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.
Arun Kumar, Mayengbam Sunil Singh· Transportation Research Reco...· 0 citations
Urban gas pipeline networks generate continuous monitoring time series from pressure sensors, flow meters, valve states, compressor stations, regulator stations, gas concentration sensors, and customer-demand meters. These signals are noisy because of daily demand fluctuation, regulator adjustment, compressor vibration, sensor drift, weather effects, and telemetry packet loss. Leakage, valve malfunction, regulator instability, and abnormal pressure drops may therefore be masked by normal operational noise. This study develops a diffusion-recovered pressure dynamics model for gas pipeline network anomaly detection. The proposed method reconstructs clean pressure-flow trajectories using a conditional diffusion process constrained by pipeline topology and operating states. A disentanglement module separates demand-driven variation, control-operation fluctuation, and fault-related pressure residuals. Experiments are conducted on a gas network dataset containing 1,280 pipeline zones, 5,640 pressure sensors, 930 flow meters, 460 regulator stations, and 31 monitoring variables collected every 30 seconds over 15 months. The dataset contains 864 million timestamped records and 1,960 verified abnormal episodes, including small leakage, regulator oscillation, valve blockage, compressor instability, and abnormal pressure loss. The proposed method shortens median leakage detection delay from 4.6 hours to 47 minutes compared with a Kalman-smoothed recurrent baseline. False dispatch alerts are controlled at 2.0 cases per pipeline zone per quarter. Diffusion recovery reduces normalized pressure reconstruction error from 0.158 to 0.061, and topology-guided denoising restores 23.4 million incomplete pressure windows during evaluation. Full city-level assessment is completed in 13.5 minutes with median scoring latency of 45 ms per zone window. These findings indicate that diffusion-guided signal recovery can improve robust anomaly detection in noisy gas pipeline operation time series.
W. Tan, J. Lim, Arun Kumar· The Journal of Applied Engi...· 0 citations
