modelling while still allowing for the identiﬁcation of important relationships in the data (Viscarra Rossel,2007).We found that the PLSR predictive models per-formed better than two-band NDSI or individual band models.This was likely due to higher information con-tent of the multiple bands used in PLSR.The predic-tion R 2obtained in this study is generally lower than that of Farifteh et al.(2007).Presumably,this is be-cause the EC of the samples used in this study is the combined eﬀect of a number of naturally occurring so-luble salts.The results of untransformed PLSR analy-sis suggested that PLSR could provide useful estimates of soil EC.The NSDI predictive models could explain up to about 50%of the variation in soil EC.Normalized in-dices have been found to be useful in many studies,including vegetation studies (Gao,1996;Al-Khaier,2003;Delbart et al .,2005;Jin and Sader,2005;Khan et al .,2005;George et al .,2006;Cho et al .,2007;Inoue et al .,2008).One of the bands (located at 1410nm)used to compute the NDSI is found in the water ab-sorption region.Consequently,the NDSI can only be applied to dry soils.No studies that linked the other band used in the NDSI (band at 2040nm)to soil EC were found.The relationship of EC with the saline soil spec-tra increased from the visible through to the short-wave infrared (SWIR)region of the spectrum.The highest correlations of untransformed saline soil spec-tra with EC occurred in the near-infrared (NIR)and SWIR regions.This is likely due to saline soils having distinct spectral features in the visible and NIR regions of the spectrum,which allows the recognition of mine-rals such as gypsum,bassanite,and polyhalite (Dehaan and Taylor,2002;Metternicht and Zinck,2003).Also,Farifteh et al .(2007)found that the best performing bands for ﬁeld-scale,experiment-scale,and image-scale datasets were in the NIR and SWIR regions of the spectrum.The untransformed individual band located at 2257nm presents possibilities for estimating soil EC for dry soils by a linear predictive model.No other studies were found linking the band at 2257nm to soil EC.Because this study was based on dry soils,the in-ﬂuence of water on the soil spectra would be minimal.648Z.E.MASHIMBYE et al.According to Weng et al.(2010),organic carbon con-tent hardly aﬀects the reﬂectance of soil when it is lower than20g kg−1.Hence,organic carbon could not have aﬀected the spectral reﬂectance of the soils in this study because the highest measured organic carbon for the samples was0.28g kg−1,while the average organic carbon content was0.03g kg−1.The techniques applied in this study have not been tested on digital hyperspectral airborne or satellite i-mages.They will be tested using digital airborne hy-perspectral data at a selected study site where fur-ther work is currently being conducted.Constraints such as atmospheric attenuation are envisaged when airborne or satellite images are used(Ben-Dor et al., 2009).Good atmospheric correction methods will have to be used.Other challenges include soil texture and bidirectional reﬂectance distribution function eﬀects. Additionally,the soil surface is not always fully ex-posed.Litter,vegetation cover and remains,rocky out-crops,and other surface features might contribute to creating spectral confusion with salt reﬂectance prope-rties(Metternicht and Zinck,2003;Ben-Dor et al., 2009).CONCLUSIONSResults of this study suggested that individual bands,an NDSI,PLSR,and bagging PLSR presented opportunities for mapping salinity during dry seasons. They also aﬃrmed that bagging PLSR produced more robust predictive models than PLSR alone.Of all the techniques evaluated in this study,bagging PLSR using FDR was the most eﬀective method of predicting soil EC.In addition,an NDSI and the untransformed band at2257nm can potentially predict soil EC under dry conditions.These techniques presented possible solu-tions for estimating soil EC using remotely sensed ima-gery during dry seasons.This study also revealed that soil EC can be explained by a linear predictive spectral model.Furthermore,this study showed that