@article{eprints1334,
           pages = {311--317},
           title = {The Use of Machine Learning Approach to Predict Pile Capacity in Non-Cohesive Soils},
          number = {3s},
           month = {September},
          volume = {14},
       publisher = {Scienceline Publication, Ltd},
          author = {Lesego Palalane and Mahongo Dithinde},
            year = {2024},
         journal = {Journal of Civil Engineering and Urbanism},
        abstract = {Existing theoretical and empirical pile design methods cannot accurately model the complex interaction between piles and soil. Consequently, there is a growing trend towards utilizing machine learning techniques to better capture the nonlinear soil-pile interaction. This paper aims to predict the capacity of bored piles in cohesionless soils using a machine learning approach. The machine learning algorithm was trained using a database of 18 bored pile cases in non-cohesive soils and validated with a separate dataset of 8 bored piles in cohesionless soil. Moreover, the performance of the machine learning method was compared with that of a traditional pile design method (i.e., SA-SPT method) in Southern Africa. The evaluation was based on the ratio of measured capacity to predicted capacity (Qm/Qp) statistics and the coefficient of determination (R2). The results showed an R2 of 0.89 for the machine learning method compared to 0.85 for the SA-SPT method, indicating the superior accuracy of the machine learning approach in predicting pile capacity.},
        keywords = {Machine learning, SPT-based pile methods, Load Bearing Capacity, Full-scale Load Test, Chin extrapolation method, Terzhagi?s 10\% criteria.},
             url = {http://eprints.science-line.com/id/eprint/1334/}
}