國立虎尾科技大學 |

Cable-Stayed Bridge Conditon Evaluations by Data Analysis Methodologies Based on Structural Health Monitoring System.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Cable-Stayed Bridge Conditon Evaluations by Data Analysis Methodologies Based on Structural Health Monitoring System./
Author:	Chen, Jian.
Description:	1 online resource (173 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-09, Section: B.
Contained By:	Dissertations Abstracts International84-09B.
Subject:	Civil engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798377640516

Cable-Stayed Bridge Conditon Evaluations by Data Analysis Methodologies Based on Structural Health Monitoring System.
Chen, Jian.

Cable-Stayed Bridge Conditon Evaluations by Data Analysis Methodologies Based on Structural Health Monitoring System. - 1 online resource (173 pages)

Source: Dissertations Abstracts International, Volume: 84-09, Section: B.

Thesis (Ph.D.)--University of Delaware, 2023.

Includes bibliographical references

For transportation agencies or private bridge owners, damage detection is a key interest as they manage their structural assets. In the past, most damage was detected using periodic visual inspection, which is time-consuming, expensive, and subjective. The advent of Structural Health Monitoring (SHM) systems which can capture the real-time response of structures can help bridge owners evaluate their bridges for potential life-safety issues and help them mitigate economic loss. The goal of this study is to develop methodologies that can be used to evaluate bridge condition efficiently by monitoring bearing displacements, strains, and cable tensions. These explicit parameters can be easily understood and used by bridge owners. In so doing, the Indian River Inlet Bride (IRIB), and data collected from its SHM system, were used to evaluate the methodologies developed. Different characteristics of the IRIB were analyzed using machine learning algorithms and statistical modeling. Direct and indirect criteria that transportation agencies can easily interpret were generated and calculated. The effectiveness and sensitivity of the proposed methodologies were validated by damage simulation. The evaluations incorporated the monitoring of bearing displacements and strain peaks, as well as cable tension tracking under critical wind load conditions. The approached developed can be used to improve the structure inspection and evaluation process, thereby strengthening the owners structural management process. The dissertation research resulted in four papers written by the author. These papers will be presented in the distinct chapters of this dissertation. In Chapter 4, bearing displacements were predicted using ANN. The same algorithm is applied to deck strains in Chapter 6. Both of these chapters focus on real-time monitoring. In Chapter 5, statistical modeling was applied to strain data to evaluate bridge condition in long-term perspective, The final paper is pressented in Chapter 7 and it is focused on cable tension and natural frequencies monitoring. Various conclusions were made based on the research. The first conclusion made by analyzing bearing displacements is that they can be accurately predicted using distributed thermal loads on the bridge. There is a nonlinear relationship between the thermal loads and bearing displacements. The second conclusion is that the abnormal responses of the bridge can be detected by comparing Strain Threshold Indexes (STIs) with Decision Boundaries (DBs). Statistical analysis was applied to truck-induced strain peaks, and corresponding STIs and DBs were calculated. The truck-induced strain peaks were also analyzed by Artificial Neural Network (ANN). The trained ANNs represented the relationship between strain gages at different locations on the bridge. The foundation idea is that the relationship among their strain peaks should be stable when a truck passes over a bridge and the bridge condition does not change. Otherwise, the response of the bridge under abnormal conditions does not match with the prediction of the strain ANNs. A mismatch will cause large Prediction Errors (PEs), and an abnormal responses warning will be triggered. The final conclusion is that the cable tension estimation process is significantly influenced by wind load conditions. By identifying the general critical wind load conditions using Decision Trees (DTs), cable tensions can be estimated using just two hours of data. The computational load and storage requirements can be heavily reduced by limiting the data to two hours. In summary, bearing displacements, truck-induced strain peaks, and cable tensions were monitored closely using a variety of proposed methodologies, and each methodology has shown promise in adding to the way in which SHM data can be used for assessing bridge conditions.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798377640516Subjects--Topical Terms:

561339
Civil engineering.
Subjects--Index Terms:

Deep learningIndex Terms--Genre/Form:

554714
Electronic books.

Cable-Stayed Bridge Conditon Evaluations by Data Analysis Methodologies Based on Structural Health Monitoring System.
LDR:05315ntm a22003977 4500 001 1141692
005 20240414211447.5
006 m o d
007 cr mn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798377640516
035 $a (MiAaPQ)AAI30245833
035 $a AAI30245833
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Chen, Jian. $3 1114540
245 1 0 $a Cable-Stayed Bridge Conditon Evaluations by Data Analysis Methodologies Based on Structural Health Monitoring System.
264 0 $c 2023
300 $a 1 online resource (173 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 84-09, Section: B.
500 $a Advisor: Chajes, Michael J.;Shenton, Harry W., III.
502 $a Thesis (Ph.D.)--University of Delaware, 2023.
504 $a Includes bibliographical references
520 $a For transportation agencies or private bridge owners, damage detection is a key interest as they manage their structural assets. In the past, most damage was detected using periodic visual inspection, which is time-consuming, expensive, and subjective. The advent of Structural Health Monitoring (SHM) systems which can capture the real-time response of structures can help bridge owners evaluate their bridges for potential life-safety issues and help them mitigate economic loss. The goal of this study is to develop methodologies that can be used to evaluate bridge condition efficiently by monitoring bearing displacements, strains, and cable tensions. These explicit parameters can be easily understood and used by bridge owners. In so doing, the Indian River Inlet Bride (IRIB), and data collected from its SHM system, were used to evaluate the methodologies developed. Different characteristics of the IRIB were analyzed using machine learning algorithms and statistical modeling. Direct and indirect criteria that transportation agencies can easily interpret were generated and calculated. The effectiveness and sensitivity of the proposed methodologies were validated by damage simulation. The evaluations incorporated the monitoring of bearing displacements and strain peaks, as well as cable tension tracking under critical wind load conditions. The approached developed can be used to improve the structure inspection and evaluation process, thereby strengthening the owners structural management process. The dissertation research resulted in four papers written by the author. These papers will be presented in the distinct chapters of this dissertation. In Chapter 4, bearing displacements were predicted using ANN. The same algorithm is applied to deck strains in Chapter 6. Both of these chapters focus on real-time monitoring. In Chapter 5, statistical modeling was applied to strain data to evaluate bridge condition in long-term perspective, The final paper is pressented in Chapter 7 and it is focused on cable tension and natural frequencies monitoring. Various conclusions were made based on the research. The first conclusion made by analyzing bearing displacements is that they can be accurately predicted using distributed thermal loads on the bridge. There is a nonlinear relationship between the thermal loads and bearing displacements. The second conclusion is that the abnormal responses of the bridge can be detected by comparing Strain Threshold Indexes (STIs) with Decision Boundaries (DBs). Statistical analysis was applied to truck-induced strain peaks, and corresponding STIs and DBs were calculated. The truck-induced strain peaks were also analyzed by Artificial Neural Network (ANN). The trained ANNs represented the relationship between strain gages at different locations on the bridge. The foundation idea is that the relationship among their strain peaks should be stable when a truck passes over a bridge and the bridge condition does not change. Otherwise, the response of the bridge under abnormal conditions does not match with the prediction of the strain ANNs. A mismatch will cause large Prediction Errors (PEs), and an abnormal responses warning will be triggered. The final conclusion is that the cable tension estimation process is significantly influenced by wind load conditions. By identifying the general critical wind load conditions using Decision Trees (DTs), cable tensions can be estimated using just two hours of data. The computational load and storage requirements can be heavily reduced by limiting the data to two hours. In summary, bearing displacements, truck-induced strain peaks, and cable tensions were monitored closely using a variety of proposed methodologies, and each methodology has shown promise in adding to the way in which SHM data can be used for assessing bridge conditions.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Civil engineering. $3 561339
650 4 $a Engineering. $3 561152
653 $a Deep learning
653 $a Machine learning
653 $a Non destructive tests
653 $a Statistic modeling
653 $a Structural Heath Monitoring
655 7 $a Electronic books. $2 local $3 554714
690 $a 0543
690 $a 0537
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Delaware. $b Civil and Environmental Engineering. $3 1182920
773 0 $t Dissertations Abstracts International $g 84-09B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30245833 $z click for full text (PQDT)