there was potential to estimate EC using laboratory spectrome-ters(where minimum soil preparation will be required) and in situ.More research is needed to evaluate these techniques underﬁeld conditions,diﬀerent soil types, and diﬀerent geological conditions.ACKNOWLEGEMENTSThe authors wish to express appreciation to Mr. Garry Patterson,Dr.Goodman Jezile and Dr.Tho-mas Fyﬁeld of the ARC-ISCW,South Africa for edit-ing the manuscript.Professor Robin Barnard of the ARC-ISCW is thanked for assistance during the prepa-ration of the proposal.We express our gratitude to Mr.Richard Tswai and Mr.Phila Sibandze of the ARC-ISCW for assistance with spectral data measure-ments.Thanks to Mr.Adam Loock of the ARC-ISCW for providing the laboratory chemical analysis of the soil samples.Ms.Marjan van der Walt and Mr.Ernst Jacobs of the ARC-ISCW are thanked for assisting withﬁnding information on the LTD.Our gratitude also goes to Mr.Lot Mokoena of the ARC-ISCW for assistance with locating soil samples in the soil sam-ple stores.Finally,the authors express gratitude to Dr.Jan van Aardt of the Council for Scientiﬁc and In-dustrial Research-Natural Resources and Environment in South Africa for making available the darkroom and for providing the spectrometer.REFERENCESAl-Khaier,F.2003.Soil salinity detection using satellite remote sensing.MS.Dissertation,International Institute for Geo-information Science and Earth Observation and Utrecht University,Enschede.Ben-Dor,E.and Banin,A.1994.Visible and near-infrared(0.4–1.1μm)analysis of arid and semiarid soils.Remote Sens.Environ.48:261–274.Ben-Dor,E.,Chabrillat,S.,Dematte,J.A.M.,Taylor,G.R., Hill,J.,Whiting,M.L.and Sommer,ing imaging spectroscopy to study soil properties.Remote Sens.Envi-ron.113:538–555.Ben-Dor,E.,Patkin,K.,Banin,A.and Karnieli,A.2002.Map-ping of several soil properties using DAIS-7915hyperspec-tral scanner data—a case study over clayey soils in Israel.Int.J.Remote Sens.23:1043–1062.Bertel,L.,Deronde,B.,Fernandez,M.,Kempeneers,P.,Knaeps,E.,Meuleman,K.,Reusen,I.,Ruddick,K.,Sterckx,S.,Tre-fois,P.and Mol,V.2006.Hyperteach:Training in Imaging Spectroscopy.AFRICAN SUN MeDia,Stellenbosch. Brus,D.J.and De Gruijter,J.J.1997.Random sampling or geo-statistical modelling?Choosing between design-based and model-based sampling strategies for soil(with Discussion).Geoderma.80:1–44.Campbell,J.B.2007.Introduction to Remote Sensing.4th ed.Guilford Press,New York.Chang,C.I.2003.Hyperspectral Imaging:Techniques for Spec-tral Detection and Classiﬁcation.Kluwer Academic/Plenum Publishers,New York.Cho,M.A.,Skidmore,A.,Corsi,F.,Van Wieren,S.E.and Sobhan,I.2007.Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression.Int.J.Appl.Earth Observ.Geoinform.9:414–424.Christoﬁdes,T.C.2005.Randomized response in stratiﬁed sam-pling.J.Stat.Plan.Infer.128:303–310.Dehaan,R.T.and Taylor,G.R.2002.Field-derived spectra of salinized soils and vegetation as indicators of irrigation-induced salinization.Remote Sens.Environ.80:406–417. Dehaan,R.T.and Taylor,G.R.2003.Image-derived spectral endmembers as indicators of salinisation.Int.J.Remote Sens.24:775–794.Delbart,N.,Kergoat,L.,Toan,T.L.,Lhernitte J.and Picard,G.2005.Determination of phenological dates in boreal re-。

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文献汇总【1】 E. Bourgeois, P. de Buhan , G .Hassen , Settlement analysis of piled-raft foundations by means of a multiphase model accounting for soil-pile interactions. Computers and Geotechnics 46 (2012) 26–38桩土之间的相互作用的多样模型对桩筏基础的沉降研究【2】 Dang Dinh Chung Nguyen, Seong-Bae Jo, Dong-Soo Kim. Design method of piled-raft foundations under vertical load considering interaction effects. Computers and Geotechnics 47 (2013) 16–27在竖向荷载作用下桩筏基础相互作用影响的设计研究【3】 Jennifer J.M.Haskell , MiskoCubrinovski , Brendon A. Bradley. Sensitivity analysis and its role in pseudo-static design of pile foundations. Soil Dynamics and Earthquake Engineering 42 (2012) 80–94敏感性分析以及其在桩基非静载设计中的作用研究【4】 Cristina Medina, JuanJ.Aznárez, LuisA.Padrón n, OrlandoMaeso. Effects of soil–structure interaction on the dynamic properties and seismic response of piled