Prediction of Pile Bearing Capacity Using Opposition-Based Differential Flower Pollination-Optimized Least Squares Support Vector Regression (ODFP-LSSVR)
Pile foundations are widely used for high-rise structures constructed in soft ground. The bearing capacity of pile is a crucial parameter required during the design and construction phase of pile foundation engineering projects. In practice, accurate predictions of pile bearing capacity are challeng...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/7183700 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849686506964254720 |
|---|---|
| author | Nhat-Duc Hoang Xuan-Linh Tran Thanh-Canh Huynh |
| author_facet | Nhat-Duc Hoang Xuan-Linh Tran Thanh-Canh Huynh |
| author_sort | Nhat-Duc Hoang |
| collection | DOAJ |
| description | Pile foundations are widely used for high-rise structures constructed in soft ground. The bearing capacity of pile is a crucial parameter required during the design and construction phase of pile foundation engineering projects. In practice, accurate predictions of pile bearing capacity are challenging due to a complex interplay of various geotechnical engineering factors including pile characteristics and ground conditions. This study proposes a data-driven model for coping with the problem of interest that hybridizes machine learning and metaheuristic approaches. Least squares support vector regression (LSSVR) is used for analyzing a dataset containing historical records of pile tests. Based on such datasets, LSSVR is capable of generalizing a multivariate function that estimates values of pile bearing capacity based on a set of variables describing pile characteristics and ground conditions. Moreover, opposition-based differential flower pollination (ODFP) metaheuristic is proposed to optimize the LSSVR learning process. Experimental results supported by the statistical test showed that the proposed ODFP-optimized LSSVR can achieve a good predictive performance in terms of root mean square error, mean absolute percentage error mean absolute error, and coefficient of determination. These results confirm that the ODFP-optimized LSSVR can be a potential alternative to assist civil engineers in the task of pile bearing capacity estimation. |
| format | Article |
| id | doaj-art-c016ce1822f8454180be4f82fcf9307c |
| institution | DOAJ |
| issn | 1687-8094 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-c016ce1822f8454180be4f82fcf9307c2025-08-20T03:22:41ZengWileyAdvances in Civil Engineering1687-80942022-01-01202210.1155/2022/7183700Prediction of Pile Bearing Capacity Using Opposition-Based Differential Flower Pollination-Optimized Least Squares Support Vector Regression (ODFP-LSSVR)Nhat-Duc Hoang0Xuan-Linh Tran1Thanh-Canh Huynh2Institute of Research and DevelopmentInstitute of Research and DevelopmentInstitute of Research and DevelopmentPile foundations are widely used for high-rise structures constructed in soft ground. The bearing capacity of pile is a crucial parameter required during the design and construction phase of pile foundation engineering projects. In practice, accurate predictions of pile bearing capacity are challenging due to a complex interplay of various geotechnical engineering factors including pile characteristics and ground conditions. This study proposes a data-driven model for coping with the problem of interest that hybridizes machine learning and metaheuristic approaches. Least squares support vector regression (LSSVR) is used for analyzing a dataset containing historical records of pile tests. Based on such datasets, LSSVR is capable of generalizing a multivariate function that estimates values of pile bearing capacity based on a set of variables describing pile characteristics and ground conditions. Moreover, opposition-based differential flower pollination (ODFP) metaheuristic is proposed to optimize the LSSVR learning process. Experimental results supported by the statistical test showed that the proposed ODFP-optimized LSSVR can achieve a good predictive performance in terms of root mean square error, mean absolute percentage error mean absolute error, and coefficient of determination. These results confirm that the ODFP-optimized LSSVR can be a potential alternative to assist civil engineers in the task of pile bearing capacity estimation.http://dx.doi.org/10.1155/2022/7183700 |
| spellingShingle | Nhat-Duc Hoang Xuan-Linh Tran Thanh-Canh Huynh Prediction of Pile Bearing Capacity Using Opposition-Based Differential Flower Pollination-Optimized Least Squares Support Vector Regression (ODFP-LSSVR) Advances in Civil Engineering |
| title | Prediction of Pile Bearing Capacity Using Opposition-Based Differential Flower Pollination-Optimized Least Squares Support Vector Regression (ODFP-LSSVR) |
| title_full | Prediction of Pile Bearing Capacity Using Opposition-Based Differential Flower Pollination-Optimized Least Squares Support Vector Regression (ODFP-LSSVR) |
| title_fullStr | Prediction of Pile Bearing Capacity Using Opposition-Based Differential Flower Pollination-Optimized Least Squares Support Vector Regression (ODFP-LSSVR) |
| title_full_unstemmed | Prediction of Pile Bearing Capacity Using Opposition-Based Differential Flower Pollination-Optimized Least Squares Support Vector Regression (ODFP-LSSVR) |
| title_short | Prediction of Pile Bearing Capacity Using Opposition-Based Differential Flower Pollination-Optimized Least Squares Support Vector Regression (ODFP-LSSVR) |
| title_sort | prediction of pile bearing capacity using opposition based differential flower pollination optimized least squares support vector regression odfp lssvr |
| url | http://dx.doi.org/10.1155/2022/7183700 |
| work_keys_str_mv | AT nhatduchoang predictionofpilebearingcapacityusingoppositionbaseddifferentialflowerpollinationoptimizedleastsquaressupportvectorregressionodfplssvr AT xuanlinhtran predictionofpilebearingcapacityusingoppositionbaseddifferentialflowerpollinationoptimizedleastsquaressupportvectorregressionodfplssvr AT thanhcanhhuynh predictionofpilebearingcapacityusingoppositionbaseddifferentialflowerpollinationoptimizedleastsquaressupportvectorregressionodfplssvr |