Establishing highway bridge network resilience against multi-hazards

Climate change presents a pressing challenge for natural disaster management, to quantify its effects and associated disasters is a persistent challenge for regional climate risk studies. As climate-induced hazards escalate in intensity and frequency, infrastructure in hazard-prone regions faces growing risks – A situation especially critical to transportation infrastructures. Recent events, such as Hurricane Helene in 2024, which caused widespread damage to life supporting infrastructures and roadways closures, underscore the urgency of addressing these combined hazards. This dissertation assesses multi-hazard risks to bridge infrastructure in North Carolina’s mountainous regions, focusing on the interplay between landslide, flooding, wildfire, and earthquake risks. We approach the multi-hazard issue using landslide as the basic quantifier and investigate the nesting effect of earthquake and rainfall triggered landslides.
Because forest fire has the potential of diminishing soil moisture and can encourage landslides, wildfire risk is also included as a predictor. Analysis identifies key wildfire-related variables, such as distance to roads, elevation, and proximity to populated areas, as significant predictors of landslide susceptibility, highlighting the role of remote sensing data in extreme weather event prediction. Soil type, included in the landslide model, had limited impact, suggesting the need for refined soil classification methods in future studies.
Utilizing logistic regression (LR) and random forest (RF) models, this study develops predictive maps for landslide and wildfire susceptibility, achieving accuracy rates of 75.7% and 83.9% for landslide prediction and 68.5% and 72.9% for wildfire prediction, respectively. The higher sensitivity of the RF model, as shown in ROC curve analysis, demonstrates its effectiveness for multi-hazard risk modeling.
The wildfire susceptibility map is then incorporated as an independent variable in predicting landslide occurrences, revealing critical interactions between wildfire and landslide risks. The result are two different landslide susceptibility maps. Finally, a novel index, the Assumed Flooding Potential (AFP), is introduced to quantify flood risk. Since it is hard to establish flooding scenarios for bridges in mountain regions. AFP is calculated as the mid-span clearance for bridges. Furthermore, bridges-in-valleys are identified for high flooding risk analysis.
The integration of multi-hazard data allows for a dynamic understanding of bridge vulnerability, resulting in a shift in risk probability for certain structures. Specifically, the number of bridges with over a 50% probability of multi-hazard risk exposure decreased from 47 to 26, while four new bridges emerged in high-risk zones due to the addition of wildfire susceptibility data. These findings provide actionable insights for decision-makers, enabling proactive mitigation strategies tailored to bridges that face increased vulnerability from wildfire-triggered landslides.
This research delivers a high-resolution multi-hazard risk map and model for infrastructure resilience planning, offering critical tools for bridge engineers and policymakers. The 2024 Hurricane Helene landslides and bridge damage data from the state have been used to validate the risk maps. The results indicated reasonable accurate predictions, thus, ascertaining the study contributed to the potential to anticipate future multi-hazard risks. However, it also highlighted the need to address the complex interactions between environmental and anthropogenic factors and the urgency for future studies to advance our understanding of climate effects and to enhance our ability to anticipate and mitigate multi-hazard impacts on critical infrastructure in the face of evolving climate challenges.