Prediction of ultimate axial load-carrying capacity of piles using a support vector machine based on CPT data |
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| Institution: | 1. Dept. of Civil Engineering, Ferdowsi University of Mashhad, Mashhad, Iran;2. School of Civil, Environmental and Mining Engineering, University of Adelaide, Australia;1. Geotechnical Engineer, Amey Consulting, International Design Hub, The Colmore Building, Birmingham B4 6AT, United Kingdom;2. Department of Roads and Transport, College of Engineering, University of Al-Qadisiyah, Al-Qadisiyah, Iraq;3. Professor of Geotechnical and Underground Engineering, School of Engineering, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom;1. Department of Infrastructure Engineering, The University of Melbourne, Parkville, Australia;2. Department of Engineering, University of Cambridge, Cambridge, United Kingdom;1. College of Civil Engineering and Architecture, Wenzhou University, Wenzhou, Zhejiang 325035, China;2. School of Civil Engineering, Southeast University, Nanjing 210096, China;3. Key Laboratory of Engineering and Technology for Soft Soil Foundation and Tideland Reclamation Zhejiang Province, Wenzhou, Zhejiang 325035, China;4. Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Nanjing 210096, China;5. Wenzhou Key Laboratory of Traffic Piezoelectric Engineering Technology, Wenzhou, Zhejiang 325035, China;6. Engineering Center for Disaster Prevention and Mitigation Technology of Coastal Soft Soil, Wenzhou, Zhejiang 325035, China;7. International Cooperation Base for Science and Technology on Ultra-soft Soil Engineering and Smart Monitoring, Wenzhou, Zhejiang 325035, China;8. WSP Australia Pty Limited, 5 Spring Street, Perth, Western Australia 6010, Australia;9. State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China;1. Department of Civil Engineering, Gonbad University, Gonbad, Golestan, Iran;2. School of Engineering, RMIT University, Melbourne, Victoria, Australia;3. Department of Civil Engineering, University of Guilan, Rasht, Guilan, Iran |
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| Abstract: | The support vector machine (SVM) is a relatively new artificial intelligence technique which is increasingly being applied to geotechnical problems and is yielding encouraging results. In this paper SVM models are developed for predicting the ultimate axial load-carrying capacity of piles based on cone penetration test (CPT) data. A data set of 108 samples is used to develop the SVM models. These data were obtained from the literature containing pile load tests and each sample contains information regarding pile geometry, full-scale static pile load tests and CPT results. Moreover, a sensitivity analysis is carried out to examine the relative significance of each input variable with respect to ultimate strength prediction. Finally, a statistical analysis is conducted to make comparisons between predictions obtained from the SVM models and three traditional CPT-based methods for determining pile capacity. The comparison confirms that the SVM models developed in this paper outperform the traditional methods. |
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| Keywords: | Support vector machine (SVM) Static pile load test Cone penetration test (CPT) Ultimate bearing capacity |
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