Multi-factor settlement prediction around foundation pit based on SSA-gradient descent model
Abstract With the rise of machine learning, a lot of excellent algorithms are used for settlement prediction. Backpropagation (BP) and Elman are two typical algorithms based on gradient descent, but their performance is greatly affected by the random selection of initial weights and thresholds, so t...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-24232-3 |
| _version_ | 1811318083659235328 |
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| author | Zhengcai Li Xinmin Hu Chun Chen Chenyang Liu Yalu Han Yuanfeng Yu Lizhi Du |
| author_facet | Zhengcai Li Xinmin Hu Chun Chen Chenyang Liu Yalu Han Yuanfeng Yu Lizhi Du |
| author_sort | Zhengcai Li |
| collection | DOAJ |
| description | Abstract With the rise of machine learning, a lot of excellent algorithms are used for settlement prediction. Backpropagation (BP) and Elman are two typical algorithms based on gradient descent, but their performance is greatly affected by the random selection of initial weights and thresholds, so this paper chooses Sparrow Search Algorithm (SSA) to build joint model. Then, two sets of land subsidence monitoring data generated during the excavation of a foundation pit in South China are used for analysis and verification. The results show that the optimization effect of SSA on the gradient descent model is remarkable and the stability of the model is improved to a certain extent. After that, SSA is compared with GA and PSO algorithms, and the comparison shows that SSA has higher optimization efficiency. Finally, select SSA-KELM, SSA-LSSVM and SSA-BP for further comparison and it proves that the optimization efficiency of SSA for BP is higher than other kind of neural network. At the same time, it also shows that the seven influencing factors selected in this paper are feasible as the input variables of the model, which is consistent with the conclusion drawn by the grey relational analysis. |
| first_indexed | 2024-04-13T12:19:36Z |
| format | Article |
| id | doaj.art-ae73fb652aab4ab9aa3e26c61cb51b8e |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-13T12:19:36Z |
| publishDate | 2022-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-ae73fb652aab4ab9aa3e26c61cb51b8e2022-12-22T02:47:15ZengNature PortfolioScientific Reports2045-23222022-11-0112111510.1038/s41598-022-24232-3Multi-factor settlement prediction around foundation pit based on SSA-gradient descent modelZhengcai Li0Xinmin Hu1Chun Chen2Chenyang Liu3Yalu Han4Yuanfeng Yu5Lizhi Du6College of Construction Engineering, Jilin UniversityCollege of Construction Engineering, Jilin UniversityBeijing Aidi Geological Engineering Technology Co., LtdCollege of Construction Engineering, Jilin UniversityCollege of Construction Engineering, Jilin UniversityBeijing Aidi Geological Engineering Technology Co., LtdCollege of Construction Engineering, Jilin UniversityAbstract With the rise of machine learning, a lot of excellent algorithms are used for settlement prediction. Backpropagation (BP) and Elman are two typical algorithms based on gradient descent, but their performance is greatly affected by the random selection of initial weights and thresholds, so this paper chooses Sparrow Search Algorithm (SSA) to build joint model. Then, two sets of land subsidence monitoring data generated during the excavation of a foundation pit in South China are used for analysis and verification. The results show that the optimization effect of SSA on the gradient descent model is remarkable and the stability of the model is improved to a certain extent. After that, SSA is compared with GA and PSO algorithms, and the comparison shows that SSA has higher optimization efficiency. Finally, select SSA-KELM, SSA-LSSVM and SSA-BP for further comparison and it proves that the optimization efficiency of SSA for BP is higher than other kind of neural network. At the same time, it also shows that the seven influencing factors selected in this paper are feasible as the input variables of the model, which is consistent with the conclusion drawn by the grey relational analysis.https://doi.org/10.1038/s41598-022-24232-3 |
| spellingShingle | Zhengcai Li Xinmin Hu Chun Chen Chenyang Liu Yalu Han Yuanfeng Yu Lizhi Du Multi-factor settlement prediction around foundation pit based on SSA-gradient descent model Scientific Reports |
| title | Multi-factor settlement prediction around foundation pit based on SSA-gradient descent model |
| title_full | Multi-factor settlement prediction around foundation pit based on SSA-gradient descent model |
| title_fullStr | Multi-factor settlement prediction around foundation pit based on SSA-gradient descent model |
| title_full_unstemmed | Multi-factor settlement prediction around foundation pit based on SSA-gradient descent model |
| title_short | Multi-factor settlement prediction around foundation pit based on SSA-gradient descent model |
| title_sort | multi factor settlement prediction around foundation pit based on ssa gradient descent model |
| url | https://doi.org/10.1038/s41598-022-24232-3 |
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