Optimized systems of multi-layer perceptron predictive model for estimating pile-bearing capacity

Abstract

The primary goal of this research is to leverage the advancements in machine learning techniques to forecast the bearing capacity of piles effectively. Accurately predicting load-bearing capability is an indispensable aspect in the field of substructure engineering. It is worth noting that determining load-bearing capability via in-place burden tests is a resource-intensive and labor-intensive process. This study presents a pragmatic soft computing methodology to tackle the aforementioned challenge, employing a multi-layer perceptron (MLP) for the estimation of load-bearing capacity. The dataset employed in this research encompasses a multitude of field-based pile load tests, with a meticulous selection of the most impactful factors influencing pile-bearing capacity as input variables. For a comprehensive comparative analysis, genetic algorithm-based optimizers (Crystal Structure Algorithm (CSA) and Fox Optimization (FOX)) were incorporated with MLP, leading to the development of hybrid models referred to as MLFO and MLSC, both structured with three layers. The performance of these models was rigorously evaluated using five key performance indices. The findings indicated a consistent superiority of MLFO over MLSC across all three layers. Remarkably, MLFO exhibited exceptional performance in the second layer (MLFO (2)), boasting an impressive R2 value of 0.992, an RMSE of 33.470, and a minimal SI value of 0.031. On the other hand, MLCS (1) registered the lowest accuracy in predicting the process with the least R2 value related to the validation phase of the model with 0.953. Taken together, these results affirm that the optimized MLP model stands as a valuable and practical tool for accurately estimating pile-bearing capacity in civil engineering applications.