Failure assessment of defected pipe under strike-slip fault with data-driven models accounting for the model uncertainty

Buried pipeline is threatened from the soil displacement due to earthquake and other causes, which leads to the formation of unexpected external forces such as bending moment. The problem can be worse with the appearance of defects, resulting in the reduction in pipe capacity. The paper focuses on the overall problem of defected pipe crossing the strike-slip fault. A full-scaled FE model can be very complicated with the large-scale and micro-scale levels corresponding to the strike-slip fault and defect on pipe problems, respectively. To ease this difficulty, the macro and micro problems are solved separately with two types of FE models and their corresponding databases. To be specific, one FE model is used for predicting external moment due to strike-slip fault and the other is for predicting the moment capacity of defected pipe. Data-driven models are consequently developed with artificial neural network (ANN) for each database generated from these types of models: ANN1 evaluating moment capacity of defected pipe (R2 is 0.9943 on test set) and ANN2 predicting both moment and axial force appeared in pipe due to strike-slip fault (R-squares are 0.9883 and 0.9929 on test set, respectively). Consequently, the stress–strength analysis for the overall problem is solved. Accounting for the unavoidable uncertainty of the models, the paper proposed an approach which assumes that the actual distribution of residual of a model is equivalent to this of the test set. The distributions of residuals on test set of these ANNs are tested to be normally distributed and generated by the conventional Monte Carlo simulation. To the end, the deterministic problem leads to the failure probability. The proposed framework has been investigated, and the final results on this selective parametric study are reasonable. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.