Machine Learning-Enhanced Analysis of Small-Strain Hardening Soil Model Parameters for Shallow Tunnels in Weak Soil

Accurate prediction of tunneling-induced settlements in shallow tunnels in weak soil is challenging, as advanced constitutive models, such as the small-strain hardening soil model (SS-HSM) require several input parameters. In this study, a case study was used as a benchmark to investigate the sensitivity of the SS-HSM parameters. An automated framework was developed, and 100 finite-element (FE) models were generated, representing realistic input ranges and inter-parameter relationships. The resulting distribution of predicted surface settlements resembled observed outcomes, exhibiting a tightly clustered majority of small displacements (less than 20 mm) alongside a minority of widely scattered large displacements. Subsequently, machine-learning (ML) techniques were applied to enhance data interpretation and assess predictive capability. Regression models were used to predict final surface settlements based on partial excavation stages, highlighting the potential for improved decision-making during staged excavation projects. The regression models achieved only moderate accuracy, reflecting the challenges of precise displacement prediction. In contrast, binary classification models effectively distinguished between small displacements and large displacements. Arguably, classification models offer a more attainable approach that better aligns with geotechnical engineering practice, where identifying favorable and adverse geotechnical conditions is more critical than precise predictions.