Load–Settlement Modeling of Micropiled Rafts in Cohesive Soils Using an Artificial Intelligence Technique

The traditional design of foundations in soft clay often relies on large-diameter piles, which, although effective, are costly and impractical for low- to medium-rise buildings. Micropiles have emerged as a cost-effective alternative, offering an efficient solution to these challenges. To advance the adoption of micropiles in geotechnical practice, this study employs a multi-objective genetic algorithm-based evolutionary polynomial regression (EPR-MOGA), a hybrid artificial intelligence method, to develop a robust and straightforward model for predicting the load–settlement response of micropiled rafts in cohesive soils under vertical loads. The model was created using an extensive database comprising 458 data points derived from field tests, centrifuge experiments, laboratory studies, and numerical simulations reported in the literature. This comprehensive database covers a wide range of scenarios by varying key parameters of micropiles within a group, including their length, diameter, number, spacing, construction method, and raft thickness. The proposed EPR model could deliver accurate predictions, providing a practical approach for geotechnical applications. In addition, the predictions of the model could support the conclusion that pressure-grouted micropiles are more efficient than gravity-grouted ones in enhancing the performance of micropiled rafts.