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Feature and Statistical Model Develo...

Kim, Inho.

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Feature and Statistical Model Development in Structural Health Monitoring.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Feature and Statistical Model Development in Structural Health Monitoring./
Author:	Kim, Inho.
Description:	179 p.
Notes:	Source: Dissertation Abstracts International, Volume: 77-10(E), Section: B.
Contained By:	Dissertation Abstracts International77-10B(E).
Subject:	Mechanical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10108842
ISBN:	9781339721767

Feature and Statistical Model Development in Structural Health Monitoring.
Kim, Inho.

Feature and Statistical Model Development in Structural Health Monitoring. - 179 p.

Source: Dissertation Abstracts International, Volume: 77-10(E), Section: B.

Thesis (Ph.D.)--Arizona State University, 2016.

All structures suffer wear and tear because of impact, excessive load, fatigue, corrosion, etc. in addition to inherent defects during their manufacturing processes and their exposure to various environmental effects. These structural degradations are often imperceptible, but they can severely affect the structural performance of a component, thereby severely decreasing its service life. Although previous studies of Structural Health Monitoring (SHM) have revealed extensive prior knowledge on the parts of SHM processes, such as the operational evaluation, data processing, and feature extraction, few studies have been conducted from a systematical perspective, the statistical model development.

ISBN: 9781339721767Subjects--Topical Terms:

649730
Mechanical engineering.

Feature and Statistical Model Development in Structural Health Monitoring.
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020 $a 9781339721767
035 $a (MiAaPQ)AAI10108842
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100 1 $a Kim, Inho. $3 1678833
245 1 0 $a Feature and Statistical Model Development in Structural Health Monitoring.
300 $a 179 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-10(E), Section: B.
500 $a Adviser: Aditi Chattopadhyay.
502 $a Thesis (Ph.D.)--Arizona State University, 2016.
520 $a All structures suffer wear and tear because of impact, excessive load, fatigue, corrosion, etc. in addition to inherent defects during their manufacturing processes and their exposure to various environmental effects. These structural degradations are often imperceptible, but they can severely affect the structural performance of a component, thereby severely decreasing its service life. Although previous studies of Structural Health Monitoring (SHM) have revealed extensive prior knowledge on the parts of SHM processes, such as the operational evaluation, data processing, and feature extraction, few studies have been conducted from a systematical perspective, the statistical model development.
520 $a The first part of this dissertation, the characteristics of inverse scattering problems, such as ill-posedness and nonlinearity, reviews ultrasonic guided wave-based structural health monitoring problems. The distinctive features and the selection of the domain analysis are investigated by analytically searching the conditions of the uniqueness solutions for ill-posedness and are validated experimentally.
520 $a Based on the distinctive features, a novel wave packet tracing (WPT) method for damage localization and size quantification is presented. This method involves creating time-space representations of the guided Lamb waves (GLWs), collected at a series of locations, with a spatially dense distribution along paths at pre-selected angles with respect to the direction, normal to the direction of wave propagation. The fringe patterns due to wave dispersion, which depends on the phase velocity, are selected as the primary features that carry information, regarding the wave propagation and scattering.
520 $a The following part of this dissertation presents a novel damage-localization framework, using a fully automated process. In order to construct the statistical model for autonomous damage localization deep-learning techniques, such as restricted Boltzmann machine and deep belief network, are trained and utilized to interpret nonlinear far-field wave patterns.
520 $a Next, a novel bridge scour estimation approach that comprises advantages of both empirical and data-driven models is developed. Two field datasets from the literature are used, and a Support Vector Machine (SVM), a machine-learning algorithm, is used to fuse the field data samples and classify the data with physical phenomena. The Fast Non-dominated Sorting Genetic Algorithm (NSGA-II) is evaluated on the model performance objective functions to search for Pareto optimal fronts.
590 $a School code: 0010.
650 4 $a Mechanical engineering. $3 649730
650 4 $a Aerospace engineering. $3 1002622
650 4 $a Civil engineering. $3 860360
690 $a 0548
690 $a 0538
690 $a 0543
710 2 $a Arizona State University. $b Mechanical Engineering. $3 1675155
773 0 $t Dissertation Abstracts International $g 77-10B(E).
790 $a 0010
791 $a Ph.D.
792 $a 2016
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10108842