The Role of Artificial Intelligence in Geotechnical Engineering: Applications, Optimization Strategies, Modeling Approaches, and Future Prospects

The principal objective of this study is to examine the utilization of Artificial Intelligence (AI) in geotechnical engineering, emphasizing modeling methodologies and prospective forecasts. Geotechnical engineering, which deals with the utilization of soils and rocks in building, often faces complicated behaviors and a high degree of uncertainty in material modeling. Over the past few decades, the integration of AI techniques has gained momentum, giving solutions to forecast sophisticated nonlinear interactions within the field. These AI technologies have emerged as powerful tools in addressing difficult geotechnical challenges, overcoming the limitations of previous methods due to their capacity to capture and anticipate complicated events with precision. As the worldwide impact of climate change continues to grow, the demand for sustainable technology in engineering processes has become more essential. Artificial intelligence offers a means to diminish dependence on conventional laboratory techniques, which frequently generate carbon emissions. AI approaches specifically improve the accuracy and efficiency of earthwork design and construction, thereby diminishing environmental impact by decreasing material usage errors. This paper analyzes several AI techniques-such as Artificial Neural Networks (ANN), Fuzzy Logic, Genetic Programming (GEP), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Analysis of Variance (ANOVA)-and their applications in solving geotechnical and geo-environmental challenges. These new approaches address the limitations of conventional experimental protocols, guaranteeing more sustainable, precise, and environmentally friendly solutions in geotechnical practice. This comprehensive analysis shows the expanding relevance of AI in geotechnical engineering, providing a significant resource for future researchers wanting to leverage intelligent algorithms in enhancing the efficiency, precision, and sustainability of engineering models. By embracing these solutions, the field can drastically reduce the environmental effect associated with traditional geotechnical operations, contributing to a more sustainable and carbon-conscious future