The Uniform California Earthquake Rupture Forecast Version 3 (UCERF3) relaxes fault segmentation, allowing multi-segment and multi-fault ruptures through fault-to-fault "jumps," with lengths up to ...∼1200 km along the San Andreas Fault. Local faults are also highly interconnected, including ruptures on the order of hundreds of kilometers. These prescribed long ruptures did not exist in older models. Longer ruptures produce larger aggregate loss estimates for geographically dispersed assets (portfolios) due to the wider areas that are affected by strong ground shaking. In this study, we model probabilistic earthquake losses of a hypothetical state-wide building portfolio in California. We develop risk deaggregation methods to identify multi-segment and multi-fault ruptures that contribute significantly to high portfolio-wide risks. Three risk measures that are commonly used in risk management decisions are examined: Average Annual Loss (AAL), Return Period Loss (RPLα), and Tail Conditional Expectation (TCEα), for an annual exceedance probability "α," or corresponding return period "1/α." Our results show that while the super long ruptures (>500 km) contribute modestly (∼7%) to the portfolio AAL estimate, they contribute more significantly to portfolio catastrophe risk estimates. Specifically, at a 250 year return period, these long ruptures contribute about 26% and 32% to RPL250 and TCE250 estimates, respectively. At a 500-year return period, the corresponding contributions reach about 35% and 39%. Ruptures that connect complex fault systems are also found to be highly influential to estimated portfolio risks. At a 500-year return period, a mere six rupture groups contribute nearly 70% to the RPL500 estimate. Due to the importance of the UCERF3 model to many risk management and public policy decisions, a critical examination of the limit and uncertainty of fault connectivity and rupture lengths of future earthquakes, as well as their impacts on catastrophe risk assessments, is warranted in future model updates.
In current practice, most earthquake risk models adopt a "declustered" view of seismicity, that is, they disregard foreshock, aftershock, and triggered earthquakes and model seismicity as a series of ...independent mainshock events, whose occurrence (typically) conforms to a Poisson process. This practice is certainly disputable but has been justified by the false notion that earthquakes of smaller magnitude than the mainshock cannot induce further damage than what was caused by the latter. A companion paper makes use of the epidemic-type aftershock sequence (ETAS) model fitted to Central Italy seismicity data to describe the full earthquake occurrence process, including "dependent" earthquakes. Herein, loss estimates for the region of Umbria in Central Italy are derived using stochastic event catalogs generated by means of the ETAS model and damage-dependent fragility functions to track damage accumulation. The results are then compared with estimates obtained with a conventional Poisson-based model. The potential gains of utilizing a model capable of capturing the spatiotemporal clustering features of seismicity are illustrated along with a discussion on the various details and challenges of such considerations.
Probability-based seismic risk and random ground motion theories are used to develop high-precision seismic risk functions and models of regional building portfolios. Seismic intensity measures and ...structural demand parameters are critical in developing seismic fragility models for building clusters. Earthquake damage data indicate that ambient temperature may influence structural fragility, but the effect of temperature on seismic risk is commonly ignored. A method for quantifying seismic intensity is proposed in this study and optimized using 450,000 acceleration records detected by 15 stations during the Luding earthquake in Sichuan, China, on September 5, 2022. Considering the influence of different temperature fields, the proposed seismic intensity quantification method was used to evaluate structural damage during two destructive earthquakes in Xinjiang, China (the Aktao earthquake on September 2, 2003 and the Zhaosu earthquake on December 1, 2003). Three structural empirical seismic risk matrices and cloud maps were developed, and a vulnerability membership curve was generated using fuzzy decision-making, a membership algorithm, and nonlinear regression. A structural seismic risk index function was proposed, and vulnerability cloud diagrams and curves based on actual structural seismic vulnerability datasets were generated. Together, these analyses demonstrated the impact of temperature and intensity measures on the seismic risk to regional buildings. A model was established to evaluate the seismic risk to typical structures and was validated using historical earthquake damage data from China. This method improves the accuracy of earthquake risk and vulnerability assessments for typical building clusters given the influence of temperature. The developed seismic risk prediction model enables the evaluation of vulnerability and resilience of typical structures under low and high temperatures.
