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.

Research on Deformation Forecasting of Deep Foundation Pit Supporting Structure Based on BP Neural Networks

2011 ◽  
Vol 250-253 ◽  
pp. 2116-2119
Author(s):  
Yi Xue ◽  
Lei Xu ◽  
Zheng Zheng Cao
Keyword(s):  
Neural Network ◽  
Network Theory ◽  
Network System ◽  
Deep Excavation ◽  
Deep Foundation ◽  
Foundation Pit ◽  
Deep Foundation Pit ◽  
Maximum Deformation ◽  
Supporting Structure ◽  
Neural Network Theory

Excavation engineering is affected by many kinds of factors. It is becoming the key and difficult point in geotechnical engineering. This paper analyzes and predicts the deformation of supporting structure in urban deep excavation with artificial neural network theory, establishing network predictive model to predict the maximum deformation of supporting structure. The result shows that the network system has high precision, and it can be applied to practice.

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.

scholarly journals Safety Prediction of Deep Foundation Pit Based on Neural Network and Entropy Fuzzy Evaluation

2021 ◽  
Vol 233 ◽  
pp. 03001
Author(s):  
Xie Xiudong ◽  
Pan Caizhen
Keyword(s):  
Neural Network ◽  
Evaluation Method ◽  
Fuzzy Comprehensive Evaluation ◽  
Development Trend ◽  
Entropy Method ◽  
Monitoring Data ◽  
Fuzzy Evaluation ◽  
Deep Foundation ◽  
Foundation Pit ◽  
Deep Foundation Pit

The monitoring data can effectively reflect the safety status of the project during the construction of deep foundation pit, and the risks existing in the project can be discovered in time and the development trend can be reasonably predicted through the processing and analysis of the existing monitoring data. In this paper, a deep foundation pit in compound soil area of a coastal city was taken as an example, the BP neural network was taken to predict the monitoring data in the next stage, the entropy method was utilized to determine the weight of the evaluation index according to the predicted value, and the fuzzy comprehensive evaluation method was used to quantitatively describe the future safety status, so as to formulate targeted countermeasures and improve the construction safety.

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.

Research of Controlling for Pile-Anchor Joint Supporting Structure on Deformation of Deep Foundation Pits

2011 ◽  
Vol 261-263 ◽  
pp. 1809-1813
Author(s):  
Jin Biao Chen ◽  
Qiang Hu ◽  
Yuan Wu Zhou
Keyword(s):  
Soil Properties ◽  
Deformation Mechanism ◽  
Geotechnical Engineering ◽  
Reliability Theory ◽  
Deep Foundation ◽  
Foundation Pit ◽  
Deep Foundation Pit ◽  
Supporting Structure ◽  
Foundation Deformation ◽  
The Stability

The excavation of deep foundation pit is very complex in the field of geotechnical engineering, how to control the deformation of deep foundation pit and protect the environment is of great significance. This paper analyzed the deformation mechanism of pile-anchor joint supporting structure in detail, established a model for deformation controlling based on the reliability theory, and then analyzed the sensitivities of prestressed, pile stiffness, spacing and soil properties to foundation deformation. Combined with an engineering example, this paper verifies the stability and effectiveness of the model for deformation controlling. This study will provide some reference to similar projects.

Monitoring and Analysis on the Supporting Structure for Deep Foundation Pit

2014 ◽  
Vol 580-583 ◽  
pp. 787-790
Author(s):  
Hai Xia Sun ◽  
Ke Zhang ◽  
Si Li Chen
Keyword(s):  
In Situ Monitoring ◽  
Monitoring Data ◽  
Deep Foundation ◽  
Foundation Pit ◽  
Metro Station ◽  
Deep Foundation Pit ◽  
Supporting Structure ◽  
Deformation Features ◽  
The Stability

This article mainly expounds the importance of in-situ monitoring on the construction process of deep foundation pit. Taking the deep foundation pit of some Shenyang metro station for example, the deformation features of the supporting structure and the internal and external of foundation pit is analyzed, according to the monitoring data of the fender pile displacement during the excavation of deep foundation pit. The conclusion is obtained that the timely and accurate in-situ monitoring information is necessary to guaranteeing construction safety. We should pay more attention to the excavation speed and exert the interior support timely during the excavation of foundation pit to avoid large deformation and danger. The analytical results of monitoring data shows that the whole stage of foundation pit excavation is stable and the fender pile with internal supports can guarantee the stability of foundation pit.

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