Reconstructing seismic response demands across multiple tall buildings using kernel‐based machine learning methods

Abstract

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.