Optimized machine learning-based enhanced modeling of pile bearing capacity in layered soils using random and grid search techniques

The bearing capacity of a pile is a critical factor in geotechnical design, necessitating extensive testing procedures that often increase both the time and cost of earthwork. Consequently, there is a growing demand for efficient and reliable methods to determine pile bearing capacity. This study aims to propose optimized machine learning based models through the application of Random Search (RS) and Grid Search (GS) optimization techniques for the prediction of pile-bearing capacity in layered soils. For this purpose, an extensive dataset is sourced from literature, and various machine learning algorithms including Random Forest (RF), Support Vector Machine (SVM), and XGBoost are investigated. Through a systematic modeling approach, multiple models are generated, and the performance of machine learning algorithms is refined using RS and GS cross validation (CV) using a customized code in Python. Optimized models are further assessed based on comprehensive evaluation criteria using key statistical performance indices. The results demonstrate that both RS and GS-tuned machine learning models achieve high accuracy, with R2 values exceeding 0.9 and a low error index score across testing and training datasets. Notably, GS exhibits slightly superior statistical performance compared to RS. Furthermore, the tuned models with RS and GS showcase high performance on the validation dataset, with GS consistently outperforming RS. XGBoost emerges as the top performer among the machine learning models, followed by RF and SVM, highlighting the efficacy of tree-based algorithms in capturing the geotechnical variability inherent within pile bearing data. The proposed models offer valuable insights for predicting the preliminary evaluation of pile bearing capacity, facilitating swift and cost-effective geotechnical characterization within an acceptable error margin. This study introduces advancements in predictive modeling for geotechnical engineering, highlighting the transformative potential of optimization methodologies to enhance the machine learning models used for decision-making processes in civil engineering applications.