ML-Based Fragility and Functional Integrity Analysis of Corroded Buried Pipelines’ Seismic Response to Combined Shaking and Fault Displacement

Buried pipelines with corrosion defects crossing reverse faults exhibit complex seismic responses under the combined effects of ground motion and fault displacement. Traditional finite-element analysis is computationally inefficient to comprehensively address such problems. This paper proposes a backpropagation neural network (BPNN)-based method for seismic response prediction and fragility assessment. A three-dimensional finite-element model is first employed to analyze the effects of corrosion depth-to-thickness ratio, diameter-to-thickness ratio, internal pressure, and burial depth on the axial compressive strain of the pipeline. Consequently, a BPNN model is constructed with these parameters, along with fault displacement, as inputs with the peak compressive strain as the output. The BPNN model demonstrated excellent predictive performance, with a maximum prediction error below 15%. The incremental dynamic analysis (IDA) method is then applied to map strength and damage indices of the pipeline, enabling quantitative evaluation of its failure probability and functional integrity under various conditions. It is found that higher diameter-to-thickness ratio (D/t) corresponds to a higher likelihood of the pipeline reaching adverse performance levels; this is also accompanied by a reduction in functional integrity. Specifically, as D/t increases from 72 to 144, the probability of pipe wall damage and the risk of transmission function loss rise significantly, highlighting the pronounced fragility of thin-walled pipelines subjected to fault movement. Moreover, corrosion defects exacerbate pipeline fragility: a corrosion depth equivalent to 10% of the wall thickness substantially amplifies strain responses, resulting in an approximately 80% probability of moderate damage, while a corrosion depth of 40% elevates the probability of severe damage beyond 60%.