Regional infrastructure resilience assessment and urban planning applications

Infrastructure is fundamental to the functioning of cities, providing essential services that support economic activity, mobility, and public well-being. However, natural disasters can severely disrupt infrastructure systems (e.g., transportation, potable water, and wastewater). Such disruptions may delay emergency response, extend recovery, and increase socioeconomic losses. Understanding infrastructure risk is essential for strengthening urban resilience. At the same time, with the continuous growth of population and expansion of cities, the role of urban planning becomes increasingly essential. Effective land use planning strengthens infrastructure resilience by guiding development patterns, reducing exposure to hazards, and supporting resilience strategies. It also balances differing priorities by integrating stakeholder interests (e.g., economic development) and broader social benefits (e.g., disaster mitigation) into planning decisions. Prediction models play a critical role in decision-making by connecting past events and literature with future outcomes. Such models help to identify trends, forecast potential risks, and inform decision-making for disaster resilience. Thus, understanding model uncertainty is significant for recognizing potential limitations and improving the robustness of decision-making. A systematic validation protocol is necessary for different prediction models and hazard types. Furthermore, to effectively support policy decisions, models must generate accurate assessments of infrastructure risks and system performance. Regional risk and resilience analysis models usually involve comprehensive prediction models, including nested models in complex multi-step procedures. A systematic approach is needed to integrate multi-level dependencies and incorporate heterogeneous data into model updates for the analysis. This dissertation addresses the fundamental challenges and focuses on transportation infrastructure, particularly roads, as an example among various critical infrastructure. The proposed ideas can further be modified and applied to other critical infrastructure systems. The contributions of this dissertation cover four main parts, specifically focusing on (1) modeling of road network disruption risk, (2) modeling of land use optimization for disaster mitigation, (3) model validation for regional risk and resilience analysis, and (4) multi-level model calibration and updating. This dissertation develops a probabilistic formulation using reliability formulation to estimate the probability of road blockage due to building damaged following an earthquake. The proposed model considers the spatial relationship between debris, building damage, road conditions. The road blockage probability at a given road section is estimated for the four road section types, considering buildings on only one side of the road or both sides, and with or without a raised traffic median. This dissertation then proposes a network-based approach for land use optimization. The optimization framework aims to minimize post-disaster accessibility risk while maximizing housing and urban development in the area. The approach integrates the quantification of infrastructure resilience into the spatial optimization process to enhance decision-making. This approach can also be applied to other infrastructure networks. This dissertation proposes three measures to validate the predictive ability of models used in regional risk analysis (i.e., Accuracy Likelihood, Prediction Error, and Distribution Match). Accuracy Likelihood quantifies the probability of observing the recorded data under the predictive model's hypotheses/assumptions. Prediction Error measures the difference between the recorded value and the values predicted by the models. Distribution Match measures the similarity between the probability distributions of the predicted quantities and the corresponding empirical distributions of the recorded data. As an example, we assess the predictive validity of seismic risk and resilience analysis models using data from the 2016 Kumamoto earthquake in Mashiki City, Kumamoto, Japan. This comparison highlights the predictive performance of available models and informs future research on crucial improvements. Finally, the dissertation proposes a probabilistic formulation using the Bayesian approach to update the model parameters and reduce the uncertainties as data becomes available. As an example, this dissertation applies the proposed methodology to updates the seismic risk analysis in the HAZUS model using data from the 2016 Kumamoto earthquake in Mashiki, Japan. Three levels of updates (i.e., hazard, vulnerability, and functionality) are applied to the risk analysis models for bridges, potable water infrastructure, and wastewater infrastructure. The proposed methodology enables consistent updates across modeling levels when data becomes available.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Civil Engineering

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Yu, Yun-Chi

Contributors dc:contributor

• Gardoni, Paolo
• Ouyang, Yanfeng
• Lee, Bumsoo
• Vogiatzis, Chrysafis

• Disaster Risk
• Infrastructure Resilience
• Urban planning
• Decision-making

Statement dc:rights

• Copyright 2025 Yun-Chi Yu

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/129535

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F129535