structures. Soil DynamicsandEarthquakeEngineering53(2013)160–175土体结构对桩结构动态特性及地震作用的的影响研究【5】 L.B. Jayasinghe, D.P. Thambiratnam, N. Perera, J.H.A.R. Jayasooriya. Blast response and failure analysis of pile foundations subjected To surface explosion. Engineering Failure Analysis 39 (2014) 41–54地表爆炸作用下桩基础受到的影响及破坏机理分析【6】 Linas Gabrielaitisa , Vytautas Papinigis, Gintaras Žaržojus. Estimation of Settlements of Bored Piles Foundation . Procedia Engineering 57 ( 2013 ) 287 –293钻孔灌注桩摆放位置研究【7】 R.Ziaie-Moayed, M.Kamalzare and M.Satavian. Evaluation of Piled Raft Foundations Behavior with Different Dimensions of Piles .Journal of Applied Sciences 10(13):1320-1325,2010桩筏基础受桩不同尺寸影响的研究【8】 Paulus Karta Wijaya Parahyangan Catholic University, Dynamic Interaction Analysis of Soil and End Bearing Pile Using Boundary Element Model Coupled with Finite Element Model. 2009 First International Conference on Computational Intelligence, Communication Systems and Networks运用边界元和有限元模型研究土和端承桩的动态的相互作用关系研究【9】 Gianpiero Russo, Experimental investigations and analysis on different pile load testing procedures, Acta Geotechnica (2013) 8:17–31 DOI10.1007/s11440-012-0177-4不同桩荷载测试程序下的实验调查及研究【10】 Fleur Loveridge • William Powrie • Duncan Nicholson. Comparison of two different models for pile thermal response test interpretation . Acta Geotechnica (2014) 9:367–384 DOI 10.1007/s11440-014-0306-3两种不同桩模型热反应的对比试验研究【11】KaiWei andWancheng Yuan， Seismic Analysis of Deep Water PileFoundation Based on Three-Dimensional Potential-Based Fluid Elements。

一个欧洲土壤侵蚀模型：预测农田和小型积水地泥沙运移的动力学过程

KEY WORDS: soil erosion model; soil erosion processes; distributed modelling; dynamic modelling.
INTRODUCTION An increasing awareness by scientists, governments and the general public that soil erosion is an important problem within the countries of the European Community (Morgan and Rickson, 1990) has drawn attention to the lack of a satisfactory system in Europe for assessing the risk of erosion, predicting erosion rates and designing and evaluating different soil protection strategies. Present technologies for erosion assessment, based on scoring systems for rainfall erosivity, soil erodibility, slope and land use (Auerswald and Schmidt, 1986; Rubio, 1988; Briggs and Giordano, 1992; Jäger, 1994), provide information on the spatial distribution of erosion risk but only limited data on erosion rates. Attempts to use the Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978) in Europe as a technique for predicting erosion rates and evaluating different soil conservation practices show that great care is required in the selection of input values for rainfall (R) (Chisci and Zanchi, 1981; Richter, 1983) and soil erodibility (K) (Richter, 1980; De Ploey, 1986; Schwertmann, 1986) factors. Also, the equation is of limited value since it cannot provide information on the fate of sediment once it is eroded. The USLE is not able to predict deposition or the pathways taken by eroded material as it moves from hillslope sites to water bodies. In a European context, where the most important consequences of erosion are pollution and sedimentation downstream rather than loss of productivity on-site, policy-makers need to know more about the location of sediment sources and sinks. Further, the design of strategies to control pollution associated with runoff and erosion on agricultural land requires knowledge of what happens in individual rain

Soil Water Relationships

Soil Water Relationships
Soil Properties
• Texture
– Definition: relative proportions of various sizes of individual soil particles – USDA classifications
ψ = ψ +ψm +ψo
• Soil Water Release Curve
– Curve of matric potential (tension) vs. water content – Less water → more tension – At a given tension, finer-textured soils retain more water finer(larger number of small pores)
• Structure
– Definition: how soil particles are grouped or arranged – Affects root penetration and water intake and movement
USDA Textural Triangle
• Bulk Density (ρb) (ρ
Available Water
• Definition
– Water held in the soil between field capacity and permanent wilting point – “Available” for plant use
