Applied Technology in Optimization Design of Pile-Anchor Support for Foundation Pit Based on BP Neural Network

2014 ◽  
Vol 910 ◽  
pp. 419-424
Author(s):  
Dong Wei Cao ◽  
Lu De Zou
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Optimization Design ◽  
Optimization Method ◽  
Engineering Optimization ◽  
Constraint Condition ◽  
Foundation Pit ◽  
Random Samples ◽  
Applied Technology

A new optimization method of pile-anchor support for foundation pit based on BP neural network was been proposed and applied in engineering example. Uniform test can be used to construct study samples efficiently. BP neural network is taken advantage to build a prediction model and predicting results of large number of random samples. Then, according to the constraint condition of optimization criterions, the best optimization result screened out from results. Through an engineering optimization example, it is showed that this method is efficient and with good economic and practical value.

Application of BP Neural Network to Risk Analysis of Deep Foundation Pit

2014 ◽  
Vol 556-562 ◽  
pp. 5989-5993
Author(s):  
Lu De Zou ◽  
Dong Wei Cao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Lateral Displacement ◽  
Uniform Design ◽  
Probability Density Distribution ◽  
Soil Parameters ◽  
Deep Foundation ◽  
Foundation Pit ◽  
Deep Foundation Pit ◽  
Random Samples

there are many uncertainty factors in the design process of the deep foundation pit engineering, such as the soil parameters, loading, which make the calculated displacement, settlement and safety factor have randomness and uncertainty. This paper combines uniform design (UD) with BP neural network. The UD structures random samples. Then, BP neural network trains random samples and the corresponding lateral displacement, settlement of ground and safety factors to get response relationship respectively. On this basis, the probability density distribution of each response parameter is obtained by predicting a large number of samples obtained by the Monte Carlo simulation. And then the Breadth Border Method, Narrow Bounds Method and PNET method are used to calculate system failure probability of foundation pit. The instance analysis shows that the method has high computing efficiency and the result is reasonable. It provides an effective way for the reliability analysis of the foundation pit engineering.

Time Series Forecast of Foundation Pit Deformation Based on BP Neural Network

2014 ◽  
Vol 556-562 ◽  
pp. 5979-5983 ◽  
Author(s):  
Jing Cao ◽  
Wen Yun Ding ◽  
Dang Shu Zhao ◽  
Hai Ming Liu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Measured Data ◽  
Engineering Optimization ◽  
Forecast Method ◽  
Foundation Pit ◽  
Time Series Forecast ◽  
Excavation Process

Combined with the advantage of BP neural network, a time series forecast method of foundation pit deformation based on BP neural network is proposed. According to the excavation process of foundation pit, the deformation forecast model is built by analyzing the measured data of early working stage. Then, the model is used to forecast the deformation of later working stage. Through an engineering optimization example, it is showed that this method is not only efficient, but also with good economic and practical value.

A Systematic Optimization Design Method for Thermal Management of Passenger Vehicles

2021 ◽  
pp. 1-27
Author(s):  
Jie Zhang ◽  
Qidong Wang ◽  
Han Zhang ◽  
Min Zhang ◽  
Jianwei Lin
Keyword(s):  
Prediction Model ◽  
Surface Temperature ◽  
Thermal Management ◽  
Optimization Design ◽  
Cfd Simulation ◽  
Optimal Solution ◽  
Optimization Method ◽  
Drive Shaft ◽  
Management Problem ◽  
Systematic Optimization

Abstract In this study, a systematic optimization method for the thermal management problem of passenger vehicle was proposed. This article addressed the problem of the drive shaft sheath surface temperature exceeded allowable value. Initially, the causes and initial measures of the thermal problem were studied through computational fluid dynamics (CFD) simulation. Furthermore, the key measures and the relevant parameters were determined through Taguchi method and significance analysis. A prediction model between the parameters and optimization objective was built by radial basis function neural network (RBFNN). Finally, the prediction model and particle swarm optimization (PSO) algorithm were combined to calculate the optimal solution, and the optimal solution was selected for simulation and experiment verification. Experiment results indicated that this method reduced the drive shaft sheath surface temperature promptly, the decreasing amplitude was 22%, which was met the experimental requirements.

Research on Stamping Spring-Back Prediction for Car Body Panel Based on BP Neural Network

2010 ◽  
Vol 97-101 ◽  
pp. 250-254 ◽  
Author(s):  
Xin Jian Zhou
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Front Panel ◽  
Spring Back ◽  
Test Analysis ◽  
Car Body ◽  
Neural Network Prediction ◽  
Mould Design ◽  
Bp Neural Network Model

On the basis of orthogonal test analysis of variance, BP neural network is used to forecast quantitatively the stamping spring-back of front panel of a car body, namely the engine hood, under the conditions of different stamping parameters. Firstly, BP neural network prediction model is established and sample training is done in Matlab. Then, the spring-back prediction using BP neural network and the result of spring-back simulation using Dynaform is compared to verify the precision and stability of the prediction model. Lastly, modification is made to the BP neural network according to practical stamping parameters and an efficient BP neural network model is established. Using this model, stamping spring-back prediction for the front panel of a car body is made. The spring-back prediction could then be used for spring-back compensation in the mould design of the front panel.

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