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Data-driven post-earthquake rapid structural safety assessment

James-A. Goulet, C. Michel and A. Der Kiureghian

Article (2015)

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Cite this document: Goulet, J.-A., Michel, C. & Kiureghian, A. D. (2015). Data-driven post-earthquake rapid structural safety assessment. Earthquake Engineering & Structural Dynamics, 44(4), p. 549-562. doi:10.1002/eqe.2541
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Abstract

Earthquake prone cities are exposed to important societal and financial losses. An important part of these losses stems from the inability to use structures as shelters or for generating economic activity after the event of an earthquake. The inability to use structures is not only due to collapse or damage; it is also due to the lack of knowledge about their safety state, which prohibits their normal use. Because a diagnosis is required for thousands of structures, city-scale safety assessment requires solutions that are economically sustainable and scalable. Data-driven algorithms supported by sensing technologies have the potential to solve this challenge. Several ambient vibration monitoring studies of buildings, before and after earthquakes, have shown that the extent of damage in a building is correlated with a decrease in the natural frequency. However, the observed worldwide data may not be representative of specific cities due to factors such as construction type, quality, material, age, etc. In this paper we propose a framework that is able to progressively learn the relationship between frequency shift and damage state as a small number of buildings in a city are inspected after an earthquake, and to use that information to predict the safety state of uninspected but monitored buildings. The capacity of the proposed framework to learn and perform prognosis is validated by applying the methodology to a city with 1000 buildings having simulated frequency shifts and damage states.

Uncontrolled Keywords

Resilience, Earthquake, Safety, Statistical Learning, Sensors, Condition assessment

Open Access document in PolyPublie
Subjects: 1000 Génie civil > 1000 Génie civil
Department: Département des génies civil, géologique et des mines
Research Center: Non applicable
Funders: Fonds national suisse de la recherche scientifique, Fonds de recherche du Québec - Nature et technologies, U.S. National Science Foundation
Grant number: CMMI-1130061
Date Deposited: 15 Jan 2018 15:08
Last Modified: 24 Oct 2018 16:12
PolyPublie URL: https://publications.polymtl.ca/2881/
Document issued by the official publisher
Journal Title: Earthquake Engineering & Structural Dynamics (vol. 44, no. 4)
Publisher: Wiley
Official URL: https://doi.org/10.1002/eqe.2541

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