This paper illustrates the derivation of an empirical fragility model for residential unreinforced masonry (URM) buildings, calibrated on Italian post-earthquake damage data and compatible with the ...key features of the Italian national seismic risk platform. Seismic vulnerability is described by fragility functions for three vulnerability classes, then refined based on the building height. To this aim, a clustering strategy is implemented to merge predefined building typologies into vulnerability classes, based on the similarity of the observed seismic fragility. On the other side, a specific procedure is built up to determine the vulnerability composition of the exposed URM building stock, starting from national census data. The empirically-derived model was implemented into the national seismic risk platform and used, together with other vulnerability models, for assessing seismic risk in Italy. The results presented in this paper, consisting of refined typological fragility curves and fragility curves for vulnerability classes, can be also exploited for estimating both expected seismic damage and risk in sites with similar seismic hazard and building inventory.
This study integrates welfare, a measure of the impact of road network disruption on individual commuters’ well-being, with a probabilistic seismic risk assessment framework in a computationally ...tractable way. Welfare is a network performance measure that reflects the differential impacts of changes in commute time on various groups. For a case study of the San Francisco Bay Area, welfare loss is computed by augmenting an origin–destination matrix with publicly available information about commuters’ income levels, residences, and workplaces. While commuters from all income groups have similar risk of drivers’ delay due to road network disruption, commuters with low incomes have a substantially higher risk of welfare loss than those with high incomes. A comparison of bridge retrofit policies shows that disaggregation of welfare loss by income group is necessary to examine whether such policies reduce risk equitably. While a retrofit policy determined using drivers’ delay reduces the expected drivers’ delay, it increases the disparity in the per-capita welfare loss of commuters with low and high incomes relative to the network’s baseline state. In contrast, a retrofit policy that prioritizes low-income commuters reduces the difference in welfare loss of commuters with low and high incomes compared to the baseline network state.
•This work quantifies the seismic risk of road networks integrating welfare metric.•Welfare utility quantifies impact of road network disruption across income group.•Low-income commuters have a higher risk of welfare loss than those with high income.•Welfare metric at regional level is not directly function of increase in travel time.•Disaggregation by income group allows analysis of retrofitting policy equitability.
Seismic risk evaluation studies for real estate portfolios conducted by technical professionals (often Civil and Structural Engineers) have become increasingly desirable and common in financial ...decisions. In this article, we develop a series of risk measures and ratings based on common outcomes from probabilistic portfolio seismic risk assessments. We first define two portfolio risk metrics: Portfolio Expected Loss (PELα) and Portfolio Upper Loss (PULα), where "α" is the annual exceedance probability, or the corresponding return period ("1/α"). PULα/PELα ratio characterizes the uncertainty in estimated portfolio risks which results from the uncertainty in seismic performance of the individual assets. Three uncertainty levels are defined, namely, low, medium, and high, based on the PULα/PELα. We then develop an asset risk metric, called Tail Contribution Index (TCIα), that characterizes the contribution of individual assets to the portfolio losses that fall within the high-consequence "tail" of the portfolio loss distribution. To describe the overall engineering efforts of a portfolio seismic risk study, we develop a portfolio risk metric, called Portfolio Level of Investigation (PLIα), that characterizes the effective level of engineering investigation. Three investigation levels are defined: low (desktop), medium (semi-engineered), and high (engineered), based on the PLIα. Finally, based on the combination of uncertainty level and investigation level, we develop a rating scheme by which the quality (Qα) of a portfolio seismic risk study is characterized. Five quality levels are defined: very poor, poor, fair, good, and very good. These risk indices and ratings can help stakeholders and technical professionals better diagnose and communicate portfolio seismic risks, scope adequate studies, effectively utilize valuable resources, and base financial decisions on risk assessment results that have the desired reliability.
