The Method Based on BP Neural Network of Pile Foundation Defect Detection

2014 ◽  
Vol 687-691 ◽  
pp. 952-956
Author(s):  
Le Ting Zhang ◽  
Ya Ping Wu ◽  
Ming Qiang Wei
Keyword(s):  
Neural Network ◽  
Model Test ◽  
Defect Detection ◽  
Bp Neural Network ◽  
Pile Foundation ◽  
Velocity Curve ◽  
Data Input ◽  
Waveform Data ◽  
Excitation Force ◽  
Axial Speed

This paper through indoor model test get the axial speed curve and waveform data when the pile top under transient excitation force. Use the method of reverberation ray matrix program to verify the correctness of the velocity curve. The data input to the BP neural network and identification data. It showed that the BP neural network can judge the defect type of pile foundation accurately.

A Method for Damage Detection Based on Correlation Characteristic of Acceleration Response

2013 ◽  
Vol 540 ◽  
pp. 87-98 ◽  
Author(s):  
Wei Ming Yan ◽  
Da Peng Gu ◽  
Yan Jiang Chen ◽  
Wei Ning Wang
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Model Test ◽  
Bp Neural Network ◽  
Detection Method ◽  
Damage Index ◽  
Fem Simulation ◽  
Acceleration Response ◽  
Correlation Characteristic ◽  
Experiment Verification

A damage detection method using BP neural network based on a novel damage index, the correlation characteristic of the acceleration response, is proposed, and is evaluated through the FEM simulation and experiment verification. On the basis of achievements in existence, the feasibility of using the correlation characteristic as damage index is validated theoretically. The damage detection for a simple-supported beam using the proposed method was FEM simulated. The results showed that the trained BP neural network can correctly detect the location and extent of damages in both single damage case and multi-damage case. A model test of a reinforced concrete simple-supported beam was performed to verify the validity and efficiency of the damage detection method. From the results of the model test, it is shown that the trained BP neural network can correctly locate the damage mostly detect the extent of damage. A conclusion is given that the novel damage detection method using the correlation characteristic of the acceleration response as damage index is feasible and efficient.

scholarly journals The Prediction of Pile Foundation Buried Depth Based on BP Neural Network Optimized by Quantum Particle Swarm Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Fei Yin ◽  
Yong Hao ◽  
Taoli Xiao ◽  
Yan Shao ◽  
Man Yuan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pile Foundation ◽  
Statistical Tests ◽  
Soil Layer ◽  
Safety Hazards ◽  
Bp Network ◽  
Potential Safety ◽  
Practical Engineering ◽  
Pile Length

Due to the fluctuation of the bearing stratum and the distinct properties of the soil layer, the buried depth of the pile foundation will differ from each other as well. In practical construction, since the designed pile length is not definitely consistent with the actual pile length, masses of piles will be required to be cut off or supplemented, resulting in huge cost waste and potential safety hazards. Accordingly, the prediction of pile foundation buried depth is of great significance in construction engineering. In this paper, a nonlinear model based on coordinates and buried depth of piles was established by the BP neural network to predict the samples to be evaluated, the consequence of which indicated that the BP neural network was easily trapped in local extreme value, and the error reached 31%. Afterwards, the QPSO algorithm was proposed to optimize the weights and thresholds of the BP network, which showed that the minimum error of QPSO-BP was merely 9.4% in predicting the depth of bearing stratum and 2.9% in predicting the buried depth of pile foundation. Besides, this paper compared QPSO-BP with three other robust models referred to as FWA-BP, PSO-BP, and BP by three statistical tests (RMSE, MAE, and MAPE). The accuracy of the QPSO-BP algorithm was the highest, which demonstrated the superiority of QPSO-BP in practical engineering.

Neural Network-Based Reliability Prediction of Free-Running Submarine Model Test System

2011 ◽  
Vol 109 ◽  
pp. 318-322
Author(s):  
Ou Liu ◽  
Wei Wang ◽  
Chun Hui Yang
Keyword(s):  
Neural Network ◽  
Model Test ◽  
Bp Neural Network ◽  
Test System ◽  
Reliability Prediction ◽  
Prediction Errors ◽  
Testing Stage ◽  
Free Running ◽  
Simulation Results

In response to the issue that FMECA and FTA analysis in the initial experimental and testing stage of free-running submarine model cannot satisfy the actual requirements, a method based on BP neural network is proposed to conduct reliability prediction of free-running submarine models. By inputting the established parameters of free-running submarine models (MTBF) as a BP neural network into neurons and by training the network, the purpose of predicting the reliability of free-running submarine model test system was fulfilled. The simulation results show that the method is effective, feasible and practically meaningful, producing smaller prediction errors.

Stitching defect detection and classification using wavelet transform and BP neural network

2009 ◽  
Vol 36 (2) ◽  
pp. 3845-3856 ◽  
Author(s):  
W.K. Wong ◽  
C.W.M. Yuen ◽  
D.D. Fan ◽  
L.K. Chan ◽  
E.H.K. Fung
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Defect Detection ◽  
Bp Neural Network

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

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