Optimized LSTM based on improved whale algorithm for surface subsidence deformation prediction
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-04-01
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| Series: | Electronic Research Archive |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/era.2023174?viewType=HTML |
| _version_ | 1797808461947338752 |
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| author | Ju Wang Leifeng Zhang Sanqiang Yang Shaoning Lian Peng Wang Lei Yu Zhenyu Yang |
| author_facet | Ju Wang Leifeng Zhang Sanqiang Yang Shaoning Lian Peng Wang Lei Yu Zhenyu Yang |
| author_sort | Ju Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T06:37:51Z |
| format | Article |
| id | doaj.art-2e69cec87f844a40a350e273822c56a8 |
| institution | Directory Open Access Journal |
| issn | 2688-1594 |
| language | English |
| last_indexed | 2024-03-13T06:37:51Z |
| publishDate | 2023-04-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Electronic Research Archive |
| spelling | doaj.art-2e69cec87f844a40a350e273822c56a82023-06-09T01:10:55ZengAIMS PressElectronic Research Archive2688-15942023-04-013163435345210.3934/era.2023174Optimized LSTM based on improved whale algorithm for surface subsidence deformation predictionJu Wang0Leifeng Zhang1Sanqiang Yang2Shaoning Lian 3Peng Wang4 Lei Yu5Zhenyu Yang61. Beijing Municipal Road and Bridge Co., LTD., Beijing, China2. Hebei Civil Engineering Monitoring and Evaluation Technology Innovation Center, College of Civil Engineering, Hebei University, Baoding, Hebei, China2. Hebei Civil Engineering Monitoring and Evaluation Technology Innovation Center, College of Civil Engineering, Hebei University, Baoding, Hebei, China1. Beijing Municipal Road and Bridge Co., LTD., Beijing, China1. Beijing Municipal Road and Bridge Co., LTD., Beijing, China1. Beijing Municipal Road and Bridge Co., LTD., Beijing, China2. Hebei Civil Engineering Monitoring and Evaluation Technology Innovation Center, College of Civil Engineering, Hebei University, Baoding, Hebei, ChinaIn 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.https://www.aimspress.com/article/doi/10.3934/era.2023174?viewType=HTMLdeep foundation pitdeep learningwhale optimization algorithmnumerical simulationshort and long term memorysettlement prediction |
| spellingShingle | 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 Electronic Research Archive deep foundation pit deep learning whale optimization algorithm numerical simulation short and long term memory settlement prediction |
| title | Optimized LSTM based on improved whale algorithm for surface subsidence deformation prediction |
| title_full | Optimized LSTM based on improved whale algorithm for surface subsidence deformation prediction |
| title_fullStr | Optimized LSTM based on improved whale algorithm for surface subsidence deformation prediction |
| title_full_unstemmed | Optimized LSTM based on improved whale algorithm for surface subsidence deformation prediction |
| title_short | Optimized LSTM based on improved whale algorithm for surface subsidence deformation prediction |
| title_sort | optimized lstm based on improved whale algorithm for surface subsidence deformation prediction |
| topic | deep foundation pit deep learning whale optimization algorithm numerical simulation short and long term memory settlement prediction |
| url | https://www.aimspress.com/article/doi/10.3934/era.2023174?viewType=HTML |
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