Fire Risk Evaluation Model of High-Rise Buildings Based on Multilevel BP Neural Network

2007 ◽  
Author(s):  
Dengyou Xia
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Risk Evaluation ◽  
Evaluation Model ◽  
Fire Risk ◽  
High Rise ◽  
Risk Evaluation Model

Power Grid Risk Evaluation Model of Empty Mine Mined-Out Area Based on BP Neural Network

2013 ◽  
Vol 756-759 ◽  
pp. 1710-1714
Author(s):  
Guo Feng Yang ◽  
Jia Kui Zhao ◽  
Ting Shun Li ◽  
Jing Zhou
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Risk Evaluation ◽  
Preventive Measures ◽  
Evaluation Model ◽  
Factors Associated ◽  
Bp Neural Network Model ◽  
Risk Evaluation Model

The risk evaluation of empty mine mined-out area is of great significance to the security and stability of the power facilities. But influence evaluation of empty mine mined-out area factor multitudinous, this paper selected seven factors associated with the system. Based on the principle of BP neural network, we build a 3 layer BP neural network model suitable for grid risk evaluation. The BP neural network model was trained by collected samples of empty mine mined-out area and the logical parameters of BP neural network were acquired and tested by the testing samples for accuracy, and finally we proposes preventive measures based on the evaluation.

The Risk Evaluation Model of Construction Project Contract Based on BP Neural Network

2013 ◽  
Vol 357-360 ◽  
pp. 2304-2307
Author(s):  
Hua Liu ◽  
Jun Fang Yang ◽  
Zhi Yuan Zhang
Keyword(s):  
Neural Network ◽  
Risk Management ◽  
Bp Neural Network ◽  
Risk Evaluation ◽  
Evaluation Model ◽  
Neural Network Method ◽  
Network Method ◽  
Indicator Model ◽  
Current Risk ◽  
Risk Evaluation Model

According to the current risk management of construction contract in China, put forward the idea of using neural network method to evaluate the risk in construction contract. Designed a comprehensive multi-indicator model for evaluating the construction contract risk based on BP Neural Network. This model can be used for simulating and evaluating the risk in construction contract in future. It has been proved that the desired results can be achieved by using this model.

scholarly journals Risk Evaluation Model of Highway Tunnel Portal Construction Based on BP Fuzzy Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Xianghui Deng ◽  
Tian Xu ◽  
Rui Wang
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Risk Assessment ◽  
Risk Evaluation ◽  
Risk Measures ◽  
Evaluation Model ◽  
Control Measures ◽  
Slope Instability ◽  
Tunnel Portal ◽  
Risk Evaluation Model

Risk assessment for tunnel portals in the construction stage has been widely recognized as one of the most critical phases in tunnel construction as it easily causes accident than the overall length of a tunnel. However, the risk in tunnel portal construction is complicated and uncertain which has made such a neural network very attractive to the construction projects. This paper presents a risk evaluation model, which is obtained from historical data of 50 tunnels, by combining the fuzzy method and BP neural network. The proposed model is used for the risk assessment of the Tiefodian tunnel. The results show that the risk evaluation level is IV, slope instability is the greatest impact index among four risk events, and the major risk factors are confirmed. According to the evaluation results, corresponding risk control measures are suggested and implemented. Finally, numerical simulation is carried out before and after the implementation of risk measures, respectively. The rationality of the proposed risk evaluation model is proved by comparing the numerical simulation results.

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