Developing advanced datadriven framework to predict the bearing capacity of piles on rock
Abstract Developing accurate predictive models for pile bearing capacity on rock is crucial for optimizing foundation design and ensuring structural stability. This research presents an advanced data-driven framework that integrates multiple machine learning algorithms to predict the bearing capacit...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-96186-1 |
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| author | Kennedy C. Onyelowe Shadi Hanandeh Viroon Kamchoom Ahmed M. Ebid Fabián Danilo Reyes Silva José Luis Allauca Palta José Luis Llamuca Llamuca Siva Avudaiappan |
| author_facet | Kennedy C. Onyelowe Shadi Hanandeh Viroon Kamchoom Ahmed M. Ebid Fabián Danilo Reyes Silva José Luis Allauca Palta José Luis Llamuca Llamuca Siva Avudaiappan |
| author_sort | Kennedy C. Onyelowe |
| collection | DOAJ |
| description | Abstract Developing accurate predictive models for pile bearing capacity on rock is crucial for optimizing foundation design and ensuring structural stability. This research presents an advanced data-driven framework that integrates multiple machine learning algorithms to predict the bearing capacity of piles based on geotechnical and in-situ test parameters. A comprehensive dataset comprising key influencing factors such as pile dimensions, geological characteristics, and penetration resistance was utilized to train and validate various models, including Kstar, M5Rules, ElasticNet, XNV, and Decision Trees. The Taylor diagram and statistical evaluations demonstrated the superiority of the proposed models in capturing complex nonlinear relationships, with high correlation coefficients and low root mean square errors indicating robust predictive capabilities. Sensitivity analyses using Hoffman and Gardener’s approach and SHAP values identified the most influential parameters, revealing that penetration resistance, pile embedment depth, and geological conditions significantly impact pile capacity. The findings underscore the effectiveness of machine learning in geotechnical engineering applications, offering a reliable and efficient alternative to traditional empirical and analytical methods. The developed framework provides engineers and practitioners with a powerful tool for improving pile design accuracy, reducing uncertainties, and optimizing construction practices. Future research should focus on expanding the dataset with diverse geological conditions and exploring hybrid modeling techniques to enhance prediction accuracy further. |
| format | Article |
| id | doaj-art-54986e321e294b09b8fd4de444c413de |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-54986e321e294b09b8fd4de444c413de2025-08-20T03:07:40ZengNature PortfolioScientific Reports2045-23222025-04-0115112210.1038/s41598-025-96186-1Developing advanced datadriven framework to predict the bearing capacity of piles on rockKennedy C. Onyelowe0Shadi Hanandeh1Viroon Kamchoom2Ahmed M. Ebid3Fabián Danilo Reyes Silva4José Luis Allauca Palta5José Luis Llamuca Llamuca6Siva Avudaiappan7Department of Civil Engineering, College of Eng & Eng Technology, Michael Okpara University of AgricultureDepartment of Civil Engineering, Faculty of Engineering, Al-Balqa Applied UniversityExcellent Center for Green and Sustainable Infrastructure, Department of Civil Engineering, School of Engineering, King Mongkut’S Institute of Technology Ladkrabang (KMITL)Department of Civil Engineering, Faculty of Engineering, Future University in EgyptFacultad de Ciencias Pecuarias, Escuela Superior Politécnica de Chimborazo (ESPOCH)Instituto Superior Tecnológico General Eloy Alfaro (ISTGEA)Facultad de Administración de Empresas, Escuela Superior Politécnica de Chimborazo (ESPOCH)Departamento de Ciencias de la Construcción, Facultad de Ciencias de la Construcción Ordenamiento Territorial, Universidad Tecnológica MetropolitanaAbstract Developing accurate predictive models for pile bearing capacity on rock is crucial for optimizing foundation design and ensuring structural stability. This research presents an advanced data-driven framework that integrates multiple machine learning algorithms to predict the bearing capacity of piles based on geotechnical and in-situ test parameters. A comprehensive dataset comprising key influencing factors such as pile dimensions, geological characteristics, and penetration resistance was utilized to train and validate various models, including Kstar, M5Rules, ElasticNet, XNV, and Decision Trees. The Taylor diagram and statistical evaluations demonstrated the superiority of the proposed models in capturing complex nonlinear relationships, with high correlation coefficients and low root mean square errors indicating robust predictive capabilities. Sensitivity analyses using Hoffman and Gardener’s approach and SHAP values identified the most influential parameters, revealing that penetration resistance, pile embedment depth, and geological conditions significantly impact pile capacity. The findings underscore the effectiveness of machine learning in geotechnical engineering applications, offering a reliable and efficient alternative to traditional empirical and analytical methods. The developed framework provides engineers and practitioners with a powerful tool for improving pile design accuracy, reducing uncertainties, and optimizing construction practices. Future research should focus on expanding the dataset with diverse geological conditions and exploring hybrid modeling techniques to enhance prediction accuracy further.https://doi.org/10.1038/s41598-025-96186-1PilesRocksBearing capacityAdvanced machine learningFoundations |
| spellingShingle | Kennedy C. Onyelowe Shadi Hanandeh Viroon Kamchoom Ahmed M. Ebid Fabián Danilo Reyes Silva José Luis Allauca Palta José Luis Llamuca Llamuca Siva Avudaiappan Developing advanced datadriven framework to predict the bearing capacity of piles on rock Scientific Reports Piles Rocks Bearing capacity Advanced machine learning Foundations |
| title | Developing advanced datadriven framework to predict the bearing capacity of piles on rock |
| title_full | Developing advanced datadriven framework to predict the bearing capacity of piles on rock |
| title_fullStr | Developing advanced datadriven framework to predict the bearing capacity of piles on rock |
| title_full_unstemmed | Developing advanced datadriven framework to predict the bearing capacity of piles on rock |
| title_short | Developing advanced datadriven framework to predict the bearing capacity of piles on rock |
| title_sort | developing advanced datadriven framework to predict the bearing capacity of piles on rock |
| topic | Piles Rocks Bearing capacity Advanced machine learning Foundations |
| url | https://doi.org/10.1038/s41598-025-96186-1 |
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