TY  - JOUR
EP  - 317
A1  - Palalane, Lesego
A1  - Dithinde, Mahongo
SP  - 311
Y1  - 2024/09/15/
N2  - 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.
VL  - 14
UR  - https://doi.org/10.54203/jceu.2024.34
KW  - Machine learning
KW  -  SPT-based pile methods
KW  -  Load Bearing Capacity
KW  -  Full-scale Load Test
KW  -  Chin extrapolation method
KW  -  Terzhagi?s 10% criteria.
ID  - eprints1334
SN  - 22520430
TI  - The Use of Machine Learning Approach to Predict Pile Capacity in Non-Cohesive Soils
IS  - 3s
AV  - public
JF  - Journal of Civil Engineering and Urbanism
PB  - Scienceline Publication, Ltd
ER  -