Safety evaluation research of hydraulic steel gate based on BP-neural network

2009 ◽  
Author(s):  
Guo Jianbin ◽  
Wen Yuanchang ◽  
Xiao Jian
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation Research ◽  
Safety Evaluation ◽  
Steel Gate

scholarly journals An Evaluation Model for Tailings Storage Facilities Using Improved Neural Networks and Fuzzy Mathematics

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Sen Tian ◽  
Jianhong Chen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation Model ◽  
Back Propagation ◽  
Safety Evaluation ◽  
Evaluation Index System ◽  
Analytic Hierarchy ◽  
Operation Rate ◽  
Network Methods ◽  
Neural Network Algorithm

With the development of mine industry, tailings storage facility (TSF), as the important facility of mining, has attracted increasing attention for its safety problems. However, the problems of low accuracy and slow operation rate often occur in current TSF safety evaluation models. This paper establishes a reasonable TSF safety evaluation index system and puts forward a new TSF safety evaluation model by combining the theories for the analytic hierarchy process (AHP) and improved back-propagation (BP) neural network algorithm. The varying proportions of cross validation were calculated, demonstrating that this method has better evaluation performance with higher learning efficiency and faster convergence speed and avoids the oscillation in the training process in traditional BP neural network method and other primary neural network methods. The entire analysis shows the combination of the two methods increases the accuracy and reliability of the safety evaluation, and it can be well applied in the TSF safety evaluation.

Study on Thermal Power Plant Safety Evaluation Based on BP Neural Network

2014 ◽  
Vol 687-691 ◽  
pp. 2083-2086
Author(s):  
Chao Wang ◽  
Ying Jie Lian
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Power Plant ◽  
Bp Neural Network ◽  
Thermal Power ◽  
Safety Evaluation ◽  
Electric Power Industry ◽  
Plant Production ◽  
Plant Safety ◽  
Safety Production

Electric power industry is a basic industry of national economy, the power plant production safety related to people's life safety and property of the state, the power of reform and social stability, safety evaluation of power generation enterprises is an important guarantee of safety production in power generation enterprises.The paper establishes the BP neural network model, utilize BP neural network optimization ability and good fitting ability, combining the index system build, carries on the appraisal to the power generation enterprise security.Now the instance verification results show that BP neural network is applied in safety evaluation of power generation enterprises, not only can accurately evaluate the safety situation of power generation enterprises, and the speed of convergence process is quickly.

Studies on Damage Distinguishing of Underground Tunnel Structure Based on BP Neural Network

2014 ◽  
Vol 580-583 ◽  
pp. 1382-1387
Author(s):  
Xi Zuo ◽  
Guo Xing Chen ◽  
Wei Qian Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Damage Identification ◽  
Underground Structure ◽  
Safety Evaluation ◽  
Identification Problem ◽  
Underground Engineering ◽  
Change Rate ◽  
Engineering Structure ◽  
Damage Degree

With the expansion and development of scale of construction on metro engineering, the damage diagnosis and the safety evaluation on underground engineering structure have become vital problems to be solved. This paper raised an idea to distinguish underground engineering structure based on BP neural network: define change rate of curvature of structure, and recognize it as the input scalar of BP neural network, using a reducing unit elastic modulus method to simulate damage location and damage degree, through various set of underground structure extent of damage, recognize the first four order curvature structure change rate as input of BP neural network. The results show that the method using BP neural network can identify the damage degree of underground engineering structure accurately and can solve the damage identification problem of underground engineering structure conveniently and effectively.

Safety Assessment of the General Overhead Traveling Crane Metal Structure Based on BP Neural Network

2011 ◽  
Vol 101-102 ◽  
pp. 15-20 ◽  
Author(s):  
Ge Ning Xu ◽  
Qian Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation System ◽  
Safety Assessment ◽  
Safety Evaluation ◽  
Metal Structure ◽  
Assessment Model ◽  
Security Model ◽  
Network Training ◽  
Training Samples

Safety assessment of bridge crane metal structure is widely needed. A general bridge safety assessment model of metal structure based on BP neural network is established. BP neural network is suitable for the problem that is not fully known and the adaptability of the dynamic system, and can facilitate the assignment and statistics of the safety evaluation system. Matlab7.0 software is used for the network training process. Through the training, samples to be tested were verified for the feasibility of the security model. The security model based on BP neural network for the general overhead traveling crane structure could provide a safety assessment and evaluation methods.

Research on the Safety Evaluation of Large Recreation Facilities Based on BP Neural Network

2014 ◽  
Vol 522-524 ◽  
pp. 881-886
Author(s):  
Yu Zhang ◽  
Zhi Rong Wang ◽  
Qing Qing Zuo ◽  
Xin Dong Zhang ◽  
Xiang Dong Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Human Factor ◽  
Evaluation Model ◽  
Safety Evaluation ◽  
Qualitative Evaluation ◽  
Evaluation Index System ◽  
Model Based ◽  
Network Training ◽  
Recreation Facilities

A safety evaluation index system regarding to the current safety situation of large recreation facilities in China is established. 13 secondary standard items are built by considerring human factor, equipment factor, environment factor and management factor. The existing safety evaluation of large recreation facilities are conducted by qualitative evaluation methods with highly fuzziness. The evaluation results are uncertain. After the network training, a safety evaluation model based on BP neural network is built. It can reduce the subjectivity of qualitative evaluation effectively with more scientific and objective results. Through the model based on BP neural network, the present safety situation of one large amusement facility is evaluated. The evaluation result is consistent with the actual situation. The method based on BP neural network in the paper provides a new method for safety evaluation of large recreation facilities.

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