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Performance-Based Wind Engineering of Tall Buildings: Nonstationary Wind, Loading Protocol and Nonlinear Response.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Performance-Based Wind Engineering of Tall Buildings: Nonstationary Wind, Loading Protocol and Nonlinear Response./
作者:	Wang, Haifeng.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	290 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-04, Section: B.
Contained By:	Dissertations Abstracts International83-04B.
標題:	Civil engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28720842
ISBN:	9798460420742

Performance-Based Wind Engineering of Tall Buildings: Nonstationary Wind, Loading Protocol and Nonlinear Response.
Wang, Haifeng.

Performance-Based Wind Engineering of Tall Buildings: Nonstationary Wind, Loading Protocol and Nonlinear Response. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 290 p.

Source: Dissertations Abstracts International, Volume: 83-04, Section: B.

Thesis (Ph.D.)--State University of New York at Buffalo, 2021.

This item must not be sold to any third party vendors.

The wind design of tall buildings is moving towards the performance-based design (PBD) methodology due mainly to its advantages in clearly articulating stakeholder expectations, explicitly satisfying performance objectives and considerably reducing building life-cycle cost. The development of performance-based wind engineering (PBWE) for tall buildings benefits significantly from the seismic performance assessment framework advanced by Pacific Earthquake Engineering Research (PEER) center and ATC-58 project (FEMA P-58), consisting essentially of hazard, response, damage and loss analysis modules. Due to the inherent difference of hazard characteristics and acceptable/expected structural response under wind and seismic events, some theoretical and practical considerations in performance-based seismic engineering (PBSE) may need to be revisited and modified before their applications to PBWE. To facilitate the implementation of PBWE, this study contributes to nonstationary wind hazard analysis, wind demand assessment of structural component and efficient estimation of nonlinear structural response by using cutting-edge signal processing and machine learning techniques. For nonstationary wind hazard analysis in PBWE, Hilbert-wavelet-based identification and simulation framework of nonstationary wind was established. Specifically, the multi-level nonstationarity index was developed to quantitatively investigate the wind signal nonstationarity. With consideration of building aerodynamics and dynamics, the proposed nonstationarity index was capable of capturing the effect of wind nonstationarity on structural response. In addition, improved simulation accuracy and efficiency (compared to state-of-the-art simulation algorithms) were achieved with the Hilbert-wavelet-based analysis and synthesis of nonstationary wind. For wind demand assessment of structural component in PBWE, hurricane wind duration analysis and loading protocol design frameworks were developed. Specifically, the statistics of wind duration, speed evolution and direction evolution were extracted from a large number of scenarios generated by a refined hurricane track model. Based on the statistical investigation of hurricane wind and associated building aerodynamics and dynamics, a rational loading protocol was came up with consideration of the cumulative damage on deformation-controlled structural components. For efficient estimation of nonlinear structural response in PBWE, machine learning tools were utilized. Specifically, the knowledge-enhanced deep learning technique was exploited to reduce the high demand on training data in the application of deep neural networks to nonlinear response-history analysis of a single building. To avoid retraining the neural network for application to each new building, the conditional neural network was developed to enhance response estimation efficiency for nonlinear response history of a group of buildings.

ISBN: 9798460420742Subjects--Topical Terms:

860360
Civil engineering.
Subjects--Index Terms:

Loading protocol

Performance-Based Wind Engineering of Tall Buildings: Nonstationary Wind, Loading Protocol and Nonlinear Response.
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520 $a The wind design of tall buildings is moving towards the performance-based design (PBD) methodology due mainly to its advantages in clearly articulating stakeholder expectations, explicitly satisfying performance objectives and considerably reducing building life-cycle cost. The development of performance-based wind engineering (PBWE) for tall buildings benefits significantly from the seismic performance assessment framework advanced by Pacific Earthquake Engineering Research (PEER) center and ATC-58 project (FEMA P-58), consisting essentially of hazard, response, damage and loss analysis modules. Due to the inherent difference of hazard characteristics and acceptable/expected structural response under wind and seismic events, some theoretical and practical considerations in performance-based seismic engineering (PBSE) may need to be revisited and modified before their applications to PBWE. To facilitate the implementation of PBWE, this study contributes to nonstationary wind hazard analysis, wind demand assessment of structural component and efficient estimation of nonlinear structural response by using cutting-edge signal processing and machine learning techniques. For nonstationary wind hazard analysis in PBWE, Hilbert-wavelet-based identification and simulation framework of nonstationary wind was established. Specifically, the multi-level nonstationarity index was developed to quantitatively investigate the wind signal nonstationarity. With consideration of building aerodynamics and dynamics, the proposed nonstationarity index was capable of capturing the effect of wind nonstationarity on structural response. In addition, improved simulation accuracy and efficiency (compared to state-of-the-art simulation algorithms) were achieved with the Hilbert-wavelet-based analysis and synthesis of nonstationary wind. For wind demand assessment of structural component in PBWE, hurricane wind duration analysis and loading protocol design frameworks were developed. Specifically, the statistics of wind duration, speed evolution and direction evolution were extracted from a large number of scenarios generated by a refined hurricane track model. Based on the statistical investigation of hurricane wind and associated building aerodynamics and dynamics, a rational loading protocol was came up with consideration of the cumulative damage on deformation-controlled structural components. For efficient estimation of nonlinear structural response in PBWE, machine learning tools were utilized. Specifically, the knowledge-enhanced deep learning technique was exploited to reduce the high demand on training data in the application of deep neural networks to nonlinear response-history analysis of a single building. To avoid retraining the neural network for application to each new building, the conditional neural network was developed to enhance response estimation efficiency for nonlinear response history of a group of buildings.
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