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5122011 International Conference on Computer, Electrical, and Systems Sciences, and EngineeringThe Application of BP Neural Network Model in the ProjectImplementation ControlYang Xiaoping, Wu ShengcaiManagement CollegeUniversity of Science and Technology of China Hefei 230026, China wsc854@Abstract: - The application of BP neural network in dynamic management of the implementation and control ofthe construction project is a scientific method corresponding to the practice and its importance has gradually been valued by parties in project activities. Based on the theory of project implementation phase dynamic management, this paper uses the BP neural network method to analyze the projects in implementation phase. It focuses on the analysis of the idea works that using the BP neural network theory and application theory to the project control research, and discusses the dynamic modeling, the learning and training algorithms of the hidden layer space structure, and forming the training sample set from the collected project costs information, finally achieving the application of the project costs dynamic management of the project implementation phase based on BP neural network model.Key-Words: - BP neural network, project implementation control, model, sample1 IntroductionIn recent years, in the international developed countries, the requirements for project investment are to pre-control in advance and control in the progress. Chinese traditional methods will objectively cause the result of despising decision-making, valuing implementation, despising economic and valuing technology, building first and evaluating later. In this way, make the long-term management target difficult to achieve. For the above situation, from the perspective of construction enterprises, based on the analysis of the status of domestic and international project management, on the guide of the basic theory of the project dynamic management of project implementation phase, using BP neural network theory to built the construction costs dynamic management model in the implementation phase. Considering the various effect factors in the implementation phase of project, analyzing the influence range that each effect factor did to the construction costs and quantities, the author hopes to find a more effective and quick way and method to control the project price and save social resources. This has practical guidance meaning to the owners to strengthen the costs management in the construction phase, has good enlightenment to the enterprises to raise their own construction costs management level. Therefore, select this topic to research has great theoretical and practical significance; it is in line with the requirements of the age and is an urgent and beneficial subject.2 BP neural network theoryNeural network (or known as connection model) is the abstraction and simulation of human brain or some basic features of the natural neural network. It originated from the interdisciplinary research of physics, psychology and neurobiology. The basical unit of neural network is the nerve cells, nerve cells also known as biological neuron, in short, neuron. BP network is a one-way transmission a nd multilayer forward network, which has three layers or more than three-layer network structure, including input layer, middle layer (hidden layer) and output layer, there are whole connections between the upper and lower layers, but no connection between eachneuron. Specific structure shows as Fig. 1.Fig. 1 BP network model constructionBP network transfer function is generally Sigmoid function, for the BP network, its transfer function must be differentiable,978-0-615-42292-3/10/$25.00 ©2011 IERICESSE2011generally is Sigmoid type logarithm, tangent function or linear function. Since transmission network is differentiable in all respects, so for the BP networks, on the one hand, the network strictly using gradient descent method to learn, the value of modified analytic formula is very clear; on the other hand, the divided area is no longer a linear division, but a region constitutes by a non-linear hyper plane, it is a relatively smooth surface, and thus its classification is more accurate than the linear division, fault tolerance is better than the linear division. The working progress of BP network is divided into two parts, study period and work period. Study period is composed of the progress of the forward propagation of input information and the back propagation of error. BP algorithm's basic idea is that the learning process is composed of the signal forward propagation and error back propagation two processes. In the forward propagation process, the input information from input layer to hidden layer and then to output layer, it is dealt with layer by layer, the state of neuron in each layer only affect the state of neuron in next layer. information and data of typical project (including construction costs, index of price rises and project features Etc.), consider the specific circumstances of the project being built and then determine the cost of the project being built. Then we have to calculate the construction costs and costs’influence range of the