Optimized LSTM based on improved whale algorithm for surface subsidence deformation prediction

Ju Wang; Leifeng Zhang; Sanqiang Yang; Shaoning Lian; Peng Wang; Lei Yu; Zhenyu Yang; Ju Wang; Leifeng Zhang; Sanqiang Yang; Shaoning Lian; Peng Wang; Lei Yu; Zhenyu Yang

doi:10.3934/era.2023174

Electronic Research Archive

2023, Volume 31, Issue 6: 3435-3452. doi: 10.3934/era.2023174

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Research article Special Issues

Optimized LSTM based on improved whale algorithm for surface subsidence deformation prediction

1.
Beijing Municipal Road and Bridge Co., LTD., Beijing, China
2.
Hebei Civil Engineering Monitoring and Evaluation Technology Innovation Center, College of Civil Engineering, Hebei University, Baoding, Hebei, China

Academic Editor: Hui Yao

Received: 07 March 2023 Revised: 11 April 2023 Accepted: 12 April 2023 Published: 19 April 2023

In order to effectively control and predict the settlement deformation of the surrounding ground surface caused by deep foundation excavation, the deep foundation pit project of Baoding City Automobile Technology Industrial Park is explored as an example. The initial population approach of the whale algorithm (WOA) is optimized using Cubic mapping, while the weights of the shrinkage envelope mechanism are adjusted to avoid the algorithm falling into local minima, the improved whale algorithm (IWOA) is proposed. Meanwhile, 10 benchmark test functions are selected to simulate the performance of IWOA, and the advantages of IWOA in learning efficiency and convergence speed are verified. The IWOA-LSTM deep foundation excavation deformation prediction model is established by optimizing the input weights and hidden layer thresholds in the deep long short-term memory (LSTM) neural network using the improved whale algorithm. The IWOA-LSTM prediction model is compared with LSTM, WOA-optimized LSTM (WOA-LSTM) and traditional machine learning, the results show that the final prediction score of the IWOA-LSTM prediction model is higher than the score of other models, and the prediction accuracy is better than that of traditional machine learning.
- deep foundation pit,
- deep learning,
- whale optimization algorithm,
- numerical simulation,
- short and long term memory,
- settlement prediction
Citation: Ju Wang, Leifeng Zhang, Sanqiang Yang, Shaoning Lian, Peng Wang, Lei Yu, Zhenyu Yang. Optimized LSTM based on improved whale algorithm for surface subsidence deformation prediction[J]. Electronic Research Archive, 2023, 31(6): 3435-3452. doi: 10.3934/era.2023174

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Abstract

In order to effectively control and predict the settlement deformation of the surrounding ground surface caused by deep foundation excavation, the deep foundation pit project of Baoding City Automobile Technology Industrial Park is explored as an example. The initial population approach of the whale algorithm (WOA) is optimized using Cubic mapping, while the weights of the shrinkage envelope mechanism are adjusted to avoid the algorithm falling into local minima, the improved whale algorithm (IWOA) is proposed. Meanwhile, 10 benchmark test functions are selected to simulate the performance of IWOA, and the advantages of IWOA in learning efficiency and convergence speed are verified. The IWOA-LSTM deep foundation excavation deformation prediction model is established by optimizing the input weights and hidden layer thresholds in the deep long short-term memory (LSTM) neural network using the improved whale algorithm. The IWOA-LSTM prediction model is compared with LSTM, WOA-optimized LSTM (WOA-LSTM) and traditional machine learning, the results show that the final prediction score of the IWOA-LSTM prediction model is higher than the score of other models, and the prediction accuracy is better than that of traditional machine learning.

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