Interpretable machine learning models for the estimation of seismic drifts in CLT buildings
File(s)
Published version
Author(s)
Junda, Eknara
Malaga Chuquitaype, Christian
Ketsarin, Chawgien
Type
Journal Article
Abstract
An accurate estimation of drift demands is crucial for designing and assessing structures under seismic loads. Given the novelty of massive timber buildings, predictive models for the estimation of drifts in mid- to high-rise CLT structures are lacking, particularly in the form of simple models suitable for preliminary design evaluations or regional seismic assessments. In this paper, we present and compare several Machine Learning (ML) models for the estimation of peak inter-storey and roof drifts in multi-storey Cross-Laminated Timber (CLT) walled structures. The ML techniques used include: Multiple Linear Regression, Regression Trees, Random Forest, K-nearest Neighbour, and Support Vector Regression. To this end, 69 structures spanning mid-rise to tall timber buildings are subjected to a large collection of acceleration records and used to create the training and testing datasets. Different structural configurations and behaviour factors, related to the assumed energy dissipation capacity of the buildings, are considered. A diversity of feature selection techniques informs our choice of parameters to the reduced input space leading to a set of six most efficient features: the spectral acceleration at the building’s fundamental period (Sa(T1)), the Peak Ground Velocity (PGV), tuning ratio (T1/Tm), behaviour factor (q), wall height (Hw), and the wall subdivision ratio (Wr). After verifying the high accuracy of our model predictions, the SHapley Additive exPlanation method (SHAP) is used to gain insight into the influence of key input features on the ML model outputs. Finally, our ML drift estimations are compared against previous proposals and design code assumptions, and the potential causes of disagreement are discussed.
Date Issued
2023-07-01
Date Acceptance
2023-03-22
Citation
Journal of Building Engineering, 2023, 70, pp.1-20
ISSN
2352-7102
Publisher
Elsevier
Start Page
1
End Page
20
Journal / Book Title
Journal of Building Engineering
Volume
70
Copyright Statement
© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/).
(http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
https://www.sciencedirect.com/science/article/pii/S2352710223005442
Publication Status
Published
Article Number
106365
Date Publish Online
2023-04-03