A framework for estimating the matric suction in unsaturated soils using multiple artificial intelligence techniques

Implementation of the state-of-the-art understanding of the mechanics of unsaturated soils into geotechnical engineering practice is partly limited due to the lack of quick, reliable, and economical techniques for matric suction measurement. Matric suction is one of the key stress state variables that significantly influences the hydro-mechanical behavior of unsaturated soils. In this paper, to address this objective, two artificial intelligence (AI) models were developed for estimating matric suction in unsaturated soils based on the particle swarm optimization support vector regression (PSO-SVR) and multivariate adaptive regression spline (MARS) algorithms. The results suggest that both these models can reasonably estimate matric suction. Compared to the MARS model, the PSO-SVR model can achieve higher accuracy. Nonetheless, the MARS model facilitates the sensitivity analysis and the selection of essential inputs. A novel integrated framework is proposed and validated, leveraging the strengths, and alleviating the limitations of the PSO-SVR and MARS algorithms for reliable and rapid estimation of matric suction in the range of 0-1500 kPa for low plastic soils (0 < I-p <= 7). Six inputs are required to use this model successfully; some can be measured using conventional laboratory tests, and others can be calculated from mass-volume relationships.