Lukas Bodenmann

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Bodenmann

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Lukas

ORCID

0000-0002-7742-3306

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Publications 1 - 10 of 15

Towards a dynamic earthquake risk framework for Switzerland
Böse, Maren; Danciu, Laurentiu; Papadopoulos, Athanasios N.; et al. (2024)
Natural Hazards and Earth System Sciences
Scientists from different disciplines at ETH Zurich are developing a dynamic, harmonised, and user-centred earthquake risk framework for Switzerland, relying on a continuously evolving earthquake catalogue generated by the Swiss Seismological Service (SED) using the national seismic networks. This framework uses all available information to assess seismic risk at various stages and facilitates widespread dissemination and communication of the resulting information. Earthquake risk products and services include operational earthquake (loss) forecasting (OE(L)F), earthquake early warning (EEW), ShakeMaps, rapid impact assessment (RIA), structural health monitoring (SHM), and recovery and rebuilding efforts (RRE). Standardisation of products and workflows across various applications is essential for achieving broad adoption, universal recognition, and maximum synergies. In the Swiss dynamic earthquake risk framework, the harmonisation of products into seamless solutions that access the same databases, workflows, and software is a crucial component. A user-centred approach utilising quantitative and qualitative social science tools like online surveys and focus groups is a significant innovation featured in all products and services. Here we report on the key considerations and developments of the framework and its components. This paper may serve as a reference guide for other countries wishing to establish similar services for seismic risk reduction.
Dynamic post-earthquake updating of regional damage estimates using Gaussian Processes
Bodenmann, Lukas; Reuland, Yves; Stojadinovic, Bozidar (2023)
Reliability Engineering & System Safety
The widespread earthquake damage to the built environment induces severe short- and long-term societal consequences. Better community resilience may be achieved through well-organized recovery. Decisions to organize the recovery process are taken under intense time pressure using limited, and potentially inaccurate, data on the severity and the spatial distribution of building damage. We propose to use Gaussian Process inference models to fuse the available inspection data with a pre-existing earthquake risk model to dynamically update regional post-earthquake damage estimates and thereby support a well-organized recovery. The proposed method consistently aggregates the gradually incoming building damage inspection data to reduce the uncertainty in ground shaking intensity geographic distribution and to update regional building damage estimates. The performance of the proposed Gaussian Process methodology is demonstrated on one fictitious earthquake scenario and two real earthquake damage datasets. A comparison with purely data-driven methods shows that the proposed method reduces the number of building inspections required to provide reliable and precise damage predictions.
Dynamic Updating of Building Loss Predictions Using Regional Risk Models and Conventional Post-Earthquake Data Sources
Bodenmann, Lukas; Reuland, Yves; Stojadinovic, Bozidar (2021)
Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021)
Earthquakes can cause widespread damage to the built environment, disrupt the function of many residential buildings to provide safe housing capacities and thus, potentially induce severe long-term societal consequences. Rapid recovery significantly improves the short-term resilience of communities after an earthquake. However, time pressure and scarce information on the severity and the spatial distribution of damage complicate the decision-making. Therefore, early damage estimates are produced using regional earthquake risk models with rapid earthquake intensity data and typological building vulnerability functions. While the precision of the former depends, amongst other issues, on the density of seismic network stations and the region-specific geological knowledge, the typological classification of buildings often involves attribution models correlating exposure data, such as building height and age, with typological seismic vulnerability classes. Typological attribution models are approximate and locally add to the uncertainties resulting from the average representation of buildings forming one building class. Employing probabilistic machine-learning tools, the continuous inspection data inflow is leveraged to dynamically update initial regional earthquake risk predictions by updating simultaneously the functions that govern typological attribution and building damage. Hence, while completing inspection of all affected buildings may take several weeks, the limited information becoming available in the first days following an earthquake helps constraining underlying uncertainties. This leads to more reliable rapid estimates of losses of building functions and their respective spatial distribution. The framework is demonstrated on a region in Switzerland subjected to a fictitious earthquake scenario.
Data-efficient learning techniques to improve regional earthquake risk models
Bodenmann, Lukas (2023)
Accounting for path and site effects in spatial ground-motion correlation models using Bayesian inference
Bodenmann, Lukas; Baker, Jack W.; Stojadinovic, Bozidar (2023)
Natural Hazards and Earth System Sciences
Ground-motion correlation models play a crucial role in regional seismic risk modeling of spatially distributed built infrastructure. Such models predict the correlation between ground-motion amplitudes at pairs of sites, typically as a function of their spatial proximity. Data from physics-based simulators and event-to-event variability in empirically derived model parameters suggest that spatial correlation is additionally affected by path and site effects. Yet, identifying these effects has been difficult due to scarce data and a lack of modeling and assessment approaches to consider more complex correlation predictions. To address this gap, we propose a novel correlation model that accounts for path and site effects via a modified functional form. To quantify the estimation uncertainty, we perform Bayesian inference for model parameter estimation. The derived model outperforms traditional isotropic models in terms of the predictive accuracy for training and testing data sets. We show that the previously found event-to-event variability in model parameters may be explained by the lack of accounting for path and site effects. Finally, we examine implications of the newly proposed model for regional seismic risk simulations.
The role of risk measures in making seismic upgrading decisions
Bodenmann, Lukas; Galanis, Panagiotis; Broccardo, Marco; et al. (2020)