• Available Water Capacity (AWC)
– AWC = θfc - θwp – Units: depth of available water per unit depth of soil, “unitless” (in/in, or mm/mm) – Measured using field or laboratory methods (described in text)

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V olume modeling of soils using GRASS GIS3D-Toolspresented atSecond Italian GRASS Users Meeting,University of Trento,Feb.1-22001Markus NetelerUniversity of Hannover–Geographical InstituteSchneiderberg50–30167Hannover,Germanyneteler@geog.uni-hannover.deJuly2001AbstractThe analysis of spatial variability of soils has been of interest to soil scientists and geographers for quite some rmation on soil properties are usually available from a limited number ofpoint measurements and spatial estimates are prepared in two dimensions(e.g.by interpolationor other technique).However,soil is essentially a3D object with varying properties in all spatialdimensions.This study focuses on3D capabilities of GRASS GIS providing new3D tools to manipulate, analyse and model3D landscape phenomena.For example,the multivariate interpolation method–regularized spline with tension(RST)–has a capability to interpolate and analyse geometricproperties of selected soil properties in three-dimensional space.We investigate the options ofmodeling dynamic processes occuring in soil using simple3D map algebra algorithms.An inher-ent part of scientiﬁc investigation and analysis is visualization.New GRASS visualization toolsexploit3D OpenGL graphics capabilities,coupling to external visualization software(Vis5D,Hi-bbard et al.1994)allows animated views to time-dependent processes in soil volumes.An increasing number of available3D environmental data requires a complex GIS solution for manipulation,analysis and ing G3D library with new tools for modeling andvisualization,GRASS has proved that it fulﬁlls these requirements.Key words:volumes,voxel,GRASS,GIS,soil representation,visualization,dynamic processes1Introduction to3D raster modelingV olume modeling is a more and more demanded feature of Geographical Information Systems(GIS). As most real-world phenomena are located within3D space and usually also a time component (changes orﬂuxes),so the demand for a related3D/4D data representation in GIS is increasing. While volume analysis is already common in geophysics and groundwater modeling software pack-ages,such tools are not yet integral component of off-the-shelf GIS software.There is a need for modeling tools as numerous countries have environmental laws which demand the assessment of the effects of certain plans and programmes on the environment.A recent example is the“Strategic Environmental Assessment for policies,plans and programmes”(SEA)from European community.On31May2001the European Parliament and on5June2001the Council formally adopted the SEA Directive2001/42/EC.It shall ensure that signiﬁcant environmental impacts are identiﬁed and assessed and taken into account in the decision-making process to which the public can participate1.This paper focuses on GIS-technical aspects of3D modeling.Depending on the scientiﬁc context, two main approaches are used for volume representations:three dimensional triangulated irregular networks(3D TINs)based on vector/point information, and3D raster pixels(voxels).Theﬁrst approach commonly uses3D Delaunay triangulation with minimized interior angles and with the property that a circle around three points of any triangle doesn’t include any other points(Tucker et al.2000).As a major advantage TINs support dynamic resolutions:An optimized data representation is possible through variable resolution,i.e.different triangle sizes.However,the general structure of TINs leads to complex algorithm in case of topological analysis or transport processes uallyﬁnite elements orﬁnite differences are used demanding high computational power as provided by parallel computers.A different approach of volume discretization are volume pixels,so-called“voxels”.As the cube edge lengths areﬁxed,the resolution is common within the volume.This results in a cubic growing