Artificial Intelligence for Bearing Capacity Evaluation of Shallow Foundation: an Overview

The investigation of the ultimate bearing capacity (UBC) of shallow foundations has consistently been a significant area of study within the realm of geotechnical engineering. Estimating the UBC is a challenging and intricate task due to the influence of various factors, including soil qualities, foundation depth, and shape. Consequently, this subject gained considerable interest from researchers during the past century. Due to the rapid advancement of AI techniques, numerous models have been effectively utilized in foundation engineering, resulting in a significant rise in the number of related research publications. Despite considerable advancement in recent years, there is still a lack of a comprehensive overview of this topic. In order to comprehensively summarize the most recent developments and offer insights into future investigations, the objective of this review is to present a detailed analysis of AI applications in the evaluation of UBC of shallow foundations. By analyzing the recently published articles concerning the application of AI techniques in UBC prediction, the advantages and disadvantages of the established techniques are clearly outlined. According to the investigation, the choice of input parameters and the quantity and types of datasets used in AI systems determine their accuracy and success. Upon analysis of the considered publications, it was observed that the utilization of AI techniques for evaluating the UBC of shallow foundations has produced acceptable and encouraging outcomes.