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Reconstructing seismic response demands across multiple tall buildings using kernel‐based machine learning methods
Author(s) -
Sun Han,
Burton Henry,
Wallace John
Publication year - 2019
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.2359
Subject(s) - kernel (algebra) , portfolio , range (aeronautics) , nonlinear system , structural engineering , roof , computer science , machine learning , engineering , artificial intelligence , mathematics , combinatorics , physics , quantum mechanics , financial economics , economics , aerospace engineering
Summary 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.