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Risk Evaluate the Construction Project Based on BP Neural Network

Xing Li-ying School of Civil Engineering, NanYang Normal University

Nanyang City, P.R.China xly19854525@126.com

Wang Xin-zheng School of Civil Engineering, NanYang Normal University

Nanyang City, P.R.China wxz791023@126.com

Abstract—Traditional risk assessment methods of construction project are often affected by the subjective factors. In order to reduce or avoid the effect of subjective factors, This paper firstly established the project risk evaluation index system, based on the detailed analysis of the project’s internal and external environment, then built up the risk evaluation model with BP neural network, learned and trained the model by MATLAB neural network toolbox. The training datas shows that the model has the more accurate result and the comprehensive practicability.

Keywords-Risk evaluation；Neural network；Risk evaluation index

I. PERFACE In recent years, Chinese infrastructure’s investment scale

have enlarged unceasingly, project construction’s management has developed toward the specialization and the technicalization, and the construction’s management level also has been improved greatly. The process of construction project is full of various risks, which is affected by many factors under the complex natural and social environment. If improper management, it inevitablely will affect the cost, schedule and quality of the construction projects[1]. To avoid and reduce the loss, it is very important of prior understanding and mastering the risk origin of construction project process, correctly evaluating and effectively dealing with the risks, and establishing the risk prevention plan.

At present, the common methods of risk assessment have Expert Evaluating method, Analytic Hierarchy Process (AHP), Fuzzy Comprehensive Evaluation method, Monte Carlo Simulation method. The results of these methods can partly reflect the size of the risks’ level, but they have a common fault, that is these methods involve a lot of qualitative indicators, and the subjective factors have a bigger effect on these indicators. For example, many weights value are estimated by the experts, which easily leads to an impractical or even wrong conclusion, and influences the risk decision-making of risk management. In recent years, neural network, with its special advantages: self-learning, self-organizing, self-adaptive capacity, overcomes the subjective factors’ influence, thus it has being increasingly applied in construction project’s risk comprehensive evaluation. Neural network’s nonlinear mapping ability and pattern recognition ability can dynamically identify and predict the risk, and provide a basis for the risk

decision[2-3]. Therefore, it has a very important theoretical significance and practical value of strengthening the application research of neural network in the project risk management.

II. CONSTRUCTION PROJECT RISK EVALUATION’S PROCESS Risk evaluation is the key of construction project risk

management work. The process of risk evaluation mainly includes the two stages: risk identification and risk analysis.

A. Risk identification Risk identification refers to the investigation research of

various aspects and every key process of the construction projects, thereby identifies and records the risk process. Risk identification is the first step of the risk evaluation process, its basic task is to search the entire project, and to find those risk events that will hinder the project realising its goal. The basic thinking and the implementation procedure of risk identification are shown in Figure 1. People have made a lot of research work about the risk management, many literature has established many different construction project’s risk evaluation index systems from different angles. But overall, the construction project’s risk generally can be divided into political risk, natural risk, economic risk, technical risk and management risk, and these five categories also can be further divided into eleven kinds, which is shown in Table 1.

Figure 1. The basic thinking and the implementation procedure of risk identification

Supported by the Soft-Science Program of HeNan Province (092400440076); Education Department of HeNan Province(2009B630006) and Soft-Science Program of NanYang City (2008RK015))

Make clear the risk identification’s major objective, and optimize the risk identification method according to the project’s characteristics

Divide the risk source’s large category according to the risk nature, then further subdivision the category with experts investigation

method

According to the risk source’s small categories and risk identification’s objective, design the all departments’ risk questionnaire survey and the expert’s questionnaire survey

Preliminary analysis and sort out the questionnaire and the expert’s information

Analyse the risk’s feature, according to the risk’s analysis results and identification results compile the risk list

978-1-4244-5326-9/10/$26.00 ©2010 IEEE

TABLE I. THE ASSESSMENT INDEX SYSTEM OF PROJECT RISK

B. Risk analysis Risk analysis refers to the investigation research of various

identified risk events to further refining the risk description, and finding out the risk’s influence factor. Its purpose is to collect the sufficient information of these risks, to judge the occurrence probabilities of various risks and the results of the risk carring about the project performance, tempo and cost. If having these information, it can determine the risk grade according to the project standards.

