Machine-Learning Based Prediction Model for Identifying Torsion-Induced Seismic Response Amplification in Plan-Asymmetric Buildings

Yao Hu (Lead / Corresponding author), Elisa Lumantarna, Nelson Lam, Hing Ho Tsang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Torsion-induced seismic response amplification in plan-asymmetric buildings is of major concern in structural design. Code-based seismic design procedures based on elastic analyses do not address potential seismic risks that are aggravated by torsional actions. Implementing rigorous nonlinear dynamic analysis to guide the design of buildings featuring plan asymmetry is costly and not practical for day-to-day structural engineering practice. This paper presents a machine learning based methodology to identify a building that may experience the stepped increase in the drift demand ratio (i.e. hump) when the yield limit of the lateral load-resisting elements has been exceeded. Parametric studies based on nonlinear dynamic analysis of single-storey buildings with structural walls in varying number, size and position are undertaken to examine the effect of system parameters on hump that may occur in the post yield conditions. Buildings are divided into three categories including no hump, slight hump and large hump by assessing the increase in the inelastic drift demand ratio in comparison to the elastic drift demand ratio. Machine learning based prediction models have been developed to achieve a rapid identification of hump in a building based on dynamic analysis results of various single-storey buildings. The models can be an effective tool for optimising the design of plan asymmetric buildings by identifying the potential seismic risks posed by torsional action at the preliminary design stage.

Original languageEnglish
Title of host publicationProceedings of the 26th Australasian Conference on the Mechanics of Structures and Materials - ACMSM26
Subtitle of host publicationACMSM26, 3–6 December 2023, Auckland, New Zealand
EditorsNawawi Chouw, Chunwei Zhang
PublisherSpringer Singapore
Pages593-604
Number of pages12
Edition1
ISBN (Electronic)9789819733972
ISBN (Print)9789819733965, 9789819733996
DOIs
Publication statusPublished - 3 Sept 2024
Event26th Australasian Conference on the Mechanics of Structures and Materials, ACMSM 2023 - Auckland, New Zealand
Duration: 3 Dec 20236 Dec 2023
Conference number: 26th
https://www.acmsm26.com/

Publication series

NameLecture Notes in Civil Engineering
Volume513 LNCE
ISSN (Print)2366-2557
ISSN (Electronic)2366-2565

Conference

Conference26th Australasian Conference on the Mechanics of Structures and Materials, ACMSM 2023
Abbreviated titleACMSM26
Country/TerritoryNew Zealand
CityAuckland
Period3/12/236/12/23
Internet address

Keywords

  • Drift demand ratio
  • Machine learning
  • Nonlinear dynamic analysis
  • Seismic response amplification
  • Torsional behaviour

ASJC Scopus subject areas

  • Civil and Structural Engineering

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