Foundation pit displacement monitoring and prediction using least squares support vector machines based on multi-point measurement
© 2018, The Author(s) 2018. Foundation pit displacement is a critical safety risk for both building structure and people lives. The accurate displacement monitoring and prediction of a deep foundation pit are essential to prevent potential risks at early construction stage. To achieve accurate predi...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Published: |
Sage Publications
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/69191 |
| _version_ | 1848761991970160640 |
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| author | Li, X. Liu, Xin Li, C. Hu, Z. Shen, G. Huang, Z. |
| author_facet | Li, X. Liu, Xin Li, C. Hu, Z. Shen, G. Huang, Z. |
| author_sort | Li, X. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2018, The Author(s) 2018. Foundation pit displacement is a critical safety risk for both building structure and people lives. The accurate displacement monitoring and prediction of a deep foundation pit are essential to prevent potential risks at early construction stage. To achieve accurate prediction, machine learning methods are extensively applied to fulfill this purpose. However, these approaches, such as support vector machines, have limitations in terms of data processing efficiency and prediction accuracy. As an emerging approach derived from support vector machines, least squares support vector machine improve the data processing efficiency through better use of equality constraints in the least squares loss functions. However, the accuracy of this approach highly relies on the large volume of influencing factors from the measurement of adjacent critical points, which is not normally available during the construction process. To address this issue, this study proposes an improved least squares support vector machine algorithm based on multi-point measuring techniques, namely, multi-point least squares support vector machine. To evaluate the effectiveness of the proposed multi-point least squares support vector machine approach, a real case study project was selected, and the results illustrated that the multi-point least squares support vector machine approach on average outperformed single-point least squares support vector machine in terms of prediction accuracy during the foundation pit monitoring and prediction process. |
| first_indexed | 2025-11-14T10:40:28Z |
| format | Journal Article |
| id | curtin-20.500.11937-69191 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:40:28Z |
| publishDate | 2018 |
| publisher | Sage Publications |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-691912018-06-29T12:35:25Z Foundation pit displacement monitoring and prediction using least squares support vector machines based on multi-point measurement Li, X. Liu, Xin Li, C. Hu, Z. Shen, G. Huang, Z. © 2018, The Author(s) 2018. Foundation pit displacement is a critical safety risk for both building structure and people lives. The accurate displacement monitoring and prediction of a deep foundation pit are essential to prevent potential risks at early construction stage. To achieve accurate prediction, machine learning methods are extensively applied to fulfill this purpose. However, these approaches, such as support vector machines, have limitations in terms of data processing efficiency and prediction accuracy. As an emerging approach derived from support vector machines, least squares support vector machine improve the data processing efficiency through better use of equality constraints in the least squares loss functions. However, the accuracy of this approach highly relies on the large volume of influencing factors from the measurement of adjacent critical points, which is not normally available during the construction process. To address this issue, this study proposes an improved least squares support vector machine algorithm based on multi-point measuring techniques, namely, multi-point least squares support vector machine. To evaluate the effectiveness of the proposed multi-point least squares support vector machine approach, a real case study project was selected, and the results illustrated that the multi-point least squares support vector machine approach on average outperformed single-point least squares support vector machine in terms of prediction accuracy during the foundation pit monitoring and prediction process. 2018 Journal Article http://hdl.handle.net/20.500.11937/69191 10.1177/1475921718767935 Sage Publications restricted |
| spellingShingle | Li, X. Liu, Xin Li, C. Hu, Z. Shen, G. Huang, Z. Foundation pit displacement monitoring and prediction using least squares support vector machines based on multi-point measurement |
| title | Foundation pit displacement monitoring and prediction using least squares support vector machines based on multi-point measurement |
| title_full | Foundation pit displacement monitoring and prediction using least squares support vector machines based on multi-point measurement |
| title_fullStr | Foundation pit displacement monitoring and prediction using least squares support vector machines based on multi-point measurement |
| title_full_unstemmed | Foundation pit displacement monitoring and prediction using least squares support vector machines based on multi-point measurement |
| title_short | Foundation pit displacement monitoring and prediction using least squares support vector machines based on multi-point measurement |
| title_sort | foundation pit displacement monitoring and prediction using least squares support vector machines based on multi-point measurement |
| url | http://hdl.handle.net/20.500.11937/69191 |