(1) Determine the possibility of risk happening

Table 2 can be used to determine the possibility grade of various risks happening. Among the table, risk probability refers to the possibility of various risk happening

TABLE II. RISK PROBABILITY’S GRADE

Grade Risk probability a Minimum possiable b Unlike liness c possiable d Likely e Almost certainly

(2) Determine the risk occurred consequence

The risk occurred consequence involves various problems, for a construction project, here mainly evaluates from four aspects: quality, schedule, costs and other aspects, at least one aspect as a risk to consider, which is listed in Table 3.

(3) Determine the risk factor’s grade

Risk grade is determined by the possibility of risk happening and the influencing degree of risk consequence together. Table 4 lists the risk’s grade of the two factors. It can be seen from the table, if using the corresponding basic dimension accurately quantifies the risk probability and the risk consequences, we can accordingly get the quantification express of the risk grade.

(4) Determine the risk level of the whole project

The basic principle of applying BP network to carry on the risk evaluation is, take the risk grade as the neural network's input vector, and the project’s risk assessment value as the output. Before the network, firstly use some successful system

samples to train the network, which make its peculiar weights coefficient value get the correct internal relations through adaptive learning. The trained neural network can be taken as the effective tools for project risk evaluation.

TABLE III. RISK CONSEQUENCE’S GRADE

Grade The influence degree after the risk becoming fact Quality Speed Cost Others

1 Influence minimum

Influence minimum

Influence minimum

No influence

2 Some residual Can be accepted

Basically satisfy the schedule

Less than 5%

Some difference

3 Residual greatly reduced, still acceptable

Lightly delay the progress

5%～7% Moderrate impact

4 No relief allowance reluctantly acceptable

Significantly delay the progress

More than 7%～10%

Significant impact

5 Unacceptable Key milestone can’t pass

More than 10%

Unacceptable

TABLE IV. DETERMINE THE RISK GRADE

Risk possibility’s grade

Risk consequence’s grade 1 2 3 4 5

a Middle Middle High High High b Low Middle Middle High High c Low Middle Middle Middle High d Low Low Low Middle Middle e Low Low Low Low Middle

III. BASED ON BP NEURAL NETWORK ESTABLISH THE RISK EVALUATION’S MODEL

A. Basic principle of BP neural network Artificial neural network is a method of information

processing, which is developed by the biological neural systems inspired. Based on the learning sample process, the artificial neural network analysis the data mode, builds the model and then finds some new knowledge. Neural network can automatically adjust the neurons input and output in accordance with the rules through learning, to change the internal state.

Back-propagation network (BP neural network) is the most widely neural network model. The BP neural network process

Risk categories Specific indexes Indicators describe political

risk administrative intervention risk administrative departments’ excessive intervention and improper command to the project

policies and regulations risk project construction system’s and policy’s change natural

risk weather conditions risk bad weather conditions’ effect on the construction and the difficultity to the construction

catastrophe risk earthquakes, floods,etc. some overpowering disasters

economic risk interest rate risk country micro-scope interest rate adjustment prices rising risk engineering materials’ prices rising, such as cement

technical risk

feasibility research risk the wrong decision caused by the imperfect work during the feasibility research stage, etc. design risk defective designs, error or omission, unreasonable selection of the safety coefficient, etc.

construction risk lagging construction technology; unreasonable construction technologies and solutions, etc.

management risk project target control risk poor control measures such as schedule, cost, safety, etc. poor control measures

business activities risk poor management, reachless expected earnings

is composed of the learning signal’s forward-propagating and the error’s back-propagating.

(1) Based on the BP algorithm feedforward network model

The BP network may have multiple hidden layer, but the single hidden layer’s BP network is in common use. The single hidden layer’s BP network is composed of three parts: the input vector [ ]Tni xxxX ,,,1= , the hidden layer’s

output vector [ ]Tmj yyyY ,,,1= , the output layer’s output

vector [ ]Tlk oooO ,,,1= , the expected output vector

[ ]Tlk dddD ,,,1= .The weight matrix from the input layer to

the hidden layer is expressed by [ ]Tmj vvvV ,,,1= ,

among the formula, the column vector jv is the j neuron’s corresponding weight vector of the hidden layer. The weight matrix from the hidden layer to the output layer is expressed by [ ]Tlk wwwW ,,,1= , among the formula, the column

vector kw is the k neuron’s corresponding weight vector of the output layer.

