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 language | English |
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Title of host publication | Proceedings of the 26th Australasian Conference on the Mechanics of Structures and Materials - ACMSM26 |
Subtitle of host publication | ACMSM26, 3–6 December 2023, Auckland, New Zealand |
Editors | Nawawi Chouw, Chunwei Zhang |
Publisher | Springer Singapore |
Pages | 593-604 |
Number of pages | 12 |
Edition | 1 |
ISBN (Electronic) | 9789819733972 |
ISBN (Print) | 9789819733965, 9789819733996 |
DOIs | |
Publication status | Published - 3 Sept 2024 |
Event | 26th Australasian Conference on the Mechanics of Structures and Materials, ACMSM 2023 - Auckland, New Zealand Duration: 3 Dec 2023 → 6 Dec 2023 Conference number: 26th https://www.acmsm26.com/ |
Publication series
Name | Lecture Notes in Civil Engineering |
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Volume | 513 LNCE |
ISSN (Print) | 2366-2557 |
ISSN (Electronic) | 2366-2565 |
Conference
Conference | 26th Australasian Conference on the Mechanics of Structures and Materials, ACMSM 2023 |
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Abbreviated title | ACMSM26 |
Country/Territory | New Zealand |
City | Auckland |
Period | 3/12/23 → 6/12/23 |
Internet address |
Keywords
- Drift demand ratio
- Machine learning
- Nonlinear dynamic analysis
- Seismic response amplification
- Torsional behaviour
ASJC Scopus subject areas
- Civil and Structural Engineering