memory demand.This disadvantage is nowadays solved technically as ordinary PC workstations provide a large amount of memory.The major advantage of voxels is the simple internal structure with implicit topology.So the voxel format reduces the complexity of data management as no mesh is needed to be established.The voxel approach implemented in GRASS-3D performs on rather any (PC)workstation.In contrast to TINs the problem of resolution deﬁnition has to be addressed before starting the modeling process.As known from2D raster processing the3D raster resolution has to be deﬁned according to the highest resolution needs.The user should consider the relation between optimal resolution and increasingﬁle memory and computation power demands-a balance between performance and accuracy.The basic voxel library development has been undertaken a few years ago,along with data man-agement and interpolation tools voxel support was integrated into GRASS5.0.0recently.Beside data import and export modules two volume modeling tools are available.They allow to interpolate volumes from3D sites data(point data with three spatial dimensions and values),either utilizing the3D-IDW(inverse distance weighted)or the3D-RST(regularized splines with tension)algorithm. Spatially dependent analysis can be performed with a3D map calculator which allows to use common algebraic,trigonometric and binary functions and operators.Finally two visualization approaches are available:A GRASS internal OpenGL-based viewer for isosurface visualization and an interface to the external Vis5D OpenGL-based visualization and query tool initially developed for meteorological applications.2The“Ambergau”study siteThe project area is situated16km south-east to Hildesheim southern to Hannover in Lower Saxony. It is part of the Lower Saxonian hill country with an average elevation height of200m and average slopes around4degrees.Due to the loessial cover and the long-term intense agricultural landuse, soils have been transformed to para-brown earths.For these investigations78soil samples were used from a survey undertaken at Institute of Physical Geography and Landscape Ecology,University of Hannover.The survey area size is3.5km x3.5km.Per drill hole three toﬁve samples have been taken at different depth and analysed in laboratory.Various soil parameters have been measured to describe soil characteristics.However,the present study focuses on technical aspects rather than soil scientiﬁc questions.33D data handlingV olumetric sample data are collected at3D spatially distributed sampling ually,especially in soil sciences,samples are taken along the third dimension(depth)in irregular spacings.The data representation for GRASS volume modeling uses3spatial dimensions(x,y,z)and one or more data dimension(w)(Neteler2000):The well known“sites lists”can be used to generate such sample lists,they can be extended to any dimension and hold multiple attributes for one point.The format for this record is:east|north[|dim]...|#cat%double[%double]@string[@string]Note,that the pipe character("")is used to separate the dimensionﬁelds in the records,blank spaces are used to separate decimal and string descriptions.This format can be written either directly and theﬁle stored in$LOCATION/siteFigure1:Nadir view on3D-IDW vol-ume cross section from interpolated pH values based on3D soilsites.Figure2:Nadir view on3D-RST volume cross section from interpolated pH val-ues based on3D soil sites.In contrast to direct conversion the s.vol.idw module(developed by Hoﬁerka1999from2D s.surf.idw algorithm)interpolates missing values.Fig.1demonstrates a horizontal cutting plane through the interpolated volume.However,it is obvious that the search radius of the moving window leads to some unexpected results,the3D-IDW tends to cluster in case of distant sample points.A much more sophisticated interpolation tool is s.vol.rst(developed by Mitás,Mitásováand others, see also Hoﬁerka et.al.2002).It uses the regularized splines with tension(RST)algorithm(Mitásová&Mitás1993,Mitásová&Hoﬁerka1993)in three dimensions.Beside the core