Work

Deep Learning for Accelerated Seismic Reliability Analysis of Transportation Networks

Year: 2018

Type: article

Abstract: To optimize mitigation, preparedness, response, and recovery procedures for infrastructure systems, it is essential to use accurate and efficient means to evaluate system reliability against probabilistic events. The predominant approach to quantify the impact of natural disasters on infrastructure systems is the Monte Carlo approach, which still suffers from high computational cost, especially when applied to large systems. This article presents a deep learning framework for accelerating seismic reliability analysis, on a transportation network case study. Two distinct deep neural network surrogates are constructed and studied: (1) a classifier surrogate that speeds up the connectivity determination of networks and (2) an end-to-end surrogate that replaces modules such as roadway status realization, connectivity determination, and connectivity averaging. Numerical results from k-terminal connectivity analysis of a California transportation network subject to a probabilistic earthquake event demonstrate the effectiveness of the proposed surrogates in accelerating reliability analysis while achieving accuracies of at least 99%.

Source: Computer-Aided Civil and Infrastructure Engineering

DOI: 10.1111/mice.12359

Authors: Mohammad Amin Nabian, Hadi Meidani

Institution: University of Illinois Urbana-Champaign

Language: English

Topic: Infrastructure Resilience and Vulnerability Analysis

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: hybrid

Estimated APC paid: $5,230