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Diagnosing Masonry Structures Using ...

Napolitano, Rebecac Kristine.

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Diagnosing Masonry Structures Using Advanced Sensing Techniques, Physics-based Modeling, and Supervised Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Diagnosing Masonry Structures Using Advanced Sensing Techniques, Physics-based Modeling, and Supervised Learning./
作者:	Napolitano, Rebecac Kristine.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	168 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-01, Section: B.
Contained By:	Dissertations Abstracts International82-01B.
標題:	Architectural engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27994349
ISBN:	9798662419018

Diagnosing Masonry Structures Using Advanced Sensing Techniques, Physics-based Modeling, and Supervised Learning.
Napolitano, Rebecac Kristine.

Diagnosing Masonry Structures Using Advanced Sensing Techniques, Physics-based Modeling, and Supervised Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 168 p.

Source: Dissertations Abstracts International, Volume: 82-01, Section: B.

Thesis (Ph.D.)--Princeton University, 2020.

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

Continued advances in computational, networking, and sensor technologies have begun to bring urban-scale structural health monitoring and prognostics into the realm of possibility. We are starting to think about how events can not only impact single buildings, but also how a city can operate as a network of sensors and how we can leverage urban-scale digital twins to develop smart, sustainable cities. In that city-scale digital twin however, aging infrastructure introduces a myriad of unknowns such as material condition, load paths, etc.So what should we do with our aging infrastructure? What is the place of buildings of the past in cities of the future?To answer these questions, my research focuses on minimizing uncertainties regarding the condition of aging infrastructure through new methods and applications of data extraction, multi-modal data fusion, hybrid analytics, and information modeling. I have had success creating a novel method for integrating non-destructive testing, laser scanning, and numerical modeling for damage assessment which reduces the time required for a practitioner to assess the condition as well as decreases the likelihood of diagnostic bias affecting the resulting decisions. Additionally, I have quantified how different levels of detail for modeling and simulation can affect the results of diagnostics. I found that in some cases more detailed simulations can be 100 times slower; thus practitioners should think carefully about what they need from a model before running simulations. Additionally, it was shown that if the geometry and initial conditions are not captured accurately, these can skew the results of diagnostics. Lastly, I have developed a new technique for combining physics-based modeling and machine learning tools for diagnostics of masonry which is 105 times faster than prior methods and requires less human intervention. These results illustrate that computational tools can increase the efficiency and decrease the time associated with diagnostics of existing infrastructure. Additionally, these results show how new, digital solutions for diagnostics will play a critical role in preservation and reuse efforts for developing the next generation of smart and green infrastructure.

ISBN: 9798662419018Subjects--Topical Terms:

3174102
Architectural engineering.
Subjects--Index Terms:

Masonry structures

Diagnosing Masonry Structures Using Advanced Sensing Techniques, Physics-based Modeling, and Supervised Learning.
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500 $a Source: Dissertations Abstracts International, Volume: 82-01, Section: B.
500 $a Advisor: Glisic, Branko.
502 $a Thesis (Ph.D.)--Princeton University, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Continued advances in computational, networking, and sensor technologies have begun to bring urban-scale structural health monitoring and prognostics into the realm of possibility. We are starting to think about how events can not only impact single buildings, but also how a city can operate as a network of sensors and how we can leverage urban-scale digital twins to develop smart, sustainable cities. In that city-scale digital twin however, aging infrastructure introduces a myriad of unknowns such as material condition, load paths, etc.So what should we do with our aging infrastructure? What is the place of buildings of the past in cities of the future?To answer these questions, my research focuses on minimizing uncertainties regarding the condition of aging infrastructure through new methods and applications of data extraction, multi-modal data fusion, hybrid analytics, and information modeling. I have had success creating a novel method for integrating non-destructive testing, laser scanning, and numerical modeling for damage assessment which reduces the time required for a practitioner to assess the condition as well as decreases the likelihood of diagnostic bias affecting the resulting decisions. Additionally, I have quantified how different levels of detail for modeling and simulation can affect the results of diagnostics. I found that in some cases more detailed simulations can be 100 times slower; thus practitioners should think carefully about what they need from a model before running simulations. Additionally, it was shown that if the geometry and initial conditions are not captured accurately, these can skew the results of diagnostics. Lastly, I have developed a new technique for combining physics-based modeling and machine learning tools for diagnostics of masonry which is 105 times faster than prior methods and requires less human intervention. These results illustrate that computational tools can increase the efficiency and decrease the time associated with diagnostics of existing infrastructure. Additionally, these results show how new, digital solutions for diagnostics will play a critical role in preservation and reuse efforts for developing the next generation of smart and green infrastructure.
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