AI-assisted reliability-based design framework for tunnel concrete linings in weak rocks

Reliability-based design (RBD) of tunnel concrete linings in weak rocks is challenging due to the uncertainty of geomechanical parameters and the ground-support interaction. This study introduces a transparent and efficient framework that integrates three key components: (1) implicit ground-support equilibrium analysis based on the Convergence-Confinement Method (CCM), (2) probabilistic evaluation of the safety factor ([Formula: see text]), reliability index ([Formula: see text]), and failure probability ([Formula: see text]) and (3) a data-driven surrogate model based on artificial intelligence for rapid parametric analysis. The uncertainties of the rock mass and concrete linings are treated through Monte Carlo sampling, and the outcomes are benchmarked against the First-Order Reliability Method (FORM) to verify accuracy and potential bias. The results are transformed into decision-making charts linking the thickness and compressive strength of concrete linings to target [Formula: see text] levels, thus providing risk-consistent objectives instead of a fixed safety factor criterion. Two types of linings are considered: conventional concrete (with an average uniaxial compressive strength of 20 MPa) and fiber-reinforced reactive powder concrete (FRPC, with an average uniaxial compressive strength of 65 MPa, developed by the research team). The findings demonstrate that increasing thickness and material quality significantly reduce [Formula: see text], achieving the reliability thresholds required for final tunnel support (2E-5). This effect is more critical at smaller thicknesses and with lower-quality materials. The use of FRPC also leads to a considerable reduction in [Formula: see text] at intermediate thicknesses, making it an efficient option when construction constraints limit thickness increase. The surrogate model successfully reproduces probabilistic trends with high consistency and slight conservatism. Ultimately, the integrated CCM-reliability-artificial intelligence framework bridges the gap between deterministic design and risk management, delivering economical and resilient tunnel lining designs in weak rocks.