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Clustering-based adaptive ground motion selection algorithm for efficient estimation of structural fragilities

Engineering and Technology

Clustering-based adaptive ground motion selection algorithm for efficient estimation of structural fragilities

T. Kim, O. Kwon, et al.

Experience a breakthrough in earthquake engineering with a clustering-based algorithm that efficiently selects representative ground motions to predict seismic demands. This innovative research by Taeyong Kim, Oh-Sung Kwon, and Junho Song showcases a method that enhances accuracy while reducing the number of ground motions needed for robust structural fragility assessments.

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Playback language: English
Abstract
This paper proposes a clustering-based algorithm for efficiently selecting a representative subset of ground motions to predict seismic demands of structural systems accurately. The algorithm first identifies critical ground motion features significantly affecting seismic demands using Lasso regression on responses of various single-degree-of-freedom (SDOF) systems. Then, it adaptively selects ground motions based on hierarchical clustering of these features until the fragility curve converges. Applications to reinforced concrete and steel moment-resisting frames demonstrate improved efficiency and wide applicability, confirming robust performance across various ground motions, structural types, and limit-state definitions. The algorithm allows for obtaining structural fragilities using significantly fewer ground motions while maintaining consistency with the initial ground motion set.
Publisher
Earthquake Engineering & Structural Dynamics
Published On
Nov 16, 2021
Authors
Taeyong Kim, Oh-Sung Kwon, Junho Song
Tags
clustering algorithm
ground motions
seismic demands
Lasso regression
structural fragility
reinforced concrete
moment-resisting frames
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