Interpretable Crack-Risk Grading for Tunnel Linings: A Comprehensive Framework

Abstract

The structural integrity of tunnel infrastructures is of paramount importance to global transportation networks. As tunnel systems age, the structural degradation of their concrete linings, primarily manifested through cracking, presents significant safety and economic challenges. While recent advancements in artificial intelligence have substantially improved the automated detection of these anomalies, the prevalent reliance on opaque deep learning architectures poses a critical barrier to their adoption in practical engineering workflows. Domain experts and structural engineers require transparent, explainable decision-making processes to validate safety-critical maintenance interventions. This paper introduces a comprehensive framework for interpretable crack-risk grading in tunnel linings. By synergizing advanced feature extraction techniques with explainable machine learning paradigms, the proposed methodology transforms raw visual and structural data into a multidimensional risk assessment model. The framework evaluates not only the geometric parameters of the cracks, such as width and length, but also contextual structural factors, including moisture ingress and spalling proximity. We present an exhaustive literature review detailing the mechanical origins of lining deterioration and the evolution of monitoring systems. Following this, we delineate the methodological architecture, which prioritizes transparency without significantly compromising predictive accuracy. Extensive empirical analyses demonstrate that the interpretable grading system provides actionable, trustworthy insights for infrastructure management, bridging the gap between algorithmic complexity and practical engineering requirements.