Sensitivity analysis and uncertainty quantification of a seismic risk model for road networks

Abstract

Natural hazards may cause significant disruptions to road infrastructure, subsequently affecting road agencies, users, and productive activities. Despite the existence of infrastructure fragilities to seismic hazard and some operational consequences on network mobility, previous research has not modeled risk in terms of traffic disruptions and consequent travel time delays in subduction environments, analyzing the sensitivity to model parameters and quantified model uncertainty. This study proposes a risk framework to evaluate operational consequences in interurban road networks exposed to seismic hazard using travel time delays and propagate uncertainty in the model. Risk values are evaluated using Monte Carlo simulations, and uncertainty is propagated using a polynomial chaos expansion meta‐model. The framework was applied to a very critical interurban network in central Chile. Results demonstrate that the parameters that most significantly influence risk are fragility, loss of road capacity, and traffic volume.

SIGeR-RV: A Web-Geographic Information System-Based System for Risk Management of Road Networks Exposed to Natural Hazards

“…Figure 6 a shows the simulation of the seismic hazard for the study area carried out by SIGeR-RV. On the other hand, Figure 6 b presents the seismic risk curve developed by Allen et al ( 1 ), developed from the analysis of the software. This example shows the results provided by the computational tool and that it is possible to carry out various analyses that also help to better understand the operation of the networks before the occurrence of a seismic event.…”

“…In addition, this software generates an expected travel time for different seismic scenarios given the recurrence of each one of them. Based on the methodology explained in Allen et al ( 1 ) to quantify risk in transportation networks exposed to seismic hazard, the information provided by this software can be used to generate risk curves as shown in Figure 6 b . This risk approach evaluates the mean annual frequency of exceedance of a decision variable (in this case travel time delays) given a set of seismic scenarios (uploaded in this software), fragility of different transportation assets, and a traffic assignment model to estimate the new trip patterns.…”

“…Because of this phenomenon, high expected travel times for Santiago-Valparaı´so are reached with less frequent earthquakes than those required for Santiago-San Antonio. For instance, as shown in Allen et al (1), expected travel time of 5 h is reached with a mean annual frequency of 3.56 3 10 23 for Santiago-San Valparaı´so and 5.54 3 10 23 for Santiago-San Antonio; these correspond to return periods of 280 and 180 years, respectively.…”

“…In addition, road networks enable access to emergency services in critical infrastructures such as hospitals or fire stations. Risk management practices mainly study these networks from a physical perspective using the damage probability of a road estimated with regard to its fragility and hazard exposure ( 1 – 3 ). The expected losses are commonly estimated in damage to infrastructure and operational failures.…”

Systemic reliability of bridge networks with mobile sensing‐based model updating for postevent transportation decisions

“…Structural reliability assessment aims to determine the probability of failure in different engineering systems given a certain demand and capacity (Dai & Cao, 2017; Dai et al., 2012). It is also extended to other coupled systems, such as bridge‐network performance assessment (Allen et al., 2021; Nabian & Meidani, 2018). There is also a significant investment made in utilizing machine learning models for structural damage detection and reliability analysis.…”