ANN-Based Dynamic Prediction of Daily Ground Settlement of Foundation Pit Considering Time-Dependent Influence Factors
Accurately and dynamically predicting ground settlements during the construction of foundation pits is pivotal to the understanding of the potential risk of foundation pits and, therefore, enables constructors to take timely and effective actions to ensure the construction safety of foundation pits....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/13/6324 |
| _version_ | 1797481109504655360 |
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| author | Zhenyu Zhang Rongqiao Xu Xi Wu Jinchang Wang |
| author_facet | Zhenyu Zhang Rongqiao Xu Xi Wu Jinchang Wang |
| author_sort | Zhenyu Zhang |
| collection | DOAJ |
| description | Accurately and dynamically predicting ground settlements during the construction of foundation pits is pivotal to the understanding of the potential risk of foundation pits and, therefore, enables constructors to take timely and effective actions to ensure the construction safety of foundation pits. Existing settlement prediction methods mainly focus on the prediction of the maximum ground settlements based on static influence factors, such as soil properties and the geometry of foundation pits. However, these methods are unable to be applied to the prediction of daily ground settlements in a direct way because daily ground settlements can be affected by many time-dependent influence factors, and an accurate prediction of daily ground settlements should take into consideration such factors. To address this problem, this paper proposes an artificial neural network-based daily ground settlement prediction method, where both static and time-dependent influence factors, as well as previous settlement monitoring data, are considered in the optimum artificial neural network. The proposed method is validated using data collected from a real cut-and-cover highway tunnel project in western Hangzhou, China. The results demonstrate that time-dependent influence factors and previous settlement monitoring data play vital roles in establishing an optimum artificial neural network for the accurate prediction of daily ground settlement. |
| first_indexed | 2024-03-09T22:09:48Z |
| format | Article |
| id | doaj.art-cff3a356fac94ac38b54644b2995665d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:09:48Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-cff3a356fac94ac38b54644b2995665d2023-11-23T19:34:39ZengMDPI AGApplied Sciences2076-34172022-06-011213632410.3390/app12136324ANN-Based Dynamic Prediction of Daily Ground Settlement of Foundation Pit Considering Time-Dependent Influence FactorsZhenyu Zhang0Rongqiao Xu1Xi Wu2Jinchang Wang3Department of Civil Engineering, Zhejiang University, Hangzhou 310058, ChinaDepartment of Civil Engineering, Zhejiang University, Hangzhou 310058, ChinaDepartment of Civil Engineering, Zhejiang University City College, Hangzhou 310015, ChinaDepartment of Civil Engineering, Zhejiang University, Hangzhou 310058, ChinaAccurately and dynamically predicting ground settlements during the construction of foundation pits is pivotal to the understanding of the potential risk of foundation pits and, therefore, enables constructors to take timely and effective actions to ensure the construction safety of foundation pits. Existing settlement prediction methods mainly focus on the prediction of the maximum ground settlements based on static influence factors, such as soil properties and the geometry of foundation pits. However, these methods are unable to be applied to the prediction of daily ground settlements in a direct way because daily ground settlements can be affected by many time-dependent influence factors, and an accurate prediction of daily ground settlements should take into consideration such factors. To address this problem, this paper proposes an artificial neural network-based daily ground settlement prediction method, where both static and time-dependent influence factors, as well as previous settlement monitoring data, are considered in the optimum artificial neural network. The proposed method is validated using data collected from a real cut-and-cover highway tunnel project in western Hangzhou, China. The results demonstrate that time-dependent influence factors and previous settlement monitoring data play vital roles in establishing an optimum artificial neural network for the accurate prediction of daily ground settlement.https://www.mdpi.com/2076-3417/12/13/6324artificial neural networkdaily ground settlementdynamic predictionfoundation pitsettlement monitoring |
| spellingShingle | Zhenyu Zhang Rongqiao Xu Xi Wu Jinchang Wang ANN-Based Dynamic Prediction of Daily Ground Settlement of Foundation Pit Considering Time-Dependent Influence Factors Applied Sciences artificial neural network daily ground settlement dynamic prediction foundation pit settlement monitoring |
| title | ANN-Based Dynamic Prediction of Daily Ground Settlement of Foundation Pit Considering Time-Dependent Influence Factors |
| title_full | ANN-Based Dynamic Prediction of Daily Ground Settlement of Foundation Pit Considering Time-Dependent Influence Factors |
| title_fullStr | ANN-Based Dynamic Prediction of Daily Ground Settlement of Foundation Pit Considering Time-Dependent Influence Factors |
| title_full_unstemmed | ANN-Based Dynamic Prediction of Daily Ground Settlement of Foundation Pit Considering Time-Dependent Influence Factors |
| title_short | ANN-Based Dynamic Prediction of Daily Ground Settlement of Foundation Pit Considering Time-Dependent Influence Factors |
| title_sort | ann based dynamic prediction of daily ground settlement of foundation pit considering time dependent influence factors |
| topic | artificial neural network daily ground settlement dynamic prediction foundation pit settlement monitoring |
| url | https://www.mdpi.com/2076-3417/12/13/6324 |
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