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.

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.

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.

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.

Application of Comprehensive Evaluation Method to Foundation Pit Engineering

2012 ◽  
Vol 446-449 ◽  
pp. 1775-1780
Author(s):  
Jian Xin Zhang ◽  
Jia Li Dai ◽  
Bin Liu
Keyword(s):  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Fuzzy Synthetic Evaluation ◽  
Deep Foundation ◽  
Foundation Pit ◽  
Safety Risk ◽  
Deep Foundation Pit ◽  
Comprehensive Evaluation Method ◽  
Risk Condition

Based on the monitoring data of the deep foundation pit, established an adjacent deep foundation pit safety risk fuzzy synthetic evaluation model, and then determine safety grades , using the method to evaluate the safety of the environment surrounding the foundation pit. Practice proves that the method can reflect the risk condition of the pit, and provided a new way to check the security of the pit.

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.

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