Machine-learning interpretability techniques for seismic performance assessment of infrastructure systems

Volume: 250, Pages: 112883 - 112883
Published: Jan 1, 2022
Abstract
Machine-learning has recently gained considerable attention in the earthquake engineering community, as it can map the complex relationship between the expected damage and the input parameters. It is often necessary to understand the reasons for the behavior and predictions of the machine-learning model. This paper addresses this issue through interpretable machine-learning approaches such as partial dependence plots, accumulated local effects,...
Paper Details
Title
Machine-learning interpretability techniques for seismic performance assessment of infrastructure systems
Published Date
Jan 1, 2022
Volume
250
Pages
112883 - 112883
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