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.

Application of BP Neural Network in Structural Damage Diagnosis of Bridge behind Abutment

2016 ◽  
Vol 847 ◽  
pp. 440-444 ◽  
Author(s):  
Yu Hui Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Structural Damage ◽  
Damage Identification ◽  
Current Knowledge ◽  
Horizontal Displacement ◽  
Structural Deformation ◽  
Bp Network ◽  
Damage Location

BP neural network is introduced and applied to identify and diagnose both location and extent of bridge structural damage; static load tests and dynamic calculations are also made on bridge structural damage behind abutment. The key step of this method is to design a reasonably perfect BP network model. According to the current knowledge, three BP neural networks are designed with horizontal displacement rate and inherent frequency rate as damage identification indexes. The neural networks are used to identify the measurement of structure behind abutment and the calculation of damage location and extent, at the same time, they can also be used to compare and analyze the results. The test results show that: taking the two factors (static structural deformation rate and the change rate of natural frequency in dynamic response) as input vector, the BP neural network can accurately identify the damage location and extent, implying a promising perspective for future applications.

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.

scholarly journals Beam Structure Damage Identification Based on BP Neural Network and Support Vector Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Bo Yan ◽  
Yao Cui ◽  
Lin Zhang ◽  
Chao Zhang ◽  
Yongzhi Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Bp Neural Network ◽  
Damage Identification ◽  
Support Vector ◽  
Single Crack ◽  
Beam Structure ◽  
Strain Mode ◽  
Structure Damage ◽  
Model Based

It is not easy to find marine cracks of structures by directly manual testing. When the cracks of important components are extended under extreme offshore environment, the whole structure would lose efficacy, endanger the staff’s safety, and course a significant economic loss and marine environment pollution. Thus, early discovery of structure cracks is very important. In this paper, a beam structure damage identification model based on intelligent algorithm is firstly proposed to identify partial cracks in supported beams on ocean platform. In order to obtain the replacement mode and strain mode of the beams, the paper takes simple supported beam with single crack and double cracks as an example. The results show that the difference curves of strain mode change drastically only on the injured part and different degrees of injury would result in different mutation degrees of difference curve more or less. While the model based on support vector machine (SVM) and BP neural network can identify cracks of supported beam intelligently, the methods can discern injured degrees of sound condition, single crack, and double cracks. Furthermore, the two methods are compared. The results show that the two methods presented in the paper have a preferable identification precision and adaptation. And damage identification based on support vector machine (SVM) has smaller error results.

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.

scholarly journals Conventional Bridge Damage Identification Based on BP Neural Network

2020 ◽  
Vol 730 ◽  
pp. 012037
Author(s):  
Zeying Yang ◽  
Yalei Zhang ◽  
Yaping Wang ◽  
Ning Wang ◽  
Haina Cui ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Damage Identification ◽  
Bridge Damage

Application of Improved BPNN to Damage Detection of Composite Materials

2011 ◽  
Vol 467-469 ◽  
pp. 1097-1101
Author(s):  
Xiao Ma Dong
Keyword(s):  
Neural Network ◽  
Composite Materials ◽  
Damage Detection ◽  
Bp Neural Network ◽  
Dynamic Method ◽  
Damage Identification ◽  
Wavelet Series ◽  
Improved Algorithm

A dynamic method based on improved algorithm BP neural network for damage identification of composite materials was proposed. By using wavelet series, the features of signals were extracted and input to improved algorithm BP neural network for training the network and identifying the damages. Finally, the experiment results show that this proposed method can exactly identify the faults of composite materials.

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