interpolation method it offers calculations of various geometric parameters:magnitude of gradient,horizontal and vertical aspects,change of gradient,Gauss-Kronecker and mean curvatures.Of interest for a mixed2D/3D analysis are cross sections,the module will produce2D maps but utilizing the3D-RST algorithm for calculations(for applications see Hoﬁerka et.al.2002).Fig.2shows the same soil pH values interpolated with s.vol.rst.4.2Further3D management and modeling toolsSimilar to2D GRASS masks can be deﬁned in3D space using r3.mask.Such masks may be devel-oped with r3.mapcalc module(developed by Paudits&Hoﬁerka2000from r.mapcalc).However, the strength of this module lies in it’s capability for3D data manipulation and exploration as alge-braic,trigonometric and binary functions and operators are provided.For complex operations a3D neighborhood modiﬁer is available:voxel positions relative to the moving voxel center can be deﬁned as map[r,c,d](row,column,depth).In iterative environments like scripts r3.mapcalc can be used to dynamically simulate time variant processes in a volume.A simple example shall demonstrate how to achieve dynamic modeling using r3.mapcalc.The script is adapted from the hydrologic model presented by Shapiro&Westervelt1992and extended to3D. While the2D r.mapcalc uses a3x3moving window with a centered pixel,the3D module r3.mapcalc used a3x3x3cube with a centered voxel.Considering a soil volume,the cube shows three planes.For a dynamic model the upper and the middle plane shall be considered.Related to the center voxel of the cube(located in middle plane)ﬂuxes from and to this middle voxel of the cube are considered de-pending on the local gradient.Generally a cube offers3-1=26directions related to the middle voxel(3-1=8directions in2D space).For the simple dynamic model presented here17ﬂow directions are taken into account as the moving cube will itself decent within the soil volume.Capillary rise it not implemented here.For a more realisticﬂow distribution,the17ﬂuxes are weighted according to their direction.The overall contribution to the middle voxel is set to100%.Following assumption is implemented within the model:The upper plane shall contribute70%ofﬂows into vertical directions,while the middle plain(except the middle voxel)contributes30%of lateralﬂuxes.This leads to a set of con-ditions,nine conditions for the upper plane of the3x3x3cube and8conditions for the middle plane, altogether17if-conditions as described below.The individual voxel contributions are dependent from their geometrical position to the middle voxel.So the individual weighting is calculated according to the contribution-per-plane and position.This leads to three equations for the upper plane and two equations for the middle plane which can be easily solved.Each equation for the outer voxels is used four times,so the weight coefﬁcient is divided by four to receive theﬁnal weights as they can be found in the script below.The example below has been run on a volume of permeability coefﬁcients(rendered with s.vol.rst) which have been measured in the soil proﬁles of the Ambergau study site.During the simulation a wa-terﬂow(volume“water”)is drained through the volume,controlled by the permeability coefﬁcients stored in the volume(volume“pcoeff”):water=water+eval(x=pcoeff+water,\if(x>(y=pcoeff[0,0,-1]+water[0,0,-1]),\-.3064*if(pcoeff>y,water,x-y),\.3064*if(pcoeff[0,0,-1]>x,water[0,0,-1],y-x))+\if(x>(y=pcoeff[0,-1,-1]+water[0,-1,-1]),\-.0542*if(pcoeff>y,water,x-y),\.0542*if(pcoeff[0,-1,-1]>x,water[0,-1,-1],y-x))+\if(x>(y=pcoeff[1,0,-1]+water[1,0,-1]),\-.0542*if(pcoeff>y,water,x-y),\.0542*if(pcoeff[1,0,-1]>x,water[1,0,-1],y-x))+\if(x>(y=pcoeff[0,1,-1]+water[0,1,-1]),\-.0542*if(pcoeff>y,water,x-y),\.0542*if(pcoeff[0,1,-1]>x,water[0,1,-1],y-x))+\if(x>(y=pcoeff[-1,0,-1]+water[-1,0,-1]),\-.0542*if(pcoeff>y,water,x-y),\.0542*if(pcoeff[-1,0,-1]>x,water[-1,0,-1],y-x))+\if(x>(y=pcoeff[-1,-1,-1]+water[-1,-1,-1]),\-.0442*if(pcoeff>y,water,x-y),\.0442*if(pcoeff[-1,-1,-1]>x,water[-1,-1,-1],y-x))+\if(x>(y=pcoeff[1,-1,-1]+water[1,-1,-1]),\-.0442*if(pcoeff>y,water,