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Rapid Stochastic Response Estimation of Dynamic Nonlinear Structures : = Innovative Frameworks and Applications.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Rapid Stochastic Response Estimation of Dynamic Nonlinear Structures :/
其他題名:	Innovative Frameworks and Applications.
作者:	Li, Bowei.
面頁冊數:	1 online resource (190 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-01, Section: B.
Contained By:	Dissertations Abstracts International84-01B.
標題:	Civil engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29275005click for full text (PQDT)
ISBN:	9798438777724

Rapid Stochastic Response Estimation of Dynamic Nonlinear Structures : = Innovative Frameworks and Applications.
Li, Bowei.

Rapid Stochastic Response Estimation of Dynamic Nonlinear Structures :Innovative Frameworks and Applications. - 1 online resource (190 pages)

Source: Dissertations Abstracts International, Volume: 84-01, Section: B.

Thesis (Ph.D.)--University of Michigan, 2022.

Includes bibliographical references

Response estimation of structural systems characterized by large numbers of degrees of freedom (DOF) is universally needed in practical engineering for performance evaluation and design. When subjected to extreme events, e.g., destructive earthquakes or hurricanes, such structural systems typically experience significant damage and therefore nonlinearity in their response. This can lead to extremely computationally cumbersome problems. Further, if repetitions of such analyses are required, e.g., in uncertainty propagations or optimizations, the associated computational burden can quickly become intractable. This has created a need for efficient response simulation methods for nonlinear dynamic structural systems. To address this need, innovative mechanics and data-driven approaches are investigated in this research. The mechanics-based approaches address the problem by exploring strategies for increasing the efficiency of the response simulation algorithms. In particular, a novel adaptive fast nonlinear analysis (AFNA) algorithm is developed for solving nonlinear structural systems discretized at the level of the fibers or stress resultants. In the proposed AFNA scheme, algorithm configurations, such as the bases for model order reduction (MOR) and time step sizes, are determined adaptively. Compared to direct integration schemes, the AFNA approach is seen to have comparable accuracy with, however, speedups of an order of magnitude. Further, the solution scheme is embedded into a sampling-based wind reliability analysis framework that enables not only more accurate reliability assessment but also a full range of time history analyses for shakedown and beyond. The data-driven approaches, on the other hand, center on training efficient surrogates of the original high-fidelity model. In particular, the data-driven approaches were developed by leveraging MOR and time series metamodeling. Firstly, a data-driven Galerkin projection is introduced to reduce the response space of the original structural system. Subsequently, techniques based on the multi-input-multi-output nonlinear autoregressive model with exogenous input (MIMO NARX) system identification and long-short term memory (LSTM) deep learning are introduced to capture the dynamics of the reduced system. These approaches are capable of accurately reproducing both the global displacement and local hysteretic curves, with speedups over the high-fidelity simulations of three to five orders of magnitude. As a separate application of metamodeling, a real-time risk forecast framework for hurricane-induced damage in building systems is developed, where metamodels were created to reduce the computational demand to enable the real-time use of high-fidelity performance assessment frameworks. The methodology was used to show the strong potential of metamodeling for informing early emergency response.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798438777724Subjects--Topical Terms:

860360
Civil engineering.
Subjects--Index Terms:

Rapid nonlinear time history estimationIndex Terms--Genre/Form:

542853
Electronic books.

Rapid Stochastic Response Estimation of Dynamic Nonlinear Structures : = Innovative Frameworks and Applications.
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504 $a Includes bibliographical references
520 $a Response estimation of structural systems characterized by large numbers of degrees of freedom (DOF) is universally needed in practical engineering for performance evaluation and design. When subjected to extreme events, e.g., destructive earthquakes or hurricanes, such structural systems typically experience significant damage and therefore nonlinearity in their response. This can lead to extremely computationally cumbersome problems. Further, if repetitions of such analyses are required, e.g., in uncertainty propagations or optimizations, the associated computational burden can quickly become intractable. This has created a need for efficient response simulation methods for nonlinear dynamic structural systems. To address this need, innovative mechanics and data-driven approaches are investigated in this research. The mechanics-based approaches address the problem by exploring strategies for increasing the efficiency of the response simulation algorithms. In particular, a novel adaptive fast nonlinear analysis (AFNA) algorithm is developed for solving nonlinear structural systems discretized at the level of the fibers or stress resultants. In the proposed AFNA scheme, algorithm configurations, such as the bases for model order reduction (MOR) and time step sizes, are determined adaptively. Compared to direct integration schemes, the AFNA approach is seen to have comparable accuracy with, however, speedups of an order of magnitude. Further, the solution scheme is embedded into a sampling-based wind reliability analysis framework that enables not only more accurate reliability assessment but also a full range of time history analyses for shakedown and beyond. The data-driven approaches, on the other hand, center on training efficient surrogates of the original high-fidelity model. In particular, the data-driven approaches were developed by leveraging MOR and time series metamodeling. Firstly, a data-driven Galerkin projection is introduced to reduce the response space of the original structural system. Subsequently, techniques based on the multi-input-multi-output nonlinear autoregressive model with exogenous input (MIMO NARX) system identification and long-short term memory (LSTM) deep learning are introduced to capture the dynamics of the reduced system. These approaches are capable of accurately reproducing both the global displacement and local hysteretic curves, with speedups over the high-fidelity simulations of three to five orders of magnitude. As a separate application of metamodeling, a real-time risk forecast framework for hurricane-induced damage in building systems is developed, where metamodels were created to reduce the computational demand to enable the real-time use of high-fidelity performance assessment frameworks. The methodology was used to show the strong potential of metamodeling for informing early emergency response.
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