Earthquake Spectra
Risk measures are tools that enable consistent measurement of financial risk and quantify the risk exposure to an associated hazard. In finance, there is a broad spectrum of risk measures which reflect different asset performance goals and the risk appetite of the decision-maker. In this study, the authors leverage advancements in financial risk management to examine the role of risk measures to quantify the seismically induced financial risk, measure the benefit of seismic upgrading, and relate the benefit of seismic risk reduction to a degree of the implemented seismic upgrade. The findings demonstrate that the relation between the financial benefits of a seismic upgrade, quantified using risk measures that consider the full range of earthquake events, and the degree of the seismic upgrade are concave, that is, the incremental financial benefit reduces gradually with increasing degree of seismic upgrading. The opposite holds if the risk measures consider only the high-severity low-likelihood events. Therefore, the study shows that the selection of the risk measure plays a crucial role in determining the target degree of seismic upgrading. Equivalently, quantifying the financial benefits of seismic risk mitigation using different risk measures might lead to different seismic upgrading decisions for the same structure.
Accounting for ground-motion uncertainty in empirical seismic fragility modeling
Bodenmann, Lukas; Baker, Jack W.; Stojadinovic, Bozidar (2024)
Earthquake Spectra
Seismic fragility models provide a probabilistic relation between ground-motion intensity and damage, making them a crucial component of many regional risk assessments. Estimating such models from damage data gathered after past earthquakes is challenging because of uncertainty in the ground-motion intensity the structures were subjected to. Here, we develop a Bayesian estimation procedure that performs joint inference over ground-motion intensity and fragility model parameters. When applied to simulated damage data, the proposed method can recover the data-generating fragility functions, while the traditionally used method, employing fixed, best-estimate, intensity values, fails to do so. Analyses using synthetic data with known properties show that the traditional method results in flatter fragility functions that overestimate damage probabilities for low-intensity values and underestimate probabilities for large values. Similar trends are observed when comparing both methods on real damage data. The results suggest that neglecting ground-motion uncertainty manifests in apparent dispersion in the estimated fragility functions. This undesirable feature can be mitigated through the proposed Bayesian procedure.
The Financial Benefit of Earthquake Retrofitting in Different Hazard Environments
Galanis, Panagiotis; Bodenmann, Lukas; Broccardo, Marco; et al. (2018)
Integrating Science, Engineering, & Policy: 11th National Conference on Earthquake Engineering 2018 (11NCEE)
Validating a resilience quantification framework: The Case of 2010 Kraljevo Earthquake
Blagojević, Nikola; Bodenmann, Lukas; Reuland, Yves; et al. (2022)
Proceedings of the Third European Conference on Earthquake Engineering and Seismology – 3ECEES
Communities need to be resilient to minimize direct and indirect losses due to earthquakes. To develop and compare effective resilience improvement measures, tools that quantify community earthquake resilience are needed. The iRe-CoDeS framework quantifies resilience by monitoring the post-disaster supply, demand and consumption of a community, viewed as a system-of-interdependent-systems, for various resources and services. Lack of Resilience is defined as the unmet demand of a community for a certain resource or service during the recovery period. This study illustrates the application of the iRe-CoDeS framework to quantify housing resilience of Kraljevo, Serbia, following the M5.4 2010 earthquake. Furthermore, damage prediction models are updated using the early arriving inspection data and the effect of updated damage assessment on the estimated resilience metrics is studied. Resilience quantification results obtained using the iRe-CoDeS model are compared to data collected after the event to validate the proposed resilience quantification model.
Comparison of different risk measures for portfolio-level earthquake risk assessment
Bodenmann, Lukas; Galanis, Panagiotis; Broccardo, Marco; et al. (2021)
WCEE Online Proceedings ~ Proceedings of the Seventeenth World Conference on Earthquake Engineering Japan 2021
A risk measure quantifies the risk associated with a single asset (e.g., an individual building) or a group of assets (e.g., a building portfolio of a region) exposed to one or more sources of hazard during a given time horizon. These risk measures serve as objective functionals that define subsets of “acceptable” and “unacceptable” risks. In performance-based seismic design, a new building should fulfill a set of performance objectives not only to protect human life in rare earthquakes but also to limit direct (e.g., repair cost) and indirect (e.g., downtime, business interruption) financial losses in more frequent seismic events. These performance objectives are commonly formulated as limits on risk measures for individual buildings. The potentially large spatial footprint of earthquakes and the increased concentration of population and values in dense urban areas call for an explicit consideration of seismic risk at a regional level, in particular when formulating performance objectives for new individual building structures. Subadditivity is a desired mathematical property of risk measures in this setting, because the sum of subadditive risk measures evaluated separately for each individual building is an upper bound on the joint risk measured for a portfolio of buildings. The present study reviews different risk measures commonly employed in earthquake engineering and in the financial industry and discusses their mathematical properties with special emphasis on subadditivity. To illustrate the importance of subadditivity for earthquake engineering, a seismic loss analysis is performed for a given portfolio of buildings situated in a virtual hazard environment. Financial losses due to earthquake-induced building property damage are quantified for the individual buildings and for the portfolio of buildings using a set of risk measures. Given the defined hazard, vulnerability and exposure, the results show that quantile-based measures, such as the loss with a certain mean annual frequency of exceedance, are subadditive only for losses with a recurrence interval longer than 200 years. As a consequence, using quantile-based measures could lead to underestimation of portfolio-level financial losses for more frequent events.

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Lukas Bodenmann

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