For the output layer, there is

∑=

==m

jjjkkkk ywnetnetfo

1

)( （1）

For the hidden layer, there is

∑=

==n

iiijjjj xvnetnetfy

1)( （2）

Above the two formulas, the transfer functions are all the Sigmoid function:

)1(1)( xexf −+=

（3）

The 1-3 formulas constitute a three layers feed forward mathematical model.

(2) BP algorithm

If the network output differs from the expected output, there must be an output error E :

∑ ∑ ∑= = =⎪⎩

⎪⎨⎧

⎪⎭

⎪⎬⎫⎥⎦

⎤⎢⎣

⎡⎟⎠

⎞⎜⎝

⎛−=l

k

m

j

n

iiijjkk xvfwfdE

1

2

1 121 （4）

It can be seen from the formula 4, the network input error is the function of each layer’s weight value jkw and ijv , adjusting relevance weights can change the error. Obviously the principle of adjusting relevance weights is decreasing the error. Therefore the weights’ adjustment is proportional to the error’s negative gradient, namely

jkjk w

Ew∂∂−=Δ η

mj ,2,1,0 …= ； lk ,,2,1 …=

（5a）

ijij v

Ev∂∂−=Δ η ni ,2,1,0 …= ； mj ,,2,1 …=

（5b） Among the formula, the minus sign expresses the gradient

descent, the )1,0(∈η often means the scale coefficient, and reflects the learning rate during the training.

As for the Sigmoid function, three layers of BP network weights to adjust formula is:

jkkkkjk yooodw )1()( −−=Δ η (6a)

ijj

l

kjk

okij xyywv )1()(

1

−=Δ ∑=

δη (6b)

Three layers feed forward network BP algorithm can also be written as vector form, for the output layer and the hidden layer, respectively：

( )Tmj yyyyyY ,,,,,, 210= ,

( )Tol

ok

oooo δδδδδδ ,,,,,, 321=

( )Tni xxxxxX ,,,,,, 210= ,

( )Tym

yj

yyyy δδδδδδ ,,,,,, 321=

Then, ( )TToYW δη=Δ ， ( )TTy XV δη=Δ （7）

B. Based on BP neural network establish project risk comprehensive evaluation model Construction project risk evaluation’s BP neural network

model takes the risk assessment’s influence factors as the BP network input, and the comprehensive evaluation’s result as the BP network output. By utilizing the BP network’s learning ability, we can obtain the comprehensive evaluation result from the seemingly random risk evaluation factor datas, which may provide the basis for the risk manager’s risk decision. The project risk comprehensive evaluation model, which is based on neural network, has three sections: input pretreatment module, neural network module and output module, whose core is the neural network module.

The input pretreatment module mainly pretreats the input datas, changes the qualitative things into the quantitative ones, and convenient for the neural network’s calculation. The output module changes the output of neural network into the data of estimation what we need.

(1) BP neural network structure

BP neural network structure is a multi-layer structure, which is made up of input layer、hidden layer and output layer. Each layer adopts a whole mutual connection, but the cells of same layer has no connection. Network performance is closely related with the training sample, a good training sample should pay attention to it’s size and quality.

Firstly determine the training samples. The required samples quantity of network training depends on the complexity of input and output nonlinear mapping relationship. The more complex the mapping relationship, the more required

samples to guarantee the accuracy. In addition, when the samples number reaches to a certain extent, the network precision is difficult to improve.

Secondly select and organize the samples. The extracted law of network training contains in samples, so the samples must be typical. Selecting the training samples must pay attention to the equilibrium of sample category, and try to make every category sample quantity roughly equal. Organizing the sample should pay attention to make the different types of samples crossing. If the same category sample is too centralized, when network training, it will only tend to build the mapping relation of matching with concentrative sample category.

(2)Select the model parameter

Firstly select the number of network hidden layer. Three layers network could meet any requirement. In addition, the more hidden layers, the bigger network structure, the longger learning and training time. So this paper selects one hidden layer.

