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An Optimal Sensor Design Framework for Structural Health Monitoring.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	An Optimal Sensor Design Framework for Structural Health Monitoring./
作者:	Yang, Yichao.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	228 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-07, Section: B.
Contained By:	Dissertations Abstracts International83-07B.
標題:	Civil engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28861323
ISBN:	9798759997030

An Optimal Sensor Design Framework for Structural Health Monitoring.
Yang, Yichao.

An Optimal Sensor Design Framework for Structural Health Monitoring. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 228 p.

Source: Dissertations Abstracts International, Volume: 83-07, Section: B.

Thesis (Ph.D.)--University of California, San Diego, 2021.

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

Over the life cycle of large civil infrastructure, there is often significant degradation in capability and design performance due to extend usage and, in many cases, deferred maintenance. The ultimate limit states in this case can result in unexpected failure, with potentially large economic or life-safety consequences. Structural health monitoring (SHM) is a framework for monitoring the structure over its life cycle, and this field has expanded rapidly in the past two to three decades due to the urgency of infrastructure aging beyond its design life. One of the critical challenges in a monitoring process is obtaining the most valuable information from the structure responses in the field under a continuous monitoring paradigm. This dissertation will present a new optimal sensor design framework, using Machine Learning (ML) techniques, including Gaussian Process Regression (GPR), Bayesian inference, uncertainty quantification, and Bayesian optimization, that guides risk-informed SHM system design. In particular, this dissertation: (1) demonstrates a framework for optimal sensor design using Bayes risk as the objective function; (2) further explores the framework using risk-weighted f-divergence functions and implements it in a real miter gate problem as a case study; (3) investigates the effects of sensor reliability over a life cycle monitoring of the miter gate for informing optimal sensor arrangement.

ISBN: 9798759997030Subjects--Topical Terms:

860360
Civil engineering.
Subjects--Index Terms:

Sensors

An Optimal Sensor Design Framework for Structural Health Monitoring.
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500 $a Source: Dissertations Abstracts International, Volume: 83-07, Section: B.
500 $a Advisor: Todd, Michael D.
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506 $a This item must not be sold to any third party vendors.
520 $a Over the life cycle of large civil infrastructure, there is often significant degradation in capability and design performance due to extend usage and, in many cases, deferred maintenance. The ultimate limit states in this case can result in unexpected failure, with potentially large economic or life-safety consequences. Structural health monitoring (SHM) is a framework for monitoring the structure over its life cycle, and this field has expanded rapidly in the past two to three decades due to the urgency of infrastructure aging beyond its design life. One of the critical challenges in a monitoring process is obtaining the most valuable information from the structure responses in the field under a continuous monitoring paradigm. This dissertation will present a new optimal sensor design framework, using Machine Learning (ML) techniques, including Gaussian Process Regression (GPR), Bayesian inference, uncertainty quantification, and Bayesian optimization, that guides risk-informed SHM system design. In particular, this dissertation: (1) demonstrates a framework for optimal sensor design using Bayes risk as the objective function; (2) further explores the framework using risk-weighted f-divergence functions and implements it in a real miter gate problem as a case study; (3) investigates the effects of sensor reliability over a life cycle monitoring of the miter gate for informing optimal sensor arrangement.
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