Article Contents
Article Contents

# Major project risk assessment method based on BP neural network

• * Corresponding author: Shenghan Zhou
The first author is supported by NSFC grant 71501007, 71672006 and 71332003
• In order to prevent risks in major projects, it is of great importance to accurately assess risks in advance. Therefore, in this paper, we propose a novel major project risk assessment method with the BP neural network model. Firstly, we propose an index system for major project risk assessment, which is made up of four parts: 1) Schedule risk, 2) Cost risk, 3) Quality risk, and 4) Resource risk. Secondly, we propose a hybrid BP neural network and particle swarm optimization (PSO) model to evaluate risks in major projects. Especially, major project risk assessment results are achieved from the output layers of the BP neural network which is optimized by the PSO algorithm. In our proposed hybrid model, the fitness for each particle is computed through an optimal function, and then the particle can improve its velocity for the next cycle by searching the optimal value. Furthermore, this process should be repeated when the end condition is satisfied. Finally, experimental results demonstrate that the proposed method is able to evaluate risk level of major projects with high accuracy.

Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.

 Citation:

• Figure 1.  Index system for major project risk assessment

Figure 2.  Framework of the BP neural network

Figure 3.  Calculation process of the BP neural network algorithm

Figure 4.  Strucutre of the BP neural network for major project risk assessment

Figure 5.  The trend of training error varying

Figure 6.  Risk assessment results for different major projects

Figure 7.  Error rates of risk assessment for different major projects

Table 1.  Testing data of the major project risk assessment problem

 S1 S2 S3 S4 S5 S6 S7 S8 A1 0.135 0.154 0.228 0.190 0.218 0.184 0.208 0.252 A2 0.145 0.216 0.195 0.221 0.140 0.287 0.210 0.243 A3 0.139 0.138 0.363 0.270 0.139 0.306 0.151 0.274 A4 0.427 0.520 0.599 0.582 0.633 0.618 0.535 0.587 A5 0.451 0.510 0.638 0.605 0.639 0.616 0.630 0.571 A6 0.156 0.210 0.493 0.167 0.305 0.393 0.219 0.289 A7 0.241 0.215 0.390 0.185 0.214 0.334 0.334 0.169 A8 0.371 0.319 0.343 0.208 0.179 0.397 0.380 0.356 A9 0.385 0.419 0.312 0.220 0.303 0.323 0.356 0.117 A10 0.250 0.325 0.383 0.259 0.249 0.366 0.258 0.269 A11 0.237 0.275 0.357 0.352 0.242 0.310 0.363 0.253 A12 0.339 0.349 0.325 0.333 0.321 0.328 0.309 0.329 A13 0.211 0.216 0.329 0.281 0.209 0.295 0.215 0.347 A14 0.341 0.379 0.307 0.330 0.280 0.332 0.247 0.374 A15 0.171 0.182 0.319 0.213 0.148 0.348 0.150 0.195 A16 0.122 0.139 0.577 0.483 0.128 0.481 0.372 0.474 A17 0.149 0.162 0.400 0.335 0.120 0.478 0.468 0.363 A18 0.164 0.225 0.320 0.284 0.212 0.351 0.332 0.398 A19 0.219 0.246 0.176 0.250 0.155 0.316 0.209 0.214 A20 0.124 0.225 0.239 0.135 0.130 0.309 0.209 0.302

Table 2.  Risk scores from experts?opinion

 Project S1 S2 S3 S4 S5 S6 S7 S8 Expert opinion 0.155 0.189 0.362 0.171 0.158 0.347 0.273 0.301

Table 3.  Parameters of the propose BP neural network model

 ID Parameter name Value 1 Number of hidden layer nodes 35 2 Transfer function type of hidden layer nodes logsig 3 Neuron excitation function of output layer purelin 4 Training function trainlm 5 Learning function learngdm 6 Maximum iteration number 550 7 Learning rate 0.00001 8 Momentum coefficient 0.94 9 Error rate of network training 0.0001
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Tables(3)