Journal
STRUCTURAL CONTROL & HEALTH MONITORING
Volume 26, Issue 7, Pages -Publisher
JOHN WILEY & SONS LTD
DOI: 10.1002/stc.2359
Keywords
machine learning; rapid damage assessment; seismic instrumentation and monitoring; seismic response demands; tall building clusters
Funding
- National Science Foundation Division of Civil, Mechanical and Manufacturing Innovation (CMMI) [1538866]
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [1538866] Funding Source: National Science Foundation
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An approach to reconstructing full-profile seismic response demands across multiple tall buildings, using kernel-based machine learning methods, is introduced. Nonlinear response history analyses are used to generate a dataset of peak floor accelerations and peak story drift ratios for a portfolio of tall buildings, using spatially explicit ground motions from the Northridge earthquake. Structural dissimilarities are incorporated by including a range of building heights and differences in the type and combination of lateral force resisting systems. Using measurements from limited locations within a subset of buildings, the full-profile response demands for all buildings in a portfolio are reconstructed. A rigorous evaluation procedure is used to demonstrate the ability of the kernel-based methods to accurately capture the highly nonlinear response demand patterns within and across buildings. For a scenario where the first floor, mid-height, and roof level responses are known for 40% of the buildings, the kernel-based machine learning methods are able to estimate the full-profile demands of the entire portfolio with a median error that is approximately 30% of the measured demands.
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