Tsunami hazard and risk assessment for multiple buildings by considering the spatial correlation of wave height using copulas

Abstract

Abstract. It is necessary to evaluate aggregate damage probability to multiple buildings when performing probabilistic risk assessment for the buildings. The purpose of this study is to demonstrate a method of tsunami hazard and risk assessment for two buildings far away from each other, using copulas of tsunami hazards that consider the nonlinear spatial correlation of tsunami wave heights. First, we simulated the wave heights considering uncertainty by varying the slip amount and fault depths. The frequency distributions of the wave heights were evaluated via the response surface method. Based on the distributions and numerically simulated wave heights, we estimated the optimal copula via maximum likelihood estimation. Subsequently, we evaluated the joint distributions of the wave heights and the aggregate damage probabilities via the marginal distributions and the estimated copulas. As a result, the aggregate damage probability of the 99th percentile value was approximately 1.0 % higher and the maximum value was approximately 3.0 % higher while considering the wave height correlation. We clearly showed the usefulness of copula modeling considering the wave height correlation in evaluating the probabilistic risk of multiple buildings. We only demonstrated the risk evaluation method for two buildings, but the effect of the wave height correlation on the results is expected to increase if more points are targeted.

Probabilistic Tsunami Hazard and Risk Analysis: A Review of Research Gaps

“…Risk assessment is not necessarily limited to quantifying the direct and indirect impact on exposed populations and infrastructures. The evaluation of safety and reliability of physical systems is of interest too and for this, fragility functions ("Gaps in Physical Vulnerability" section) can be integrated with hazard to obtain the frequency of exceeding a given damage level (see Figure 1, e.g., Park et al, 2019;Fukutani et al, 2019). The risk metrics provide valuable data also for the assessment of quantitative resilience (also denoted as engineering resilience), which aims to estimate the resilience of a network, an infrastructure, or even an urban ecosystem to a specific natural hazard (see Mebarki et al, 2016 for industrial plants, Akiyama et al, 2020 for bridges).…”

Rapid tsunami force prediction by mode-decomposition-based surrogate modeling

“…This approach is also widely accepted, and several studies have been reported. Among these is a study on making real-time predictions using source information and precomputed tsunami waveform and inundation databases (Gusman et al, 2014), another uses a combination of precomputed tsunami databases and the neural networks (Fauzi and Mizutani, 2019;Liu et al, 2021), and another features the surrogate modeling of numerical simulation results (e.g., Fukutani et al, 2019;Kotani et al, 2020).…”

“…Therefore, a surrogate-modeling-based prediction method is employed in this study. Surrogate modeling has been widely accepted for uncertainty quantification and probabilistic risk assessment, such as studies using response surface (e.g., Fukutani et al, 2019;Kotani et al, 2020), Gaussian process (e.g., Sarri et al, 2012;Salmanidou et al, 2017Salmanidou et al, , 2021, polynomial chaos expansion (e.g., Denamiel et al, 2019;Giraldi et al, 2017;Sraj et al, 2017) and multifidelity sparse grids (e.g., de Baar and Roberts, 2017). These works demonstrate the potential of the surrogate-modeling-based approach, while the surrogate model considering spatiotemporal variation has not been well studied.…”