Seismic risk estimation for urban building clusters is essential when developing regional seismic resilience models. A masonry structure is a type of building with a large stock, broad applicability, ...and simplified construction technology. The traditional seismic intensity measures and fuzzy quantification of structural vulnerability levels result in low accuracy in estimating and predicting seismic damage to masonry structure clusters. This paper proposes a method for evaluating bivariate seismic intensity measure. Nonlinear dynamic and spectral analyses were conducted on real acceleration records monitored by ten stations during the Wenchuan earthquake in Sichuan, China, on May 12, 2008. The outdated seismic vulnerability level of masonry structures is updated, and an optimized structural seismic risk model based on the seismic damage database of 2407 masonry structures in Dujiangyan city under the influence of the Wenchuan earthquake is established. An innovative seismic risk membership index algorithm was developed, and a vulnerability curve was generated for masonry structures considering multiple risk membership indices. An improved seismic risk index calculation function is proposed using the proposed updated vulnerability level, and the seismic risk index curve and stripe model of typical urban masonry building clusters are established. According to the field investigation of the actual structures in Dujiangyan city affected by the Wenchuan earthquake, the typical failure features and disaster mechanism of masonry structures are reported and analysed, and measures and methods to improve and enhance the seismic capacity of such buildings are proposed. The developed seismic risk model for urban masonry structures can contribute positively to the development of relatively accurate seismic resilience models for urban buildings and can improve the accuracy of seismic risk estimation for masonry structures.
In this paper, empirical fragility curves for reinforced concrete buildings are derived, based on post-earthquake damage data collected in the aftermath of earthquakes occurred in Italy in the period ...1976–2012. These data, made available through an online platform called Da.D.O., provide information on building position, building characteristics and damage detected on different structural components. A critical review of this huge amount of data is carried out to guarantee the consistency among all the considered databases. Then, an in-depth analysis of the degree of completeness of the survey campaign is made, aiming at the identification of the Municipalities subjected to a partial survey campaign, which are discarded from fragility analysis. At the end of this stage, only the Irpinia 1980 and L’Aquila 2009 databases are considered for further elaborations, as fully complying with these criteria. The resulting database is then integrated with non-inspected buildings sited in less affected areas (assumed undamaged), to account for the negative evidence of damage. The PGA evaluated from the shakemaps of the Italian National Institute of Geophysics and Volcanology (INGV) and a metric based on six damage levels according to EMS-98 are used for fragility analysis. The damage levels are obtained from observed damage collected during post-earthquake inspections through existing conversion rules, considering damage to vertical structures and infills/partitions. The maximum damage level observed on vertical structures and infills/partitions is then associated to the whole building. Fragility curves for two vulnerability classes, C2 and D, further subdivided into three classes of building height, are obtained from those derived for specific structural typologies (identified based on building height and type of design), using their frequency of occurrence at national level as weights.
Vulnerability functions relate loss to seismic intensity and can be developed via several approaches. They are a fundamental part of seismic risk assessment on a regional level and support ...decision-making and intervention strategies aimed at reducing risk. This article discusses a prominent analytical approach to developing seismic vulnerability models for buildings based on equivalent single-degree-of-freedom (SDOF) modeling, fragility function, and damage-to-loss model integration. The fundamental assumptions are scrutinized, and their principal drawbacks are highlighted. An alternative approach also based on an equivalent SDOF modeling approach is discussed but instead capitalizes on story loss functions (SLFs) as a means to more accurately compute economic losses and their sources. The main benefit is that the contribution of floor acceleration-based losses can be directly considered, and the disaggregation of losses is fully represented. A case study comparison is presented to highlight the similarities and key benefits. It is seen that the SLF-based approach can provide a much more comprehensive means to compute and communicate loss contributions among different element groups (i.e., structural, non-structural, and contents) and individual stories along the building height. Existing models can simply be adjusted to this approach and provide a more holistic view of risk. The benefits and potential applications in the (re)insurance sector are also discussed.
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