Deep Learning for Accelerated Reliability Analysis of Infrastructure Networks

TL;DR

This paper introduces a deep learning framework that significantly accelerates the reliability analysis of infrastructure networks, especially under natural disaster scenarios, by using neural network surrogates to replace computationally intensive Monte Carlo simulations.

Contribution

The paper develops two neural network surrogates for rapid infrastructure reliability assessment, reducing computational costs while maintaining high accuracy, applicable to large-scale systems.

Findings

Deep neural network surrogates achieve high prediction accuracy.

The approach significantly reduces analysis time.

Effective for large infrastructure systems under disaster scenarios.

Abstract

Natural disasters can have catastrophic impacts on the functionality of infrastructure systems and cause severe physical and socio-economic losses. Given budget constraints, it is crucial to optimize decisions regarding mitigation, preparedness, response, and recovery practices for these systems. This requires accurate and efficient means to evaluate the infrastructure system reliability. While numerous research efforts have addressed and quantified the impact of natural disasters on infrastructure systems, typically using the Monte Carlo approach, they still suffer from high computational cost and, thus, are of limited applicability to large systems. This paper presents a deep learning framework for accelerating infrastructure system reliability analysis. In particular, two distinct deep neural network surrogates are constructed and studied: (1) A classifier surrogate which speeds up the connectivity determination of networks, and (2) An end-to-end surrogate that replaces a number of components such as roadway status realization, connectivity determination, and connectivity averaging. The proposed approach is applied to a simulation-based study of the two-terminal connectivity of a California transportation network subject to extreme probabilistic earthquake events. Numerical results highlight the effectiveness of the proposed approach in accelerating the transportation system two-terminal reliability analysis with extremely high prediction accuracy.