Comprehensive Design and Structural Analysis of Steel-Fiber-Reinforced Concrete Tunnel Linings for Railway Infrastructure

Abstract

The structural performance and durability of tunnel linings are critical for the safety of modern railway infrastructure. This study presents a comprehensive analysis of steel-fiber-reinforced concrete (SFRC) tunnel linings using a hybrid analytical and data-driven framework. The primary objective is to evaluate the effectiveness of SFRC in enhancing structural behavior under complex loading conditions. An integrated ORCA cyclic generative adversarial monitoring (OCGAM) framework, combining ORCA-based optimization with generative adversarial networks, is proposed to model and predict crack development, stress–strain response, and service life of tunnel linings. The framework incorporates key factors such as train-induced dynamic loads, soil–structure interaction, and seismic effects. Numerical simulations demonstrate that SFRC tunnel linings significantly reduce crack width, improve load-bearing capacity, and enhance durability compared with conventional reinforced concrete. The OCGAM framework further enables efficient prediction of structural responses with higher computational accuracy and reduced error rates than traditional analytical approaches. Although the study is limited to simulation-based validation, the results highlight the potential of combining optimization techniques with machine learning for advanced structural assessment. The proposed methodology provides a valuable tool for the design and monitoring of resilient and sustainable railway tunnel systems, and future work will focus on validation by experiments.