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Data Driven Framework for Structural...

Zhang, Tonghao,

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Data Driven Framework for Structural Health Monitoring of Concrete using Elastic Wave Method /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data Driven Framework for Structural Health Monitoring of Concrete using Elastic Wave Method // Tonghao Zhang.
Author:	Zhang, Tonghao,
Description:	1 electronic resource (138 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-01, Section: B.
Contained By:	Dissertations Abstracts International86-01B.
Subject:	Acoustics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31496206
ISBN:	9798382952963

Data Driven Framework for Structural Health Monitoring of Concrete using Elastic Wave Method /
Zhang, Tonghao,

Data Driven Framework for Structural Health Monitoring of Concrete using Elastic Wave Method /Tonghao Zhang. - 1 electronic resource (138 pages)

Source: Dissertations Abstracts International, Volume: 86-01, Section: B.

In this research, we explored the characteristics of elastic waves in relation to damage within heterogeneous and dispersive solid structures, specifically focusing on concrete. Current elastic wave-based Structural Health Monitoring (SHM) and Non-Destructive Evaluation (NDE) techniques, such as Acoustic Emission (AE) and Ultrasonic Testing (UT), are widely utilized for assessing damage in concrete structures. However, the linear nature of ultrasonic wavesand the heterogeneous characteristics of concrete often limit the effectiveness of these methods, especially for detecting defects smaller than half wavelength. To address these limitations, we developed a data-driven method that was both numerically and experimentally validated through tests on concrete samples ranging from small cubes to large-scale slabs. Our approach to enhancing the detection of defects in concrete structures includes: (i) Integrating linear and nonlinear ultrasonics into a single measurement system to detect multi-scale subsurface defects.(ii) Combining supervised and unsupervised learning techniques to classify entire signal patterns as physical mechanisms with increased accuracy. (iii) Implementing SHAP (SHapley Additive exPlanations) analysis to identify the most relevant single AE parameter that represents damage, thereby reducing data size. (iv) Employing cluster-based analysis to select signals that show the highest similarity, improving AE source localization accuracy in heterogeneous media. This proposed data-driven framework demonstrated significant improvements in defect detection within concrete structures.

English

ISBN: 9798382952963Subjects--Topical Terms:

879105
Acoustics.
Subjects--Index Terms:

Ultrasonics

Data Driven Framework for Structural Health Monitoring of Concrete using Elastic Wave Method /
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245 1 0 $a Data Driven Framework for Structural Health Monitoring of Concrete using Elastic Wave Method / $c Tonghao Zhang.
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300 $a 1 electronic resource (138 pages)
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500 $a Source: Dissertations Abstracts International, Volume: 86-01, Section: B.
500 $a Advisors: Ozevin, Didem Committee members: Issa, Mohsen; Chi, Sheng-Wei; Sneed, Lesley; Cetin, Ahmet Enis.
502 $b Ph.D. $c University of Illinois at Chicago $d 2024.
520 $a In this research, we explored the characteristics of elastic waves in relation to damage within heterogeneous and dispersive solid structures, specifically focusing on concrete. Current elastic wave-based Structural Health Monitoring (SHM) and Non-Destructive Evaluation (NDE) techniques, such as Acoustic Emission (AE) and Ultrasonic Testing (UT), are widely utilized for assessing damage in concrete structures. However, the linear nature of ultrasonic wavesand the heterogeneous characteristics of concrete often limit the effectiveness of these methods, especially for detecting defects smaller than half wavelength. To address these limitations, we developed a data-driven method that was both numerically and experimentally validated through tests on concrete samples ranging from small cubes to large-scale slabs. Our approach to enhancing the detection of defects in concrete structures includes: (i) Integrating linear and nonlinear ultrasonics into a single measurement system to detect multi-scale subsurface defects.(ii) Combining supervised and unsupervised learning techniques to classify entire signal patterns as physical mechanisms with increased accuracy. (iii) Implementing SHAP (SHapley Additive exPlanations) analysis to identify the most relevant single AE parameter that represents damage, thereby reducing data size. (iv) Employing cluster-based analysis to select signals that show the highest similarity, improving AE source localization accuracy in heterogeneous media. This proposed data-driven framework demonstrated significant improvements in defect detection within concrete structures.
546 $a English
590 $a School code: 0799
650 4 $a Acoustics. $3 879105
650 4 $a Materials science. $3 543314
653 $a Ultrasonics
653 $a Acoustic emission
653 $a Concrete
653 $a Damage detection
653 $a Machine learning
690 $a 0986
690 $a 0543
690 $a 0794
710 2 $a University of Illinois at Chicago. $b Department of Civil, Materials, and Environmental Engineering. $e degree granting institution. $3 3815925
720 1 $a Ozevin, Didem $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-01B.
790 $a 0799
791 $a Ph.D.
792 $a 2024
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