A Framework for Modeling Liquefaction-Induced Road Disruptions After Earthquakes: Implications for Emergency Response and Access in the Cascadia Region of North America

2026-03-16

AI Summary

Traditional regional assessment methods for predicting earthquake-induced road disruptions are overly simplistic and lack a mechanical basis, limiting their utility for emergency response and transportation planning. This study proposes an integrated framework that combines mechanistic understanding with data-driven approaches, uniquely coupling high-resolution (90 m) liquefaction severity predictions with empirical fragility curves to enable spatially explicit modeling of segment-level disruption probabilities. Risk propagation across the national highway network is simulated using spatially correlated Monte Carlo methods. Under a magnitude 9 Cascadia earthquake scenario, high-risk zones are identified along coastal lowlands, river valleys, and waterfront urban areas, with critical corridors such as US-10itudinal Highway 101 facing severe disruptions. In several Washington counties, limited network redundancy leads to substantially reduced hospital accessibility, disproportionately affecting socioeconomically vulnerable populations.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Transportation
• Reasoning under Uncertainty: Applications

Application Category

• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

Large earthquakes along the Cascadia Subduction Zone (CSZ) are expected to trigger widespread soil liquefaction that could disrupt transportation systems across the U.S. Pacific Northwest. However, past regional assessments have relied on simple geologic screening methods and binomial shaking thresholds that are only loosely informed by liquefaction science. This study introduces a mechanics-informed, data-driven framework for estimating liquefaction-induced road closures and service reductions, and the framework is applied to a magnitude-9 CSZ earthquake. Predicted liquefaction severity is translated into segment-level probabilities of closure and reduced service using empirically derived fragility relationships. These probabilities are mapped at 90-m resolution and propagated through the National Highway System using a spatially correlated Monte Carlo simulation to estimate link-level disruption. Results show that impacts are concentrated in low-lying coastal zones, river valleys, and urban waterfronts, with major disruptions expected along critical routes including U.S. Route 101. Local mobility is further examined in Pacific and Grays Harbor counties, Washington, where limited network redundancy, strong shaking, and high liquefaction susceptibility lead to elevated probabilities of isolation and loss of hospital access. Socioeconomic analysis reveals modest but statistically significant associations between road impacts and demographic indicators, suggesting that liquefaction impacts may compound with existing social vulnerabilities. While not a substitute for site-specific analysis, the results provide a regional baseline for emergency planning, risk communication, and prioritization of more advanced geotechnical sampling and analysis. Moreover, the methodology proposed here is not specific to the CSZ, but rather, could be applied to analogous studies of road impacts elsewhere.

Problem

• liquefaction
• road disruption
• earthquake
• transportation system
• Cascadia Subduction Zone

Innovation

• liquefaction-induced road disruption
• mechanics-informed framework
• spatially correlated Monte Carlo simulation
• fragility relationships
• transportation network vulnerability