Deformation Prediction Method of Deep Foundation Pit Support Structure Based On GA-BP

Wen, Jiaming; Guo, Ruixia

doi:10.1109/ichceswidr54323.2021.9656371

2021 7th International Conference on Hydraulic and Civil Engineering &Amp; Smart Water Conservancy and Intelligent Disaster Red 2021

DOI: 10.1109/ichceswidr54323.2021.9656371

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Deformation Prediction Method of Deep Foundation Pit Support Structure Based On GA-BP

Jiaming Wen

Ruixia Guo

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Cited by 2 publications

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A spatiotemporal correlation and attention-based model for pipeline deformation prediction in foundation pit engineering

Chen,

Yuan,

et al. 2024

Sci Rep

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In foundation pit engineering, the deformation prediction of adjacent pipelines is crucial for construction safety. Existing approaches depend on constitutive models, grey correlation prediction, or traditional feedforward neural networks. Due to the complex hydrological and geological conditions, as well as the nonstationary and nonlinear characteristics of monitoring data, this problem remains a challenge. By formulating the deformation of monitoring points as multivariate time series, a deep learning-based prediction model is proposed, which utilizes the convolutional neural network to extract the spatial dependencies among various monitoring points, and leverages the bi-directional long-short memory unit network to extract temporal features. Notably, an attention mechanism is introduced to adjust the trainable weights of spatial-temporal features extracted in the prediction. The evaluation of a real-world subway project demonstrates that the proposed model has advantages compared with current models, particularly in long-term prediction. It improves the Adjusted R2 index averagely by from 19.4 to 61.6 compared with existing models. The proposed model also exhibits a decrease in mean absolute error ranging from 51.5 to 70.3 compared to others. Experiments and analyses verify that the spatial-temporal dependencies in time series and the attention learning for spatial-temporal features can improve the prediction of such engineering problems.

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A spatiotemporal correlation and attention-based model for pipeline deformation prediction in foundation pit engineering

Chen,

Yuan,

et al. 2024

Sci Rep

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Intelligent Prediction Model (IPM) of Foundation Pit Displacement Based on Extreme Learning Machine (ELM) and Its Application

et al. 2022

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In order to effectively predict the dynamic displacement and disaster, according to the analysis of the influencing parameters affecting the deformation of a subway foundation pit supported by piles (walls), the rough set attribute reduction method (RSARM) and the average influence value algorithm (AIVA) are used to simplify the influencing factors of foundation pit deformation. Those simplified factors are taken as the input of the ELM, with the output being the displacement of the foundation pit. Finally, the IPM of foundation pit displacement derived from the ELM is obtained, which is finally used for engineering practice. The results show that it is feasible to simplify the influencing factors of the deformation of the foundation pit by RSARM and AIVA. The proposed IPM of foundation pit displacement has high accuracy and good generalization ability, which can be used for the deformation prediction.

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Deformation Prediction Method of Deep Foundation Pit Support Structure Based On GA-BP

Cited by 2 publications

References 9 publications

A spatiotemporal correlation and attention-based model for pipeline deformation prediction in foundation pit engineering

A spatiotemporal correlation and attention-based model for pipeline deformation prediction in foundation pit engineering

Intelligent Prediction Model (IPM) of Foundation Pit Displacement Based on Extreme Learning Machine (ELM) and Its Application

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