Data-Driven Method for Predicting Soil Pressure of Foot Blades within a Large Underwater Caisson
The soil pressure on the bottom surface of the foot blades is an important monitoring point during the sinking process of large underwater caissons. Complex soil-structure interactions occur during the sinking process, making it difficult to accurately predict the soil pressure of foot blades. Accur...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Geofluids |
| Online Access: | http://dx.doi.org/10.1155/2022/1983303 |
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| _version_ | 1832551240159985664 |
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| author | Can Huang Hao Zhu Kunyao Li Jianxin Zheng Hao Li Jiaming Li Yao Xiao |
| author_facet | Can Huang Hao Zhu Kunyao Li Jianxin Zheng Hao Li Jiaming Li Yao Xiao |
| author_sort | Can Huang |
| collection | DOAJ |
| description | The soil pressure on the bottom surface of the foot blades is an important monitoring point during the sinking process of large underwater caissons. Complex soil-structure interactions occur during the sinking process, making it difficult to accurately predict the soil pressure of foot blades. Accurate construction processes often rely on data from the soil pressure of foot blades in the field. In this study, a data-driven approach is used to establish the relationship between the amount of sinking of the caisson and the soil pressure of foot blades. Furthermore, by improving the splitting method of the original Classification and Regression Tree (CART) algorithm, a single model’s numerical prediction of 80-foot blades soil pressures is realized. The improved CART model, multilayer perceptron (MLP), long short-term memory (LSTM), and a linear regression model are compared through a comprehensive multiparameter evaluation method. Finally, this article discusses the deployment scheme of the model by comparing and analyzing the data in the time period of 10 : 00 on July 29, 2020, and 23 : 00 on August 7, 2020. The experimental results can satisfy the engineering demands and provide a basis for further data-driven intelligent control of large caisson sinking. |
| format | Article |
| id | doaj-art-237b5424415f430097e1077de0a842d1 |
| institution | Kabale University |
| issn | 1468-8123 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geofluids |
| spelling | doaj-art-237b5424415f430097e1077de0a842d12025-02-03T06:01:52ZengWileyGeofluids1468-81232022-01-01202210.1155/2022/1983303Data-Driven Method for Predicting Soil Pressure of Foot Blades within a Large Underwater CaissonCan Huang0Hao Zhu1Kunyao Li2Jianxin Zheng3Hao Li4Jiaming Li5Yao Xiao6Research and Development Center of Transport Industry of Intelligent Manufacturing Technologies of Transport InfrastructureResearch and Development Center of Transport Industry of Intelligent Manufacturing Technologies of Transport InfrastructureResearch and Development Center of Transport Industry of Intelligent Manufacturing Technologies of Transport InfrastructureResearch and Development Center of Transport Industry of Intelligent Manufacturing Technologies of Transport InfrastructureResearch and Development Center of Transport Industry of Intelligent Manufacturing Technologies of Transport InfrastructureState Key Laboratory of Coastal and Offshore EngineeringResearch and Development Center of Transport Industry of Intelligent Manufacturing Technologies of Transport InfrastructureThe soil pressure on the bottom surface of the foot blades is an important monitoring point during the sinking process of large underwater caissons. Complex soil-structure interactions occur during the sinking process, making it difficult to accurately predict the soil pressure of foot blades. Accurate construction processes often rely on data from the soil pressure of foot blades in the field. In this study, a data-driven approach is used to establish the relationship between the amount of sinking of the caisson and the soil pressure of foot blades. Furthermore, by improving the splitting method of the original Classification and Regression Tree (CART) algorithm, a single model’s numerical prediction of 80-foot blades soil pressures is realized. The improved CART model, multilayer perceptron (MLP), long short-term memory (LSTM), and a linear regression model are compared through a comprehensive multiparameter evaluation method. Finally, this article discusses the deployment scheme of the model by comparing and analyzing the data in the time period of 10 : 00 on July 29, 2020, and 23 : 00 on August 7, 2020. The experimental results can satisfy the engineering demands and provide a basis for further data-driven intelligent control of large caisson sinking.http://dx.doi.org/10.1155/2022/1983303 |
| spellingShingle | Can Huang Hao Zhu Kunyao Li Jianxin Zheng Hao Li Jiaming Li Yao Xiao Data-Driven Method for Predicting Soil Pressure of Foot Blades within a Large Underwater Caisson Geofluids |
| title | Data-Driven Method for Predicting Soil Pressure of Foot Blades within a Large Underwater Caisson |
| title_full | Data-Driven Method for Predicting Soil Pressure of Foot Blades within a Large Underwater Caisson |
| title_fullStr | Data-Driven Method for Predicting Soil Pressure of Foot Blades within a Large Underwater Caisson |
| title_full_unstemmed | Data-Driven Method for Predicting Soil Pressure of Foot Blades within a Large Underwater Caisson |
| title_short | Data-Driven Method for Predicting Soil Pressure of Foot Blades within a Large Underwater Caisson |
| title_sort | data driven method for predicting soil pressure of foot blades within a large underwater caisson |
| url | http://dx.doi.org/10.1155/2022/1983303 |
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