Secondly determine the parameters of BP network model. According to Ke Ermogeluofu’s theorem, the number of implication units is 2m+l, m is the number of input data.

Thirdly determine the initial weights value. Commonly, choose a random number as the initial weights value, whose original weight is between（-1，1）.

Fourthly select the node functions. BP algorithm can transform the error correction opposite to hidden nodes. Sigmoid function is always be adopted, its advantage is that,

any data inputting can be transformed into the numbers between 0 and +1.

IV. APPLICATION OF RISK EVALUATION MODEL BASED ON BP NEURAL NETWORK

This paper collects 15 groups successful cases of project risk evaluation. Take the eleven risk assessment indexes as the network input cells, such as administrative intervention risk, policies and regulations risk, weather conditions risk, seismic risk, interest rate risk, prices rising risk, feasibility research risk, design and construction risk, project target control risk, business activities risk, which is expressed by 111 ~ II ; The risk grade of each risk factor is obtained from the previous steps by experts, and the system’s risk assessment value is gained from the successful samples. Then establish enough samples, including the training samples and the test samples. In Table 5, the top ten data are the training sample, the last five ones are the test sample. MATLAB neural network toolbox can easily realize neural network training. Neural network units loads the historical data of each risk factor in accordance with Table 5, and trains the network weights, gets the weight and threshold value between the input layer and the hidden layer, the hidden layer and the output layer. After training, we can take the existing experts’ evaluation on each risk factor as the input, and gain the whole system risk assessment value. If the system risk assessment value R less than 0.4, the project is a low risk project; if R more than 0.4 and less than 0.7, the project is a moderate one; if R more than 0.7, the project is a high-risk. It can be seen from Table 5, the last five test samples’ output value agreed with the practical value.

TABLE V. .NEURAL NETWORK TRAINING SAMPLES

Seiral number I1 I2 I3 I4 I5 I6 I7 I8 I9 I10 I11 Actual evaluation System evaluation 1 0.48 0.52 0.42 0.18 0.53 0.72 0.72 0.65 0.39 0.44 0.47 0.59 2 0.54 0.49 0.54 0.18 0.53 0.57 0.42 0.46 0.35 0.48 0.55 0.50 3 0.54 0.73 0.75 0.33 0.53 0.72 0.69 0.42 0.64 0.51 0.60 0.67 4 0.27 0.36 0.43 0.12 0.33 0.57 0.44 0.22 0.19 0.24 0.26 0.30 5 0.39 0.32 0.42 0.12 0.21 0.17 0.33 0.22 0.29 0.29 0.32 0.27 6 0.51 0.41 0.56 0.31 0.48 0.49 0.51 0.45 0.33 0.46 0.53 0.43 7 0.62 0.82 0.75 0.34 0.69 0.72 0.75 0.59 0.70 0.68 0.65 0.72 8 0.37 0.34 0.55 0.22 0.34 0.53 0.36 0.47 0.49 0.41 0.55 0.44 9 0.28 0.24 0.25 0.10 0.31 0.18 0.22 0.24 0.20 0.20 0.15 0.21 10 0.33 0.35 0.74 0.21 0.49 0.62 0.49 0.46 0.36 0.30 0.42 0.39 11 0.34 0.33 0.55 0.32 0.33 0.52 0.32 0.42 0.43 0.40 0.56 0.47 0.45 12 0.36 0.36 0.59 0.16 0.40 0.55 0.43 0.49 0.56 0.44 0.57 0.51 0.54 13 0.75 0.68 0.84 0.20 0.88 0.76 0.68 0.58 0.78 0.69 0.67 0.74 0.72 14 0.25 0.24 0.31 0.15 24 0.34 0.27 0.33 0.51 0.2 0.44 0.31 0.28 15 0.32 0.38 0.58 0.20 0.30 0.52 0.34 0.49 0.46 0.40 0.52 0.42 0.43

V. CONCLUSION Utilizing BP neural network project risk evaluation model

is established, although the network structure is difficult to confirm, as long as the training data has representativeness, repeatedly training can always get the good simulation result. And the BP neural network method is a kind of non-linear method, with no obvious subjective element and man-made factor, just input the processed data into the network, through MATLAB neural network calculation box can get the evaluation result, avoid the subjectivity and the simplicity, make the evaluation result more effective and more objective.

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