Multi-factor settlement prediction around foundation pit based on SSA-gradient descent model

Li, Zhengcai; Hu, Xinyu; Chen, Chun; Liu, Chenyang; Han, Yalu; Yu, Yuanfeng; Du, Lizhi

doi:10.1038/s41598-022-24232-3

Cited by 8 publications

(3 citation statements)

References 29 publications

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“…There are also studies that have made significant contributions by using optimization techniques to improve soil quality (Yuan et al, 2024a(Yuan et al, , 2024b, 2023 [20][21][22]). Additionally, some scholars have addressed surface settlement control within the design parameter control of foundation pit supporting structures [7,[23][24][25][26][27][28][29]. Surface settlement can significantly affect nearby structures and infrastructure, making its control a vital aspect of foundation pit design.…”

Section: Introductionmentioning

confidence: 99%

Research on the Optimal Design of Retaining Piles of a Wide Metro Tunnel Foundation Pit Based on Deformation Control

Zhong,

Wan,

Nie

et al. 2024

Buildings

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Engineers pay more and more attention to the economic benefits of foundation pit engineering. At present, the optimal design of the foundation pit supporting structure mainly focuses on strength and functional design, and there is no mature theoretical design method for deformation control. In this paper, a method for calculating the overall deformation of a foundation pit supporting structure based on the principle of minimum potential energy is proposed. Based on this method, the optimal design of the foundation pit of Guangzhou Baiyun District Comprehensive Transportation Hub Metro Station is realized. The deformation calculation results and optimization design scheme are validated by finite element numerical simulation and field monitoring data. The results show that the proposed theoretical algorithm predicts the pile deformation curves better than the finite element method, suggesting the proposed theoretical method is reasonable and the optimization scheme of the retaining pile is feasible. In the optimized design, the deformation of the foundation pit retaining pile is controlled by its push-back effect. The proposed deformation calculation method can realize the overall deformation calculation of the foundation pit supporting structure.

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Section: Introductionmentioning

confidence: 99%

Research on the Optimal Design of Retaining Piles of a Wide Metro Tunnel Foundation Pit Based on Deformation Control

Zhong,

Wan,

Nie

et al. 2024

Buildings

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show abstract

“…The BP model has strong abilities of information processing, self-learning, nonlinear mapping, error feedback adjustment, and fault tolerance 37 – 41 . However, BP model also has some disadvantages such as slow convergence speed, long training time, difficult in achieve the overall optimum, and its prediction performance greatly depends on the random selection of initial weights and thresholds 35 , 42 . And the BP model requires large quantity training data and the data need to be widely representative 34 , 43 .…”

Section: Introductionmentioning

confidence: 99%

ANN deformation prediction model for deep foundation pit with considering the influence of rainfall

Wei,

Cheng,

Chen

et al. 2023

Sci Rep

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Deep foundation pits involving complex soil–water-structure interactions are often at a high risk of failure under heavy rainfall. Predicted deformation is an important index for early risk warning. In the study, an ANN model is proposed based on the Wave Transform (WT), Copula method, Convolutional Neural Network (CNN) and Long Short-Term Memory Neural Network (LSTM). The total deformation was firstly decomposed into low and high frequency components with WT. The CNN and LSTM were then used for prediction of the two components with rolling training and prediction. The input variables of the CNN and LSTM were determined and optimized based on the correlations analysis of Copula method of the two components with different random variables, especially with the rainfall. And finally, the predicted total deformation was obtained by adding the two prediction components. A deep foundation pit in Chengdu, China was taken as a case study, of which the horizontal deformation curves at different measuring points shows three types of developed trend, as unstable, less stable, and stable types. The predictions of the deformations of different development types by the proposed ANN model show high accuracies with a few input variables and can accurately prompt risk warning in advance.

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“…Li and Qiu 22 proposed an SSA-ELM model for predicting open-pit slope displacement and found that the prediction accuracy was higher than that of the BPNN model. Li et al 23 analyzed and compared the optimizing effects of three optimization algorithms, SSA, GA, and PSO, combined with BPNN prediction, and found that SSA had a higher optimization efficiency and could more accurately predict ground subsidence caused by excavation pits. Whereas, due to the drawbacks in population initialization, position update strategy, etc., SSA has the problems of weak global search ability, slow convergence speed, and easy fall into local optimality.…”

Section: Introductionmentioning

confidence: 99%

A novel combined intelligent algorithm prediction model for the tunnel surface settlement

Wang,

Dai,

Jia

et al. 2023

Sci Rep

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To ensure the safety and stability of the shield tunnel construction process, the ground settlement induced by the shield construction needs to be effectively predicted. In this paper, a prediction method combining empirical mode decomposition (EMD), chaotic adaptive sparrow search algorithm (CASSA), and extreme learning machine (ELM) is proposed. First, the EMD is used to decompose the settlement sequence into trend vectors and fluctuation vectors to fully extract the effective information of the sequence; Second, the sparrow search algorithm is improved by introducing Cubic chaotic mapping to initialize the population and adaptive factor to optimize the searcher’s position formula, and the chaotic adaptive sparrow search algorithm is proposed; Finally, the CASSA-ELM prediction model is constructed by using CASSA to find the optimal values of weights and thresholds in the extreme learning machine. The fluctuation components and trend components decomposed by EMD are predicted one by one, and the prediction results are superimposed and reconstructed to obtain the predicted final settlement. Taking a shield interval in Jiangsu, China as an example, the meta-heuristic algorithm-optimized ELM model improves the prediction accuracy by 10.70% compared with the traditional ELM model. The combined EMD-CASSA-ELM prediction model can greatly improve the accuracy and speed of surface settlement prediction, and provide a new means for safety monitoring in shield tunnel construction. Intelligent prediction methods can predict surface subsidence more automatically and quickly, becoming a new development trend.

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Multi-factor settlement prediction around foundation pit based on SSA-gradient descent model

Cited by 8 publications

References 29 publications

Research on the Optimal Design of Retaining Piles of a Wide Metro Tunnel Foundation Pit Based on Deformation Control

Research on the Optimal Design of Retaining Piles of a Wide Metro Tunnel Foundation Pit Based on Deformation Control

ANN deformation prediction model for deep foundation pit with considering the influence of rainfall

A novel combined intelligent algorithm prediction model for the tunnel surface settlement

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