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Seismic Assessment of Low-Rise Exist...

Wu, Jingren.

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Seismic Assessment of Low-Rise Existing Infilled Steel Frames Under Earthquake Sequences Using Machine Learning-based Fragility Analysis.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Seismic Assessment of Low-Rise Existing Infilled Steel Frames Under Earthquake Sequences Using Machine Learning-based Fragility Analysis./
Author:	Wu, Jingren.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	190 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-05, Section: B.
Contained By:	Dissertations Abstracts International85-05B.
Subject:	Load. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30721442
ISBN:	9798380726023

Seismic Assessment of Low-Rise Existing Infilled Steel Frames Under Earthquake Sequences Using Machine Learning-based Fragility Analysis.
Wu, Jingren.

Seismic Assessment of Low-Rise Existing Infilled Steel Frames Under Earthquake Sequences Using Machine Learning-based Fragility Analysis. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 190 p.

Source: Dissertations Abstracts International, Volume: 85-05, Section: B.

Thesis (Ph.D.)--The University of Liverpool (United Kingdom), 2023.

Most existing steel moment-resisting frames in Europe were designed before the introduction of modern seismic design codes, hence they often exhibit weak performance under earthquake loading due to their low lateral resistance and energy dissipation capacity. Such structures often also include stiff and brittle masonry infill walls, which can highly influence the lateral response and distribution of damage patterns of steel moment-resisting frames, due to the complex interactions between the infill walls and the confining frames. However, the current procedures for the assessment of existing steel buildings included in the Eurocode 8- Part 3 do not provide adequate instructions on the assessment of 'weak' steel frames with masonry infill walls. In the meantime, there is limited guidance in the code to account for the influence of multiple earthquakes, which can happen within the same day, leaving no time for repair or retrofit between and causing catastrophic collapse of buildings.The present thesis aims at investigating the seismic performance of existing low-rise steel moment-resisting frames and developing a framework for fragility assessment that incorporates machine learning techniques for improved efficiency of fragility curves. A twostorey non-seismically designed steel moment-resisting frame that represents the existing steel frames in Southern Europe is adopted as the case study building, whose seismic response was experimentally investigated through large-scale pseudo-dynamic tests. The selection of the case study building enables this thesis to perform comprehensive numerical studies relying on finite element models that are well-calibrated against experimental results for improved accuracy and reliability.A detailed model of the case study frame is built to investigate the interactions between the infill walls and the confining steel frame, as well as to evaluate the reliability and efficiency of the single-strut modelling strategy as a simplified method for modelling the infill walls in steel moment-resisting frames. Then methodologies for assessing the seismic performance of infilled steel moment-resisting frames, which focus on addressing the influence of infill walls and earthquake sequences, are presented and implemented on the case study building for demonstration purposes. In addition, this thesis also provides a machine learning-based method for deriving fragility curves, which can achieve high accuracy but at a significantly reduced computational cost, hence can be integrated into the seismic assessment procedure as an efficient tool.

ISBN: 9798380726023Subjects--Topical Terms:

3562902
Load.

Seismic Assessment of Low-Rise Existing Infilled Steel Frames Under Earthquake Sequences Using Machine Learning-based Fragility Analysis.
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520 $a Most existing steel moment-resisting frames in Europe were designed before the introduction of modern seismic design codes, hence they often exhibit weak performance under earthquake loading due to their low lateral resistance and energy dissipation capacity. Such structures often also include stiff and brittle masonry infill walls, which can highly influence the lateral response and distribution of damage patterns of steel moment-resisting frames, due to the complex interactions between the infill walls and the confining frames. However, the current procedures for the assessment of existing steel buildings included in the Eurocode 8- Part 3 do not provide adequate instructions on the assessment of 'weak' steel frames with masonry infill walls. In the meantime, there is limited guidance in the code to account for the influence of multiple earthquakes, which can happen within the same day, leaving no time for repair or retrofit between and causing catastrophic collapse of buildings.The present thesis aims at investigating the seismic performance of existing low-rise steel moment-resisting frames and developing a framework for fragility assessment that incorporates machine learning techniques for improved efficiency of fragility curves. A twostorey non-seismically designed steel moment-resisting frame that represents the existing steel frames in Southern Europe is adopted as the case study building, whose seismic response was experimentally investigated through large-scale pseudo-dynamic tests. The selection of the case study building enables this thesis to perform comprehensive numerical studies relying on finite element models that are well-calibrated against experimental results for improved accuracy and reliability.A detailed model of the case study frame is built to investigate the interactions between the infill walls and the confining steel frame, as well as to evaluate the reliability and efficiency of the single-strut modelling strategy as a simplified method for modelling the infill walls in steel moment-resisting frames. Then methodologies for assessing the seismic performance of infilled steel moment-resisting frames, which focus on addressing the influence of infill walls and earthquake sequences, are presented and implemented on the case study building for demonstration purposes. In addition, this thesis also provides a machine learning-based method for deriving fragility curves, which can achieve high accuracy but at a significantly reduced computational cost, hence can be integrated into the seismic assessment procedure as an efficient tool.
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