project being built and so on. In order to be more accurate and efficient to determine these data, we must build a dynamic model. The use of neural networks to establish mathematical model in essence is using the function approximation ability of neural network to map the actual complex functions.This paper uses BP neural network to do the mathematical model research of the construction cost of project implementation phase. Treat the construction cost of implementation phase as a mathematical mapping problem, one of the important functions of neural network is that it can increase and get close to nonlinear mapping in any two-dimensional space. In the model, assume that the feature vector affected the construction cost is m (m ≥3), the main required cost information is n (n ≥1), there is mapping from m-dimensional to n-dimensional space. If treat m as the neural network input data, n as the neural network output data, in the m Dimensional Euclidean space R m , there is a bounded subset A. There is a mapping of boundedThe choice of error bound value is solely determined on the base of the convergence speed size subset to the n-dimensional spacem nR n , that isof the network model and learning accuracy of the F : A ⊂R→R .By learning we can find a optimalcertain samples. When the selection of convergence error bound value Emin is small, the learning effect is good, but the convergence speed is slow and training times increase. If the selection of convergence error bound value Emin is larger, the result is opposite. Because the error of training samples can be very small, it is reasonable and reliable to use one part of the error of test samples which randomly extracted from the total sample to stand for the accuracy (network performance) of network model calculation and forecast. It should be noted that the generalization ability of network model is not determined by the size of test samples’error, but whether the test sample s’error is approached to training samp les’ and checkout sam ples’ error.3. The mathematical model o f the construction project dynamic management researchThe basic idea of application of BP neural network dynamic management model is that according to the construction similarity of the project being built and typical project, we can refer to the historical approximate mapping G. Theoretical proof that two layers of the forward network can find a G near F in any given accuracy.For the implementation phase of construction projects, the input indexes of project cost dynamic management model are the composition architecture, structural feature set and the project costs, such as labor cost, materials cost, design change and other factors. The inputs of artificial neural network are those output characteristics sets and can be chosen free based on the actual project needs. As for those should be valued according to the actual situation, value them according to the construction practice. If necessary, take the expanded unit, so the value can be controlled in the range -1 to 1. For the output values, which mainly are project cost, labor work day, wood, steel, bricks, cement, gravel, asphalt, linoleum, glass and others, value them according to the practice. And for the measurement unit, the expanded unit is appropriate, and the corresponding value should be controlled in the range -1 to 1.In this paper, it takes the algorithm of error back propagation (BP), which is as follows:513514t t tt Assume artificial neural input pattern vector （5）Calculate the generalization error d kof theA k = ( a 1 , a 2 , ......, a n ) ， expectation outputunits in output layer with the expectation outputvector Y k = ( y 1 , y 2 , ......, y q )； model Y k = ( y 1 , y 2 ,......, y q) and network actualInterlayer unitinput vectoroutput c t :S k = ( s 1 , s 2 , ......, s p ) ，outputk ( ) (1 )dt= y t - c t ⨯ c t ⨯ - c t （6）vector L k = (l 1,l 2 ,......,l p )； （6）Calculate the generalization error ekof theOutput layer unitinputunits in interlayer with the generalization error d k of vector i = 1, 2, ......, n ; j = 1, 2,......, p ,outputthe output layer of connection weight v jtandvector C k = (c 1 , c 2 ,......, c q ) ；Connection weight from input layer to interlayer isinterlayer output b j :w ij ， i = 1, 2,......, n ; j = 1, 2,......, p ;（7） Connection weight from interlayer to output layer E =v jt ， j = 1, 2, ......, p ； t = 1, 2,......, q （7 ）Revise connection weight v jt and domain kOutput domain value of the units in interlayer is value γ t with the generalization error d t of the units θ j ， j = 1, 2,......, p ；Output domain value of the units in output layer isin output layer and output b j interlayer: of the units inγ t ， t = 1, 2,......, q ； in the above,( 1) ( ) kv jt N + = v jt N + α ⨯ d t ⨯ b j （8）k = 1, 2,......, m ；m is the logarithm of training set ( 1) ( ) kmodel;γ t N + = γ t N + α ⨯ d t （9）Network response function is: In the formula, α is learning coefficient,f ( x ) = 1 1 + e（1） 0 < α < 1 . （8）Revise connection weight w ijand domainThe steps of BP Neural network learning model are:value θ j with the generalization error e kof the units（1）Initialization. Give random number choosing in output layer and input A k of the units in input layer:from [-1, 1] to connection weights w ij ， v jt and w ( N + 1) = w ( N ) + β ⨯ e k ⨯ adomain values θ j ，γ t ;ij ij t i（10）(2) Randomly provide the network with one modelθ (N + 1) = β ( N ) + β ⨯ e k （11） pair A k = (a 1 , a 2 , ......, a n ) ，Y k = ( y 1 , y 2 ,......, y q ); j j t In the formula, β is learning coefficient, （3）Calculate input s j and output b jof the units 0 < β < 1.