Search

Before the anticipated Nankai Trough earthquake, the effects of multiple cascading hazards on bridge performance and the associated functionalities of road networks must be assessed to develop recovery strategies. This paper presents a framework for assessing the probabilistic connectivity of road networks exposed to both ground motion and tsunami, considering the spatial correlations among hazard intensities. To evaluate the joint probabilities of bridge states (i.e., passable and impassable states) and the probabilistic connectivity of road networks, the total probability theorem was used to integrate spatially correlated seismic and tsunami hazard assessments into the fragility estimates that consider the cascading effects of ground motion and tsunami-induced damages to bridges. For illustrative purposes, the probabilistic connectivity of hypothetical road networks in Japan that could be affected by both ground motion and tsunami during the anticipated Nankai Trough earthquake was assessed using the proposed method. The results showed that seismic retrofitting of bridges located along the route where the probability of impassable states due to tsunami is very low can enhance the probabilistic connectivity of entire road networks. [ABSTRACT FROM AUTHOR]

Compound flood disaster risks are increasing due to multiple river floods within a single storm event. Many studies targeted dependence structure based on multivariate extreme value theory; however, fewer studies focused on the sum of subset joint probabilities (SSJP), defined as the probability that any combination of rivers are flooded over the target area as an integral index of compound flooding risks. Modeling multivariate extremes at high dimensions faces two challenges: model complexity and sample size. In this study, as a classical asymptotic dependence model, the Hüsler–Reiss (HR) model was explored to resolve the former issue owing to its simplicity. From the multivariate HR model, any subset joint probability is explicitly obtained without numerical integration of angular measure, and dependence parameters are constructed from only pairwise parameters. The latter challenge was addressed using a large ensemble (50 members of 60-year simulation) of the database for policy decision-making for future climate change (d4PDF), which is consequently regarded as the annual maximum flow data of 3,000 years. This study analytically derived SSJP based on the HR model and tested its applicability using annual maximum flow data simulated from d4PDF for 20 rivers in Kyushu Island, Japan. The simulated SSJP based on the HR model and empirical SSJP were compared as the probability plot of the number of river basins where peak discharge exceeds the design level. As a result, under the constraint of HR that the partial correlation must range from −1 to 1, the estimated SSJP by the HR model was in agreement with the empirical SSJP. The case study presents promising results of the proposed HR model–based approach. [ABSTRACT FROM AUTHOR]