Settlement Prediction of Foundation Pit Excavation Based on the GWO-ELM Model considering Different States of Influence
This paper proposes a novel grey wolf optimization-extreme learning machine model, namely, the GWO-ELM model, to train and predict the ground subsidence by combining the extreme learning machine with the grey wolf optimization algorithm. Taking an excavation project of a foundation pit of Kunming in...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/8896210 |
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| author | Qiao Shi-fan Tan Jun-kun Zhang Yong-gang Wan Li-jun Zhang Ming-fei Tang Jun He Qing |
| author_facet | Qiao Shi-fan Tan Jun-kun Zhang Yong-gang Wan Li-jun Zhang Ming-fei Tang Jun He Qing |
| author_sort | Qiao Shi-fan |
| collection | DOAJ |
| description | This paper proposes a novel grey wolf optimization-extreme learning machine model, namely, the GWO-ELM model, to train and predict the ground subsidence by combining the extreme learning machine with the grey wolf optimization algorithm. Taking an excavation project of a foundation pit of Kunming in China as an example, after analyzing the settlement monitoring data of cross sections JC55 and JC56, the representative monitoring sites JC55-2 and JC56-1 were selected as the training monitoring samples of the GWO-ELM model. And three kinds of GWO-ELM models such as considering the influence of time series, influence of settlement factors, and after optimization were established to predict the ground settlement near the foundation pit. The predictive results are that their average relative error and average absolute error are ranked from large to small as GWO-ELM model based on time series, GWO-ELM model based on settlement factors, and optimized GWO-ELM model for the three kinds of GWO-ELM models at monitoring points JC55-2 and JC56-1. Accordingly, the optimized GWO-ELM model has the strongest predictive ability. |
| format | Article |
| id | doaj-art-b8e2c3ae0c234d5f86ab4a6635307aef |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-b8e2c3ae0c234d5f86ab4a6635307aef2025-02-03T06:46:44ZengWileyAdvances in Civil Engineering1687-80861687-80942021-01-01202110.1155/2021/88962108896210Settlement Prediction of Foundation Pit Excavation Based on the GWO-ELM Model considering Different States of InfluenceQiao Shi-fan0Tan Jun-kun1Zhang Yong-gang2Wan Li-jun3Zhang Ming-fei4Tang Jun5He Qing6School of Civil Engineering, Central South University, Changsha 410083, ChinaSchool of Civil Engineering, Central South University, Changsha 410083, ChinaKey Laboratory of Geotechnical and Underground Engineering of Ministry of Education and Department of Geotechnical Engineering, Tongji University, Shanghai 200092, ChinaRailway Engineering Research Institute, China Academy of Railway Science Co., Ltd., Beijing 12 100081, ChinaCivil Engineering and Architecture Institute, Zhengzhou University of Aeronautics, Zhengzhou 450046, ChinaCollege of Civil Engineering, Huaqiao University, Xiamen 361000, ChinaSchool of Geotechnical Engineering, China University of Mining and Technology (Beijing), Beijing 100083, ChinaThis paper proposes a novel grey wolf optimization-extreme learning machine model, namely, the GWO-ELM model, to train and predict the ground subsidence by combining the extreme learning machine with the grey wolf optimization algorithm. Taking an excavation project of a foundation pit of Kunming in China as an example, after analyzing the settlement monitoring data of cross sections JC55 and JC56, the representative monitoring sites JC55-2 and JC56-1 were selected as the training monitoring samples of the GWO-ELM model. And three kinds of GWO-ELM models such as considering the influence of time series, influence of settlement factors, and after optimization were established to predict the ground settlement near the foundation pit. The predictive results are that their average relative error and average absolute error are ranked from large to small as GWO-ELM model based on time series, GWO-ELM model based on settlement factors, and optimized GWO-ELM model for the three kinds of GWO-ELM models at monitoring points JC55-2 and JC56-1. Accordingly, the optimized GWO-ELM model has the strongest predictive ability.http://dx.doi.org/10.1155/2021/8896210 |
| spellingShingle | Qiao Shi-fan Tan Jun-kun Zhang Yong-gang Wan Li-jun Zhang Ming-fei Tang Jun He Qing Settlement Prediction of Foundation Pit Excavation Based on the GWO-ELM Model considering Different States of Influence Advances in Civil Engineering |
| title | Settlement Prediction of Foundation Pit Excavation Based on the GWO-ELM Model considering Different States of Influence |
| title_full | Settlement Prediction of Foundation Pit Excavation Based on the GWO-ELM Model considering Different States of Influence |
| title_fullStr | Settlement Prediction of Foundation Pit Excavation Based on the GWO-ELM Model considering Different States of Influence |
| title_full_unstemmed | Settlement Prediction of Foundation Pit Excavation Based on the GWO-ELM Model considering Different States of Influence |
| title_short | Settlement Prediction of Foundation Pit Excavation Based on the GWO-ELM Model considering Different States of Influence |
| title_sort | settlement prediction of foundation pit excavation based on the gwo elm model considering different states of influence |
| url | http://dx.doi.org/10.1155/2021/8896210 |
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