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 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.