Typhoon risk and climate-change impact assessment for cultural heritage asset roofs

Recent catastrophic events in Southeast Asia have emphasized that roofs made of wood/steel frames and lightweight metal roofing sheets are the most vulnerable component in the building envelope when subjected to typhoon-induced wind uplift. This also applies to aging cultural heritage (CH) assets, which deserve special consideration because of their intangible value for local communities, and their essential role for inclusive and sustainable socio-economic development through cultural tourism. This paper introduces a simulation-based framework for fragility analysis and typhoon risk assessment of CH-asset roofs. Fastener pullout and roof-panel pullover are explicitly considered in the proposed framework to model the progressive failure of the roof system. A simplified roof geometry is assumed, requiring limited information about the structure under investigation and low computational resources. Such a low computational burden allows modeling wind-induced demands and component capacities probabilistically as well as considering the effects of load redistributions due to fastener failure and fastener/roof-panel corrosion. Variance-based sensitivity analysis (i.e., Sobol’ indices) based on polynomial chaos expansions of the limit state function is also performed, highlighting the parameters most affecting typhoon-fragility variance and then requiring special attention during data collection. Climate-change impact on the typhoon risk estimates is finally investigated through the use of various scenarios and a time-dependent function modifying the wind hazard profile of the site where the assets of interest are located. The proposed framework is applied to 25 CH assets in Iloilo City, Philippines. The required input data was collected through rapid visual surveying combined with new technologies, such as drones. It is shown that the proposed framework can be adopted in practice for both risk prioritization at a building-portfolio level and simplified risk assessment at a building-specific level

Typhoon fragility analysis and climate change impact assessment of Filipino cultural heritage asset roofs

Cultural Heritage (CH) assets are especially vulnerable to natural hazards (e.g., earthquake-induced ground shaking, typhoon-induced strong wind, and flooding) due to the lack of hazard-resistant features and to aging-induced extensive structural degradation. These considerations, together with their high historical/cultural value, justify the prioritization/implementation of disaster risk reduction (DRR) and resilience-enhancing strategies for the preservation of such assets. This paper proposes a probabilistic, simulation-based framework for the derivation of wind fragility relationships for CH roofs. Roof-panel pullout and pullover failure modes are used to model the progressive failure of the roof system, thus enabling the integration of fastener corrosion effects and load redistribution into the proposed fragility model. Monte-Carlo sampling is used to propagate the uncertainties related to wind-induced demands and roof component (i.e., fasteners and panels) capacities. Climate projections are used to assess the impact of climate change on wind hazard variations, and ultimately on the asset wind risk profile over time. An illustrative application of the proposed procedure is presented with reference to 25 heritage buildings in Iloilo City, Philippines

New combinations for two hybrids in Salvia subg. Rosmarinus (Lamiaceae)

New combinations for two hybrids in Salvia subg. Rosmarinus (Lamiaceae

A multi-fidelity Bayesian framework for robust seismic fragility analysis

Fragility analysis of structures via numerical methods involves a complex trade-off between the desired accuracy, the explicit consideration of uncertainties (both epistemic and aleatory) related to the numerical structural model and the available computational performance. This paper introduces a framework for deriving numerical fragility relationships based on multi-fidelity non-linear models of the structure under investigation and response-analysis types. The proposed framework aims to reduce the computational burden while achieving a desired accuracy of the fragility estimates without neglecting aleatory and epistemic uncertainties. The proposed approach is an extension of the well-known robust fragility (RF) analysis framework. Different model classes, each characterised by increasing refinement, are used to define multi-fidelity polynomial expansions of the fragility model parameters. Each analysis result is then considered as a ‘new observation’ in a Bayesian framework and used to update the coefficients of the polynomial expansions. An adaptive sampling algorithm is also proposed to futher improve the performance of the multi-fidelity framework. Specifically, such an adaptive sampling algorithm relies on partitioning the sample space and the Kullback–Leibler divergence to find the optimal sampling path. The sample space partitioning allows an analyst to specify different criteria and parameters of the algorithm for different regions, thus further improving the performance of the procedure. The proposed approach is illustrated for an archetype reinforced concrete (RC) frame for which two model classes are developed/analysed: the simple lateral mechanism analysis (SLaMA), coupled with the capacity spectrum method, and non-linear dynamic analysis. Both model classes involve a cloud-based approach employing unscaled real (i.e. recorded) ground motions. The fragility relationships derived with the proposed procedure are finally compared to those calculated by using only the most advanced/high-fidelity (HF) model class, thus quantifying the performance of the proposed approach and highlighting further research needs

A simulation‐based framework for earthquake risk‐informed and people‐centred decision making on future urban planning

Numerous approaches to earthquake risk modelling and quantification have already been proposed in the literature and/or are well established in practice. However, most of these procedures are designed to focus on risk in the context of current static exposure and vulnerability, and are therefore limited in their ability to support decisions related to the future, as yet partially unbuilt, urban landscape. We propose an end-to-end risk modelling framework that explicitly addresses this specific challenge. The framework is designed to consider the earthquake (ground-shaking) risks of tomorrow’s urban environment, using a simulation-based approach to rigorously capture the uncertainties inherent in future projections of exposure as well as physical and social vulnerability. The framework also advances the state-of-practice in future disaster risk modelling by additionally: (1) providing a harmonised methodology for integrating physical and social impacts of disasters that facilitates flexible characterisation of risk metrics beyond physical damage/asset losses; and (2) incorporating a participatory, people-centred approach to risk-informed decision making. The framework is showcased using the physical and social environment of an expanding synthetic city. This example application demonstrates how the framework may be used to make policy decisions related to future urban areas, based on multiple, uncertain risk drivers