x-y),\.0442*if(pcoeff[1,-1,-1]>x,water[1,-1,-1],y-x))+\if(x>(y=pcoeff[1,1,-1]+water[1,1,-1]),\-.0442*if(pcoeff>y,water,x-y),\.0442*if(pcoeff[1,1,-1]>x,water[1,1,-1],y-x))+\if(x>(y=pcoeff[-1,1,-1]+water[-1,1,-1]),\-.0442*if(pcoeff>y,water,x-y),\.0442*if(pcoeff[-1,1,-1]>x,water[-1,1,-1],y-x))+\if(x>(y=pcoeff[0,-1,0]+water[0,-1,0]),\-.0439*if(pcoeff>y,water,x-y),\.0439*if(pcoeff[0,-1,0]>x,water[0,-1,0],y-x))+\if(x>(y=pcoeff[1,0,0]+water[1,0,0]),\-.0439*if(pcoeff>y,water,x-y),\.0439*if(pcoeff[1,0,0]>x,water[1,0,0],y-x))+\if(x>(y=pcoeff[0,1,0]+water[0,1,0]),\-.0439*if(pcoeff>y,water,x-y),\.0439*if(pcoeff[0,1,0]>x,water[0,1,0],y-x))+\if(x>(y=pcoeff[-1,0,0]+water[-1,0,0]),\-.0439*if(pcoeff>y,water,x-y),\.0439*if(pcoeff[-1,0,0]>x,water[-1,0,0],y-x))+\if(x>(y=pcoeff[-1,-1,0]+water[-1,-1,0]),\ -.0311*if(pcoeff>y,water,x-y),\.0311*if(pcoeff[-1,-1,0]>x,water[-1,-1,0],y-x))+\ if(x>(y=pcoeff[1,-1,0]+water[1,-1,0]),\-.0311*if(pcoeff>y,water,x-y),\.0311*if(pcoeff[1,-1,0]>x,water[1,-1,0],y-x))+\if(x>(y=pcoeff[1,1,0]+water[1,1,0]),\-.0311*if(pcoeff>y,water,x-y),\.0311*if(pcoeff[1,1,0]>x,water[1,1,0],y-x))+\if(x>(y=pcoeff[-1,1,0]+water[-1,1,0]),\-.0311*if(pcoeff>y,water,x-y),\.0311*if(pcoeff[-1,1,0]>x,water[-1,1,0],y-x)))This model has been stored as“3dvolFigure3:Dynamic waterﬂow through a soil volume using r3.mapcalc–displayed with Vis5D visu-alization tool.directly generated with r3.mapcalc.A simple example is the initialization with an overall identical water input. First a volume of water in GRASS3D is generated(5mm):r3.mapcalc waterraw="5"Then this volume needs to be reduced to the surface,below the voxels are initialized with0:r3.mapcalc water="if(depth()>1,0,waterraw)"The contents of the volume“water”may be veriﬁed either with r3.out.ascii or by using visualization tools described below.When running the“3dﬂow.sh”every10th volume will be exported into Vis5D format using the“r3.out.v5d”module.These volumes will show the waterfront passing through the soil volume depending on the local“pcoeff”values within vertical direction.An example can be seen inﬁgure3.5Data export and visualizationThe new volume analysis and visualization tools are of special interest as they are seamless integrated into a common GIS environment.This minimizes efforts of data conversion between data import,analysis and visu-alization.Two different methods are available:A GRASS built-in volume viewer which is still in experimental stage and an interface to the external,freely available,Vis5D volume viewing and query tool.5.1GRASS built-in viewing toolsA low level option to display the spatial distribution (but not the attributes)is to convert the calculated volume back to 3D sites using r3.to.sites ,then to display these sites in NVIZ (see ﬁg.4).Alternatively GRASS provides the experimental OpenGL viewing tool r3.showdspf .First a “display ﬁle”has to be generated with r3.mkdspf to deﬁne levels of isosurfaces.In volumetric environments isosurfaces are the analogue to isolines in 2D environment surfaces of identical value.Those can then be displayed (zoom,rotation,selective display)through r3.showdspf (see ﬁg.5for 3D pH values).5.2Coupling to external OpenGL viewer Vis5DAfter exporting with r3.out.v5d GRASS volumes can be displayed in Vis5D visualization software (Hibbard et al.1994).This tool offers various methods to render rotatable semi-transparent volumes,isosurfaces,movable cutting planes and isolines.The code is based on OpenGL which may use hardware acceleration for volume display if a special video card is used.Of special interest is the feature of 3D queries within the volume.Fig.6shows an isosurface view onto the pH value interpolated volume.The same volume can be displayed as semi-transparent volume (ﬁg.7).6Future needsTo improve the interpolation of volumes more closely to the analysed phenomena,a constraint interpolation would be needed.In case of improved soil volume modeling the introduction of 3D boundaries probably in vector format,could support the quality of regionally restricted interpolation keeping soil horizon boundaries or stratiﬁcations.Due to different