Yves Reuland

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Reuland

First Name

Yves

ORCID

0000-0001-9729-4671

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Publications 1 - 10 of 32

Data-driven model updating for seismic assessment of existing buildings
Martakis, Panagiotis; Reuland, Yves; Chatzi, Eleni (2021)
Proceedings of the International Conference on Structural Health Monitoring of Intelligent Infrastructure
Monitoring-Driven Seismic Assessment of Existing Masonry Buildings
Martakis, Panagiotis; Reuland, Yves; Ntertimanis, Vasileios; et al. (2019)
ANCRiSST 2019 Procedia 14th International Workshop on Advanced Smart Materials and Smart Structures Technology
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.
Amplitude Dependency Effects in the Structural Identification of Historic Masonry Buildings
Martakis, Panagiotis; Reuland, Yves; Chatzi, Eleni (2021)
Lecture Notes in Civil Engineering ~ Proceedings of the 1st Conference of the European Association on Quality Control of Bridges and Structures
Occupant tracking using model-based data interpretation of structural vibrations
Drira, Slah; Reuland, Yves; Smith, Ian F.C. (2019)
Transferring Research into Practice. Proceedings of the 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure
Post-earthquake structural damage assessment and damage state evaluation for RC structures with experimental validation
Zhang, Hanqing; Reuland, Yves; Shan, Jiazeng; et al. (2024)
Engineering Structures
Accurate post-earthquake damage evaluation of real-world structures is essential to ensure the safe operation of buildings. To this end, we propose a damage evaluation method for earthquake-excited structures relying on the hysteresis curve reconstruction approach. Central to this method is the use of an equivalent single-degree of freedom (ESDOF) system, designed to model structural overall hysteresis and to facilitate an acceleration-based Damage Indicator (DI). Differently from the previously investigated hybrid damage index, a data-driven variant of this DI, configured for reduced reliance on model information and enhanced computational efficiency, is here introduced. This DI is integrated into a framework for assessing structural damage and seismic performance levels, leveraging Seismic Structural Health Monitoring data. The reliability and robustness of the DI with respect to earthquake excitation of different characteristics and for a varying number of floors is assessed by adopting a simulated five-degree-of-freedom degradation model. A large-scale RC frame shaking table test is employed for a comprehensive evaluation of the proposed scheme, allowing to illustration of the damage assessment and the damage state evaluation performance of the proposed data-driven DI. Though the utilization of the ESDOF concept precludes the evaluation of the intensity and location of structural damage, the damage state evaluation results may still provide effective physical information besides the DI values. The relatively low requirements for prior model information and nonlinear behavior render the proposed DI applicable to real-world implementation. This promising characteristic may consequently facilitate the rapid post-earthquake decision-making process.
A Comparative Study of Damage-Sensitive Features for Rapid Data-Driven Seismic Structural Health Monitoring
Reuland, Yves; Martakis, Panagiotis; Chatzi, Eleni (2023)
Applied Sciences
Rapid post-earthquake damage assessment forms a critical element of resilience, ensuring a prompt and functional recovery of the built environment. Monitoring-based approaches have the potential to significantly improve upon current visual inspection-based condition assessment that is slow and potentially subjective. The large variety of sensing solutions that has become available at affordable cost in recent years allows the engineering community to envision permanent-monitoring applications even in conventional low-to-mid-rise buildings. When combined with adequate structural health monitoring (SHM) techniques, sensor data recorded during earthquakes have the potential to provide automated near-real-time identification of earthquake damage. Near-real time building assessment relies on the tracking of damage-sensitive features (DSFs) that can be directly and rapidly derived from dynamic monitoring data and scaled with damage. We here offer a comprehensive review of such damage-sensitive features in an effort to formally assess the capacity of such data-driven indicators to detect, localize and quantify the presence of nonlinearity in seismic-induced structural response. We employ both a parametric analysis on a simulated model and real data from shake-table tests to investigate the strengths and limitations of purely data-driven approaches, which typically involve a comparison against a healthy reference state. We present an array of damage-sensitive features which are found to be robust with respect to noise, to reliably detect and scale with nonlinearity, and to carry potential to localize the occurrence of nonlinear behavior in conventional structures undergoing earthquakes.
Using footstep-induced vibrations for occupant detection and recognition in buildings
Drira, Slah; Pai, Sai G.S.; Reuland, Yves; et al. (2021)
Advanced Engineering Informatics
Occupant detection and recognition support functional goals such as security, healthcare, and energy management in buildings. Typical sensing approaches, such as smartphones and cameras, undermine the privacy of building occupants and inherently affect their behavior. To overcome these drawbacks, a non-intrusive technique using floor-vibration measurements, induced by human footsteps, is outlined. Detection of human-footstep impacts is an essential step to estimate the number of occupants, recognize their identities and provide an estimate of their probable locations. Detecting the presence of occupants on a floor is challenging due to ambient noise that may mask footstep-induced floor vibrations. Also, signals from multiple occupants walking simultaneously overlap, which may lead to inaccurate event separation. Signals corresponding to events, once extracted, can be used to identify the number of occupants and their locations. Spurious events such as door closing, chair dragging and falling objects may produce vibrations similar to footstep-impacts. Signals from such spurious events have to be discarded as outliers to prevent inaccurate interpretations of floor vibrations for occupant detection. Walking styles differ among occupants due to their anatomies, walking speed, shoe type, health and mood. Thus, footstep-impact vibrations from the same person may vary significantly, which adds uncertainty and complicates occupant recognition. In this paper, efficient strategies for event-detection and event-signal extraction have been described. These strategies are based on variations in standard deviations over time of measured signals (using a moving window) that have been filtered to contain only low-frequency components. Methods described in this paper for event detection and event-signal extraction perform better than existing threshold-based methods (fewer false positives and false negatives). Support vector machine classifiers are used successfully to distinguish footsteps from other events and to determine the number of occupants on a floor. Convolutional neural networks help recognize the identity of occupants using footstep-induced floor vibrations. The utility of these strategies for footstep-event detection, occupant counting, and recognition is validated successfully using two full-scale case studies.

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Yves Reuland

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