Prediction and Optimization of Pile Bearing Capacity Considering Effects of Time

Abstract

Prediction of pile bearing capacity has been considered an unsolved problem for years. This study presents a practical solution for the preparation and maximization of pile bearing capacity, considering the effects of time after the end of pile driving. The prediction phase proposes an intelligent equation using a genetic programming (GP) model. Thus, pile geometry, soil properties, initial pile capacity, and time after the end of driving were considered predictors to predict pile bearing capacity. The developed GP equation provided an acceptable level of accuracy in estimating pile bearing capacity. In the optimization phase, the developed GP equation was used as input in two powerful optimization algorithms, namely, the artificial bee colony (ABC) and the grey wolf optimization (GWO), in order to obtain the highest bearing capacity of the pile, which corresponds to the optimum values for input parameters. Among these two algorithms, GWO obtained a higher value for pile capacity compared to the ABC algorithm. The introduced models and their modeling procedure in this study can be used to predict the ultimate capacity of piles in such projects.

Proposing New Artificial Intelligence Models to Estimate Shear Wave Velocity of Fine-grained Soils: A Case Study

“…Typically, using new artificial intelligence (AI) methods to solve complex multivariate problems leads to better outcomes compared to conventional regression methods. In recent years, researchers have utilized various AI methods to predict the results of geotechnical (2)(3)(4)(5)(6)(7)(8)(9) and geophysical (10)(11)(12) tests.…”