（9）Return to the (2) step until the end of thein the interlayer with input model A k , connectionstudy on m pairs of models.weight w ij and domain v a lue θ j : ns j = ∑ w i j ⨯ a i - θ j i =1（2）（10）Re-choose randomly one pair of model from m model pairs, return to the (2) step until the overall error E is less than a predetermined b j = f (s j )（3）minimum value or a given learning number;（4）Calculate the input of the units in outputlayer l t and output response c twith interlayer E =（12）output b j , connection weight v jt and domain Where, M is the logarithm of the sample, Q is the value γt :p l t = ∑ v ij ⨯ b j - γ t j =1（4）number of output units, and other symbols are the same with the previous.c t = f ( s j )（5）5151 -0.40 output -0.75 775.46800.66 -3.25 2 0.68 0.43 867.81 885.78 -2.07 3 -0.68 -1.00 761.33 783.71 -2.94 4 -0.22 -0.23 838.33 839.09 -0.09 5 -0.29 -0.19 842.72 841.96 0.09 6 -0.44 -0.21 857.08 840.45 1.94 7 0.17 0.11 858.55 863.10 -0.53 8 -0.11 0.02 865.82 856.47 -1.08 9 0.78 0.39 855.99 883.04 -3.16 （11）Save the trained weights w ij , v jt , domainTable 1 Simulated results and actual initializedresults of the test samples values θ j , γ t of th e network into the relevant database in order to use.4. The application of B P Neural network in control of project implementationTo carry out the dynamic management of the engineering control from the view of the construction companies, we must first consider the factors that affect the project, and determine the No.Simulated initialized results of test samples The initial value of the test samples The initial results of test samplesActual values of test samplesRelative error （%）parameters of project control model. Construction cost of the project depends on the quantities of each physical part, type and price level. Physical quantity is usually determined by the structure parameters of the designed project. Through the analysis of cost components and the effects of structure parameters variation on the cost of the already-built typical projects, determine preliminarily the foundation, structure, floors, doors and windows, decorative, wall, flat combination as the main factors of the project cost, saying it is engineering characteristics. Use characterization of construction projects to describe samples of construction projects, namely: construction project = (foundation, structure, floors, doors and windows, exterior decoration, graphic combinations). The engineering characteristics of construction projects have different types (such as the structure can be masonry structure, frame structure; the base section can be brick foundation, reinforced concrete foundation, etc.), which are called the feature categories.Express the neural network input vectors respectively with symbols T1 ~ T8, and output vector with the symbol O. Divide the collected 50 samples into two groups and choose 40 samples randomly as training samples and the remaining 10 samples as testing samples. According to the characteristics of neural network, the neural network input and output data are all in [-1, 1] range. Therefore, the first is to quantify the neural network input vector. Initialize feature vectors of the project after the completion of the qualification.By using BP neural network to process the test samples simulation at implementation stage of the construction project, the maximum relative error of the output results and the actual value is 3.25%. It is in the control range of project cost in implementation phase. Therefore, the accuracy of the results obtained in this example meets the requirements.100.490.11847.76 868.87 -2.495 ConclusionThe application of BP neural network in dynamic management of the implementation and control of the construction project is a scientific method corresponding to the practice and its importance has gradually been valued by parties in building activities. Basing on the dynamic management theory of the implementation phase of construction projects, this paper analyzes and studies the project in the implementation phase using BP neural network method, makes full use of high nonlinearity, high fault tolerance, self-learning, real-time processing and other characteristics of neural network. It mainly analyzes BP neural network theory and the idea that applying the control theory in project control, discusses the dynamic modeling and the learning and training algorithms of the hidden layer space structure, and forms the project cost data collected from the training sample set ultimately achieving the application of BP neural network model in the dynamic management of project cost in implementation phase of the project.References:[1] Yingluo Wang, Yaohong Yang. Artificial Neural Network and Its Application in Project Management. Engineering Science, 2009, 6(7)： 26-33．[2] Feng Zhao. 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