Data-driven approaches to disaster preparedness: Integrating natural language processing and machine learning for enhanced infrastructure system resilience

Abstract

In an era marked by intensifying disasters caused by natural hazards, improving resilience and preparedness has become increasingly important for safeguarding communities and the critical infrastructures on which they depend. Earthquakes, in particular, pose severe threats to human well-being and the integrity of critical infrastructure systems, such as bridges, which serve as key conduits for transportation, emergency response, and economic continuity during and after crises. The rapid acceleration of data generation presents opportunities and challenges; if effectively leveraged, diverse and complex data sources can support more robust, data-driven decision-making for disaster preparedness. This dissertation presents three analytically rigorous and adaptable frameworks to address key challenges using high-dimensional, real-world data to improve disaster preparedness and infrastructure assessment. This dissertation first presents a text mining framework and an accompanying open-source code designed to extract insights from a novel corpus of practical disaster reports. This chapter highlights the ability to synthesize lessons from past disasters, providing observations that inform future preparedness initiatives. Second, this work introduces a machine learning-based framework for predicting key bridge characteristics related to seismic vulnerability. This framework reduces the need for manual data collection while increasing the availability of network-level characteristics, thus allowing for a more comprehensive understanding of infrastructure resilience. Third, this work proposes an integrated framework that combines natural language processing, feature engineering, and uncertainty quantification to improve the reliability and interpretability of seismic vulnerability assessments based on small, information-rich datasets. Together, these contributions lay the groundwork for future research and practice in disaster resilience, particularly in contexts characterized by limited data availability and high data complexity. By demonstrating how natural language processing and machine learning can be used to harness textual, structured, and high-dimensional data, we aim to advance intelligent, data-efficient methods that support more informed and effective decision-making in disaster preparedness and infrastructure system management.