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 foundation engineering projects. In practice, accurate predictions challenging due to complex interplay various geotechnical factors including characteristics ground conditions. This study proposes data-driven model coping with problem interest that hybridizes machine learning metaheuristic approaches. Least squares support vector regression (LSSVR) analyzing dataset containing historical records tests. Based on such datasets, LSSVR capable generalizing multivariate function estimates values based set variables describing Moreover, opposition-based differential flower pollination (ODFP) proposed optimize process. Experimental results supported by statistical test showed ODFP-optimized can achieve good predictive performance terms root mean square error, absolute percentage error coefficient determination. These confirm be potential alternative assist civil engineers task estimation.