data availability the implementation of multiple resolution within one volume may achieve the advantage of dynamic resolution (comparing to TINs)while keeping the simply intrinsic topology of voxels.A way to support dynamic resolutions may be reached through voxel management in oct-tree structures.The module s.vol.rst internally already uses oct-trees for large data processing,so the basic routines are already present in GRASS 5.0.0.The powerful voxel calculator r3.mapcalc needs a built-in access to 2D raster data (as already found in s.vol.rst ).In terms of soil modeling 2D data may be used as “seed information”to simulate ﬂuxes within the volume which start on top of it.Figure 4:Volume visualized as 3D sitesin NVIZ visualization tool.Figure 5:Volume visualized withr3.showdspf visualization tool.Figure6:3D pH values displayed in Vis5D visualization tool:isosurface viewAt time of this writing still inconsistencies between the2D and the3D environment have to beﬁxed.This will be addressed in a future release.7SummaryFrom the technical point of view the implemented voxel technology performs on common PC workstation.The present modules are much more than a basic environment for3D modeling,the strength lies in the seamless integration into a GIS.Beside volume interpolation GRASS offers a comprehensive voxel calculation tool which is the3D version of the well known2D map calculator.V olumes can be visualized internally and also exported to Vis5D tool.There is a need for integration of tilted or curved surface boundaries,which can eventually be achieved through vector representation.From the scientiﬁc point of view the main problem is the availability of3D data.However,this problem may be solved only by the GIS user through intense surveys rather than through modiﬁed programs.It is important to note that full-volume-interpolations don’t represent natural phenomena if the raw data are sparse.Due to the current module limitations3D modeling within boundaries(e.g.soil or geological units)is not possible. For dynamical modeling the time representation is incomplete,however,the current version already supports timestamps.As GRASS is open source software,programmers interested in3D developmentﬁnd full accessto code and algorithms.Figure7:3D pH values displayed in Vis5D visualization tool:volumetric view8ReferencesHibbard,W.L.,B.E.Paul,D.A.Santek,C.R.Dyer,A.L.Battaiola,M.-F.V oidrot-Martinez(1994)–Interac-tive Visualization of Earth and Space Science Computations,Computer27,No.7,July1994,65-72./~billh/vis5d.htmlHoﬁerka,J.,J.Parajka,H.Mitásová,L.Mitás(2002)–Multivariate Interpolation of Precipitation Using Regu-larized Spline with Tension,Transactions in GIS,(accepted for publication).Mitás,L.,W.M.Brown,H.Mitásová(1997)–Role of dynamic cartography in simulations of landscape pro-cesses based on multi-variateﬁputers and Geosciences,V ol.23,No.4,pp.437-446,http://www.elsevier.nl/locate/cgvis.MitásováH.L.Mitás(1993)–Interpolation by Regularized Spline with Tension:I.Theory and Implementa-tion,Mathematical Geology25,641-655.MitásováH.,J.Hoﬁerka(1993)–Interpolation by Regularized Spline with Tension:II.Application to TerrainModeling and Surface Geometry Analysis,Mathematical Geology25,657-667.Mitásová,H.,W.M.Brown,J.Hoﬁerka(1994)–Multidimensional dynamic cartography.Kartograﬁckélisty 2,pp.37-50.Mitásová,H.,L.Mitás,W.M.Brown,D.P.Gerdes,I.Kosinovsky(1995)–Modeling spatially and temporally distributed phenomena:New methods and tools for GRASS GIS.International Journal of GIS,9(4),special issue on integration of Environmental modeling and GIS,p.443-446.Neteler,M.(ed.)(2000)–GRASS5.0Programmer’s Manual.Geographic Resources Analysis Support Sys-tem.University of Hannover.http://grass.itc.it/grassdevel.htmlShapiro,M.,J.Westervelt(1992):r.mapcalc.An Algebra for GIS and Image Processing.U.S.-CERL,Cham-paign Illinois,22pp.http://grass.itc.it/gdp/Tucker,G.,N.Gasparini,R.Bras,S.Rybarczyk,ncaster(2000)–An Object-Oriented Framework for Distributed Hydrologic and Geomorphic Modeling Using Triangulated Irregular Networks,Computers and Geosciences,in press.11。