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.

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.

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 Deformation prediction analysis of vertical displacement of deep foundation pit based on LIBSVM

2020 ◽  
Vol 206 ◽  
pp. 01021
Author(s):  
Yueyao Zhao ◽  
Jiawei Zhang ◽  
Haojie Li
Keyword(s):  
Support Vector Machine Model ◽  
Hot Spot ◽  
Vertical Displacement ◽  
Support Vector ◽  
Deep Foundation ◽  
Foundation Pit ◽  
Machine Model ◽  
Deformation Prediction ◽  
Deep Foundation Pit ◽  
Good Agreement

The reliable prediction of the surface vertical displacement deformation of deep foundation pits is of great significance to the excavation of large foundation pits. The support vector machine model (LIBSVM) has become a hot spot in the prediction of deep foundation pit deformation and provides a new prediction for the deformation of deep foundation pits. In this paper, taking the deep foundation pit of Daoxianghu Road Station in xx as an example, a prediction model of vertical displacement on the ground is established based on LIBSVM and analysis shows that the prediction results based on the model are in good agreement with the measured data, and the MSE reaches 0.0323. The model is effective and has an effective prospective skill.

scholarly journals Safety Management System Prototype/Framework of Deep Foundation Pit Based on BIM and IoT

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Wenhan Fan ◽  
Jianliang Zhou ◽  
Jianming Zhou ◽  
Dandan Liu ◽  
Wenjing Shen ◽  
...  
Keyword(s):  
Management System ◽  
Safety Management ◽  
Deformation Monitoring ◽  
Structural Deformation ◽  
Underground Construction ◽  
Construction Sites ◽  
Deep Foundation ◽  
Foundation Pit ◽  
Safety Management System ◽  
Deep Foundation Pit

With the huge demand for building underground spaces, deep foundation pits are becoming more and more common in underground construction. Due to the serious effects associated with accidents that occur in deep foundation pits, it is very important for underground construction safety management to be proactive, targeted, and effective. This research develops a conceptual framework adopting BIM and IoT to aid the identification and evaluation of hazards in deep foundation pit construction sites using an automated early warning system. Based on the accident analysis, the system framework of Safety Management System of Deep Foundation Pits (SMSoDFP) is proposed; it includes a function requirement, system modules, and information needs. Further, the implementation principles are studied; they cover hazardous areas, namely, visualization, personnel position monitoring, structural deformation monitoring, and automatic warning. Finally, a case study is used to demonstrate the effectiveness and feasibility of the system proposed. This research provides suggestions for on-site management and information integration of deep foundation pits, with a view to improving the safety management efficiency of construction sites and reducing accidents.

scholarly journals Numerical Simulation of Deep Foundation Pit Construction under Complex Site Conditions

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Huifen Liu ◽  
Kezeng Li ◽  
Jianqiang Wang ◽  
Chunxiang Cheng
Keyword(s):  
Numerical Simulation ◽  
Ground Surface ◽  
Reference Value ◽  
Horizontal Displacement ◽  
Deformation Monitoring ◽  
Deep Foundation ◽  
Foundation Pit ◽  
Deep Foundation Pit ◽  
Ground Surface Settlement ◽  
Stress And Deformation

Based on the deep foundation pit project of Laoguancun station of Wuhan rail transit line 16 and according to the engineering characteristics of the construction conditions and the site surrounding the environment, the method of combining field monitoring and finite element numerical simulation is adopted to analyze the law of stress and deformation of the deep foundation pit during excavation and support construction; it includes the horizontal displacement of the underground diaphragm wall, supporting axial force, and the ground surface settlement, which can be compared with measured data. Finally, some suggestions for monitoring and construction of the deep foundation pit in the subway station have been put forward and have certain reference value and practical guiding significance for the design and construction of similar engineering projects. The deformation monitoring of the retaining structure at the middle of the long side of the foundation pit should be strengthened during the construction process.

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