Urban growth modelling and social vulnerability assessment for a hazardous Kathmandu Valley

In our rapidly urbanizing world, many hazard-prone regions face significant challenges regarding risk-informed urban development. This study addresses this issue by investigating evolving spatial interactions between natural hazards, ever-increasing urban areas, and social vulnerability in Kathmandu Valley, Nepal. The methodology considers: (1) the characterization of flood hazard and liquefaction susceptibility using pre-existing global models; (2) the simulation of future urban built-up areas using the cellular-automata SLEUTH model; and (3) the assessment of social vulnerability, using a composite index tailored for the case-study area. Results show that built-up areas in Kathmandu Valley will increase to 352 km2 by 2050, effectively doubling the equivalent 2018 figure. The most socially vulnerable villages will account for 29% of built-up areas in 2050, 11% more than current levels. Built-up areas in the 100-year and 1000-year return period floodplains will respectively increase from 38 km2 and 49 km2 today to 83 km2 and 108 km2 in 2050. Additionally, built-up areas in liquefaction-susceptible zones will expand by 13 km2 to 47 km2. This study illustrates how, where, and to which extent risks from natural hazards can evolve in socially vulnerable regions. Ultimately, it emphasizes an urgent need to implement effective policy measures for reducing tomorrow's natural-hazard risks

Advancements in multi-rupture time-dependent seismic hazard modeling, including fault interaction

Several recent earthquake events (e.g., 2008 moment-magnitude (M_{W}) 8.0 Wenchuan, China; 2016 M_{W} 7.8 Kaikōura earthquake, New Zealand; 2019 M_{W} 6.4–7.1 Ridgecrest sequence, USA) have emphasized the need to explicitly account for fault sources in probabilistic seismic hazard analysis (PSHA). Fault-based PSHA currently involves a number of significant but necessary modeling assumptions that mainly relate to fault segmentation, multi-segment event occurrence, long-term fault interaction, and time-dependent/independent earthquake recurrence. Each of these issues is typically investigated in isolation, neglecting the implications of their dependencies. This study offers a review of the current literature on fault-based PSHA, unifying state-of-the-art advances in the field within a single harmonized framework. The framework specifically incorporates some underlying methodologies of the latest Uniform California Earthquake Rupture Forecast (UCERF3; Field et al., 2014), providing a comprehensive means of relaxing fault segmentation, accounting for multi-segment ruptures in a standardized way, interpreting available fault data (e.g., slip rates and paleoseismic data) consistently, and inferring time-dependent probabilities of mainshock occurrence. The proposed framework also explicitly accounts for fault-interaction triggering between major known faults, using the approach outlined by Mignan et al. (2016) and Toda et al. (1998). A simple case study is established to demonstrate the framework's capabilities and limitations, involving a holistic investigation of the aforementioned modeling assumptions' effect on the seismic hazard estimates. The main findings of this study are (1) the ground-motion amplitude estimates can change significantly (for certain return periods) depending on the segmentation assumptions used (e.g., strict segmentation or relaxed segmentation, excluding multi-segment ruptures); (2) considering an ensemble of faults with a time-dependent occurrence model changes the shape of the hazard curve with respect to the time-independent assumption; (3) faults with the largest contribution to the hazard can differ between the time-dependent and time-independent cases; and (4) accounting for fault interaction may change the hazard estimates with respect to those obtained using classic time-dependent analysis (for which fault interaction is neglected). The framework provides a clear means of leveraging paleoseismic campaigns and slip rate data collections to potentially better constrain hazard estimates

Comparing the Performance of Regional Earthquake Early Warning Algorithms in Europe

Several earthquake early warning (EEW) algorithms have been developed worldwide for rapidly estimating real-time information (i.e., location, magnitude, ground shaking, and/or potential consequences) about ongoing seismic events. This study quantitatively compares the operational performance of two popular regional EEW algorithms for European conditions of seismicity and network configurations. We specifically test PRobabilistic and Evolutionary early warning SysTem (PRESTo) and the implementation of the Virtual Seismologist magnitude component within SeisComP, VS(SC), which we use jointly with the SeisComP scanloc module for locating events. We first evaluate the timeliness and accuracy of the location and magnitude estimates computed by both algorithms in real-time simulation mode, accounting for the continuous streaming of data and effective processing times. Then, we focus on the alert-triggering (decision-making) phase of EEW and investigate both algorithms’ ability to yield accurate ground-motion predictions at the various temporal instances that provide a range of warning times at target sites. We find that the two algorithms show comparable performances in terms of source parameters. In addition, PRESTo produces better rapid estimates of ground motion (i.e., those that facilitate the largest lead times); therefore, we conclude that PRESTo may have a greater risk-mitigation potential than VS(SC) in general. However, VS(SC) is the optimal choice of EEW algorithm if shorter warning times are permissible. The findings of this study can be used to inform current and future implementations of EEW systems in Europe