Study on settlement prediction model of deep foundation pit in sand and pebble strata based on grey theory and BP neural network

2020 ◽  
Vol 13 (23) ◽  
Author(s):  
Yan Lv ◽  
Tingting Liu ◽  
Jing Ma ◽  
Shengda Wei ◽  
Chengliang Gao
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Grey Theory ◽  
Deep Foundation ◽  
Foundation Pit ◽  
Deep Foundation Pit ◽  
Settlement Prediction

Application of BP-ARMA Combined Model Based on Entropy Method in the Prediction of Circle Beam Displacement of Foundation Pit

2014 ◽  
Vol 697 ◽  
pp. 530-534
Author(s):  
Yu Bo Hu ◽  
Fei Shao ◽  
Ya Xin Huang ◽  
Ya Wen Liu ◽  
Jin Jun Liang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Bp Neural Network ◽  
Entropy Method ◽  
Deep Foundation ◽  
Combined Model ◽  
Foundation Pit ◽  
Deep Foundation Pit ◽  
Supporting Structure ◽  
Beam Displacement

The prediction of deformation of foundation pit’s supporting structure is the basis of construction control of deep foundation pit. Meanwhile, it is vital to the safe excavation of foundation pit. In the work, the 1st project of Huaqiao in Jiantao Square of Kunshan City is chosen. Besides, model of combination based on entropy method is built to predict the displacement of circle beam with BP neural network and ARMA time series model. Finally, the analysis shows that combination models improve overall prediction on the premise of better predicting accuracy. Thus, it is of 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.

scholarly journals Deformation Prediction of a Deep Foundation Pit Based on the Combination Model of Wavelet Transform and Gray BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Qiang Liu ◽  
Chun-Yan Yang ◽  
Li Lin
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Bp Neural Network ◽  
Deformation Monitoring ◽  
Deep Foundation ◽  
Foundation Pit ◽  
Deformation Prediction ◽  
Deep Foundation Pit ◽  
Combination Model ◽  
Bp Neural Network Model

The purpose of this study was to predict the deformation of a deep foundation pit based on a combination model of wavelet transform and gray BP neural network. Using a case of a deep foundation pit, a combination model of wavelet transform and gray BP neural network was used to predict the deformation of the deep foundation pit. The results show that compared with the traditional gray BP neural network model, the relative error of the combination model of wavelet transform and gray BP neural network was reduced by 2.38%. This verified that the combined model has high accuracy and reliability in the prediction of foundation pit deformation and also conforms to the actual situation of the project. The research results can provide a valuable reference for foundation pit deformation monitoring.

Research of the Underground Water Level Prediction Model in Deep Foundation Pit Engineering

2014 ◽  
Vol 675-677 ◽  
pp. 901-904
Author(s):  
Hao Peng Li
Keyword(s):  
Neural Network ◽  
Water Level ◽  
Bp Neural Network ◽  
Underground Water ◽  
Deep Foundation ◽  
Foundation Pit ◽  
Related Factors ◽  
Deep Foundation Pit ◽  
Underground Water Level ◽  
Sample Set

The effect such as ion exchange, precipitation, corrosion and consolidation can occur between groundwater and rock mass, it will cause a variety of adverse effects on deep foundation pit engineering. Prediction of the underground water level and take corresponding precipitation control measures is very important. Underground water level deformation is a complicated ,nonlinear and stochastic problem, it is unable to establish accurate mathematical model. An underground water level deformation prediction model based on BP neural network was constructed in this paper. Five closely related factors in underground water level deformation are river flow, temperature, saturation deficit, rainfall and evaporation, they were selected as input vector of BP neural network, underground water level measured value as a model target output. In Matlab 2011b simulation software, 24 groups observation data for underground water level and five closely related factors of a underground parking lot deep foundation pit engineering in Jilin as the sample set,19 groups were randomly selected as the training sample set , other 5 groups were used as the testing sample set .The simulation result shows that testing value is very close to the true value in this method and the average relative error was 2.9708%.The method in this paper can achieve higher accuracy of groundwater level prediction in deep foundation pit engineering.

scholarly journals Research on Prediction of Ground Settlement of Deep Foundation Pit Based on Improved PSO-BP Neural Network

2021 ◽  
Vol 276 ◽  
pp. 01014
Author(s):  
Liu Yuhao ◽  
Feng Xiao
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Deep Foundation ◽  
Foundation Pit ◽  
Swarm Optimization ◽  
Improved Pso

In view of the limitations of the existing prediction methods for ground subsidence of deep foundation pit, a BP neural network prediction model based on improved particle swarm optimization algorithm was proposed. The mutation and crossover of genetic algorithm are integrated into particle swarm optimization algorithm, which makes full use of the global characteristics of genetic algorithm and the fast convergence speed of particle swarm optimization algorithm. In order to reduce the network output error, improve the convergence speed and enhance the network generalization ability, the final value of the optimized particle iteration was selected as the connection weight and threshold of the BP neural network. The results show that the RMSE, MAPE and R2 of the improved PSO-BP model are 0.3077, 0.7506% and 0.8811, so the improved PSO-BP model has a better prediction accuracy.

scholarly journals Deformation Detection Model of High-Rise Building Foundation Pit Support Structure Based on Neural Network and Wireless Communication

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Diandian Ding
Keyword(s):  
Neural Network ◽  
Wireless Communication ◽  
Construction Safety ◽  
Risk Identification ◽  
Identification System ◽  
Optimized Design ◽  
Deep Foundation ◽  
Foundation Pit ◽  
Safety Risk ◽  
Deep Foundation Pit

The reasonable selection and optimized design of the deep foundation pit support scheme is directly related to the safety, construction period, and cost of the entire project. Here, based on a large number of theoretical results in many related fields, relevant influencing factors are systematically analyzed, and advanced mathematical algorithms such as neural networks are introduced according to the relevant characteristics of building deep foundation pit support construction. First of all, this paper designs and implements deep foundation pit construction safety risk technology based on wireless communication and BIM technology and analyzes and describes the framework and function of the foundation pit construction safety risk identification system. Secondly, we use neural network algorithms to study the deformation prediction of the foundation pit supporting structure, which can describe the expression method of the above safety knowledge. Finally, the differences and benefits of this method and traditional methods are compared through experiments, which show that this technology can pave the way for the construction of deep foundation pit construction safety risk knowledge.

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