CAREER: Toward the Next Generation Data-Informed Probabilistic Framework for Infrastructure Seismic Risk Assessment and Uncertainty Quantification

This Faculty Early Career Development (CAREER) award will support research that seeks to (1) establish a methodological and computational framework to harness new knowledge from extensive simulation data for extreme yet plausible natural disaster scenarios and (2) seamlessly integrate it into probabilistic demand and hazard models. Conventional risk assessment approaches rely on the combined use of hazard models and likelihood of damage (i.e., fragility) models. By nature, both models make generalizations starting from the data utilized to develop them. However, extrapolation beyond available empirical data incurs large uncertainties and the chance of misestimating risk for extreme events. The progressive integration of high-performance computing architectures into civil engineering domains helps researchers to simulate all conceivable disaster scenarios. While this is creating extensive catalogs of data, a systematic methodology to extract and apply this knowledge to a cross-disciplinary framework, one that allows for continuous updates as more data become available, is missing. This research will attempt to lay the foundation for the next generation of data-informed probabilistic methods that can reliably assess infrastructure risks posed by extreme seismic loads, which are less understood but highly destructive. The educational component will operate at multiple interconnected levels by incorporating key research outcomes into various educational and K-12 outreach activities and promoting engagement with industry professionals. This award will contribute to the National Science Foundation (NSF) role in the National Earthquake Hazards Reduction Program (NEHRP) 2022-2029 Strategic Plan. The specific objectives of this research are to (i) exploit the potential of machine learning-inspired regression algorithms to map hidden correlations between forcing functions and above and below ground infrastructure response to develop new probabilistic demand metamodels, (ii) create open-source analytical tools to update hazard functions through the convolution principle with complete characterization of uncertainties, and (iii) enable selection of site and application-specific loads to support infrastructure design and assessment. This research intends to answer key fundamental questions, including (i) how newly available simulation data can enable the identification of stable trends traditionally lost in the fog of large uncertainties of sparse information for extreme events and inform the development of new demand models, (ii) how progressive integration of simulation capabilities and machine-learning regression techniques can improve the generalizability of risk assessment methods across multiple hazards and infrastructure, and (iii) how current performance-based approaches can be reliably utilized for extreme yet likely hazard scenarios. This research will foster the theory and practice of resilience engineering, contributing to the general field of risk assessment and mitigation of the adverse effects of natural hazards. Project data will be archived and made publicly available in the NSF-supported Natural Hazards Engineering Research Infrastructure (NHERI) Data Depot (https://www.DesignSafe-ci.org). This project is jointly funded by NSF's Engineering for Civil Infrastructure (ECI) program and NSF's Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.