A Generalized Artificial Neural Network for Displacement-Based Seismic Design of Mass Timber Rocking Walls

Displacement-based seismic design often requires nonlinear time history simulation of building responses which is computationally intensive. This technical note presents an artificial neural network (ANN) designed to generate max inter-story drift for buildings with post-tensioned mass timber (MT) r...

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Bibliographic Details
Published inJournal of earthquake engineering : JEE Vol. 26; no. 15; pp. 7921 - 7932
Main Authors Huang, Da, Pei, Shiling
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 18.11.2022
Taylor & Francis Ltd
Subjects
Artificial neural network
Artificial neural networks
CLT post-tensioned rocking wall
Computer applications
Design
Design parameters
displacement-based seismic design
History
mass timber building
max dynamic response prediction
Neural networks
Post-tensioning
Seismic design
Simulation
Walls
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Summary:Displacement-based seismic design often requires nonlinear time history simulation of building responses which is computationally intensive. This technical note presents an artificial neural network (ANN) designed to generate max inter-story drift for buildings with post-tensioned mass timber (MT) rocking walls systems. This particular lateral system was selected because it is an innovative system with limited physical design parameters, making it an ideal candidate for ANN. The proposed model achieved significantly higher computational efficiency than time history simulation, while maintaining similar level of accuracy. The proposed method could potentially be used for automated design of MT rocking wall lateral systems.
ISSN:1363-2469
1559-808X
DOI:10.1080/13632469.2021.1988768