Mojtaba Harati

Earthquake resilience is increasingly a problem of interactions across hazards, assets, infrastructure systems, and recovery processes

Tsunami fragility modeling plays a central role in probabilistic coastal risk assessment; however, representing structural vulnerability under near-field tsunami conditions remains challenging due to complex hydrodynamic loading, strong spatial variability, and the presence of pre-existing earthquake damage. This paper advances near-field tsunami fragility modeling through three specific contributions, each bridging simulation-based methods and empirical damage survey observations. First, it demonstrates how a successive earthquake–tsunami simulation framework can generate conditional fragility surfaces that explicitly account for pre-existing seismic damage without relying on statistically intractable probabilistic decompositions. Second, it develops and validates a distance-dependent intensity-shifting approach—derived from analysis of the 2011 Great East Japan tsunami survey dataset—that adapts baseline fragility curves to near-field and near-coast conditions in a physically interpretable and practically deployable manner. Third, it establishes an explicit cross-validation pathway between simulation-derived fragility surfaces and empirical damage observations through machine-learning-assisted feature importance analysis, a connection largely absent from prior literature. Together, these contributions provide a physically consistent and data-informed foundation for near-field tsunami fragility modeling that is directly applicable—as a methodological framework—to loss and resilience estimation platforms such as IN-CORE and HAZUS and to risk-informed coastal infrastructure design in subduction-zone regions, subject to typology-specific calibration; the simulation results are demonstrated for a US Reinforced Concrete (RC) moment-frame archetype and the empirical results for Japanese wood-frame construction, so direct quantitative application to other structural typologies requires recalibration of the respective model components.

Tsunami fragility modeling plays a central role in probabilistic coastal risk assessment; however, representing structural vulnerability under near-field tsunami conditions remains challenging due to complex hydrodynamic loading, strong spatial variability, and the presence of pre-existing earthquake damage. This paper provides a compre-hensive review and synthesis of current approaches for modeling near-field tsunami impacts on infrastructure, with a particular focus on bridging simulation-based meth-ods and empirical damage survey observations. The discussion highlights how succes-sive hazard simulations can be used to capture coupled earthquake–tsunami effects, while damage surveys offer critical insights into observed relationships between structural damage, hydrodynamic intensity measures, and spatial characteristics such as coastal proximity. Special attention is given to the role of momentum flux as a physically meaningful predictor of damage and to the systematic differences between near-field and far-field fragilities. Building on these insights, the paper outlines practical strategies for adapting baseline fragility relationships to near-field conditions, including the use of spatially dependent intensity adjustments informed by empirical data. Rather than proposing a single methodology, this work aims to provide a structured perspec-tive on existing knowledge and to guide researchers and practitioners in developing more physically consistent and data-informed fragility models for near-field tsunami risk and resilience assessments.

Flood hazard mitigation programs increasingly rely on property buyouts and home elevation, yet participation remains sensitive to program design details that affect household constraints. This study estimates homeowner preferences for buyout and elevation program attributes using a stated-preference discrete-choice experiment administered to N = 1560 homeowners, in which each respondent completed up to 4 choice tasks with 3 alternatives (Buyout, Elevation, and Neither). Choices are modeled in a random-utility framework with a multinomial logit as the primary specification and a mixed logit as a robustness specification. Observed choices favor Buyout (51.2%) over Elevation (29.6%) and the status quo (19.2%). In the estimated utility model, higher buyout offers increase acceptance, longer payment delays significantly reduce acceptance, and longer time to vacate increases acceptance; the acquisition option feature also raises buyout utility. These timing effects imply economically meaningful offer-equivalent tradeoffs: at representative baselines, an additional month of payment delay requires approximately a 6.45 percentage-point increase in offer (as a share of home value) to maintain acceptance, while households would trade about 8.02 percentage points of offer to obtain one additional month to vacate. Heterogeneity results indicate lower baseline participation among low-income respondents and attenuated marginal benefits of longer vacate time among respondents reporting damage. Respondent-level cross-validation shows stable predictive performance and similar accuracy across MNL and mixed logit models. The results highlight that accelerating payments and offering flexible time to vacate can increase program uptake, and that complementary supports may be needed to reduce participation barriers for economically vulnerable households.

This paper presents a probabilistic methodology for generating fragility surfaces for low- to mid-rise steel moment-resisting frames (MRFs) under fire-following-earthquake (FFE). The framework integrates nonlinear dynamic seismic analysis, residual deformation transfer, and temperature-dependent fire simulations within a Monte Carlo environment, while explicitly accounting for uncertainties in structural properties, ground motions, and fire simulation. A fiber-based modeling strategy is employed, combining temperature-sensitive steel materials with fatigue and fracture wrappers to capture cyclic deterioration and abrupt failure. This formulation yields earthquake-only and fire-only fragility curves along the surface boundaries, while interior points quantify the joint fragility response under sequential hazards. The methodology is benchmarked against a machine learning (ML) synthesis framework originally developed for earthquake–tsunami applications and extended here to FFE. Numerical results for a three-story steel MRF show excellent agreement (R² > 0.95, RMSE < 0.02) between simulated and ML-generated surfaces, demonstrating both the efficiency and hazard-neutral adaptability of the ML framework for multi-hazard resilience assessment.