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Near-Real Time Evaluation Method of Seismic Damage Based on Structural Health Monitoring Data
https://orcid.org/http://orcid.org/0000-0002-8536-887X
https://orcid.org/http://orcid.org/0000-0001-9729-4671
https://orcid.org/http://orcid.org/0000-0002-6870-240X
https://orcid.org/http://orcid.org/0000-0002-9233-0036
Most modern seismic design codes build upon the concept of performance-based earthquake engineering that allows structures to sustain repairable damage during moderate and large earthquakes. Therefore, accurate and quantitative post-earthquake damage evaluation of real-world structures is crucial for safe operation of buildings. Structural-health monitoring provides sensor-based information regarding the structural state and informs post-earthquake building assessment. With the utilization of monitoring data, which is recorded during earthquake excitation, damage-sensitive features (DSFs) can be extracted in both purely data-driven or hybrid forms; with the latter term referring to damage indicators (DIs) that fuse data with dynamic models. In this paper, data-driven and hybrid damage identification methods are introduced and compared with respect to their performance and robustness in detecting and quantifying structural damage. The damage localization and quantification performance are discussed for varying number of building floors. Moreover, numerical models are used to enable the comparison of DSFs with metrics of nonlinearity, such as maximum drift, and with response metrics that are traditionally used to quantify damage, such as maximum inter-story drifts. Finally, uncertainties in DSFs and their sensitivity to sensor noise, prior knowledge of mass and the spectral content of earthquake excitation are assessed to explore the robustness of the hybrid DI.
Near-Real Time Evaluation Method of Seismic Damage Based on Structural Health Monitoring Data
https://orcid.org/http://orcid.org/0000-0002-8536-887X
https://orcid.org/http://orcid.org/0000-0001-9729-4671
https://orcid.org/http://orcid.org/0000-0002-6870-240X
https://orcid.org/http://orcid.org/0000-0002-9233-0036
Most modern seismic design codes build upon the concept of performance-based earthquake engineering that allows structures to sustain repairable damage during moderate and large earthquakes. Therefore, accurate and quantitative post-earthquake damage evaluation of real-world structures is crucial for safe operation of buildings. Structural-health monitoring provides sensor-based information regarding the structural state and informs post-earthquake building assessment. With the utilization of monitoring data, which is recorded during earthquake excitation, damage-sensitive features (DSFs) can be extracted in both purely data-driven or hybrid forms; with the latter term referring to damage indicators (DIs) that fuse data with dynamic models. In this paper, data-driven and hybrid damage identification methods are introduced and compared with respect to their performance and robustness in detecting and quantifying structural damage. The damage localization and quantification performance are discussed for varying number of building floors. Moreover, numerical models are used to enable the comparison of DSFs with metrics of nonlinearity, such as maximum drift, and with response metrics that are traditionally used to quantify damage, such as maximum inter-story drifts. Finally, uncertainties in DSFs and their sensitivity to sensor noise, prior knowledge of mass and the spectral content of earthquake excitation are assessed to explore the robustness of the hybrid DI.
Near-Real Time Evaluation Method of Seismic Damage Based on Structural Health Monitoring Data
https://orcid.org/http://orcid.org/0000-0002-8536-887X
https://orcid.org/http://orcid.org/0000-0001-9729-4671
https://orcid.org/http://orcid.org/0000-0002-6870-240X
https://orcid.org/http://orcid.org/0000-0002-9233-0036
Most modern seismic design codes build upon the concept of performance-based earthquake engineering that allows structures to sustain repairable damage during moderate and large earthquakes. Therefore, accurate and quantitative post-earthquake damage evaluation of real-world structures is crucial for safe operation of buildings. Structural-health monitoring provides sensor-based information regarding the structural state and informs post-earthquake building assessment. With the utilization of monitoring data, which is recorded during earthquake excitation, damage-sensitive features (DSFs) can be extracted in both purely data-driven or hybrid forms; with the latter term referring to damage indicators (DIs) that fuse data with dynamic models. In this paper, data-driven and hybrid damage identification methods are introduced and compared with respect to their performance and robustness in detecting and quantifying structural damage. The damage localization and quantification performance are discussed for varying number of building floors. Moreover, numerical models are used to enable the comparison of DSFs with metrics of nonlinearity, such as maximum drift, and with response metrics that are traditionally used to quantify damage, such as maximum inter-story drifts. Finally, uncertainties in DSFs and their sensitivity to sensor noise, prior knowledge of mass and the spectral content of earthquake excitation are assessed to explore the robustness of the hybrid DI.
Near-Real Time Evaluation Method of Seismic Damage Based on Structural Health Monitoring Data
Lecture Notes in Civil Engineering
Rizzo, Piervincenzo (editor) / Milazzo, Alberto (editor) / Zhang, Hanqing (author) / Reuland, Yves (author) / Chatzi, Eleni (author) / Shan, Jiazeng (author)
European Workshop on Structural Health Monitoring ; 2022 ; Palermo, Italy
2022-06-16
9 pages
Article/Chapter (Book)
Electronic Resource
English
Structural health monitoring , Damage identification , Hybrid damage indicators , Damage-sensitive features , Post-earthquake damage assessment Engineering , Building Repair and Maintenance , Cyber-physical systems, IoT , Industrial and Production Engineering , Monitoring/Environmental Analysis , Analytical Chemistry
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