A Computing Model for Quantifying the Value of Structural Health Monitoring Information in Bridge Engineering
Structural health monitoring system can provide valuable information for improving decision-making process in maintenance and management of bridges. However, managers usually lack understanding of value of structural health monitoring information. This paper developed a computing model for quantifyi...
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2020-01-01
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2020/8260909 |
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doaj-ddbff8a903a1460fb5e2b839d57f2eea2020-11-25T03:41:15ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472020-01-01202010.1155/2020/82609098260909A Computing Model for Quantifying the Value of Structural Health Monitoring Information in Bridge EngineeringBaoquan Cheng0Lijie Wang1Jianling Huang2Xu Shi3Xiaodong Hu4Huihua Chen5School of Civil Engineering, Central South University, Changsha, Hunan 410083, ChinaDepartment of Architecture, Shijiazhuang Institute of Railway Technology, Shijiazhuang, Hebei 050041, ChinaSchool of Civil Engineering, Central South University, Changsha, Hunan 410083, ChinaCollege of Optoelectronic Engineering, Chongqing University, Chongqing 400044, ChinaSchool of Civil Engineering, Central South University, Changsha, Hunan 410083, ChinaSchool of Civil Engineering, Central South University, Changsha, Hunan 410083, ChinaStructural health monitoring system can provide valuable information for improving decision-making process in maintenance and management of bridges. However, managers usually lack understanding of value of structural health monitoring information. This paper developed a computing model for quantifying the value of structural health monitoring information based on Bayesian theory. Then, the model was demonstrated and validated using a simple case and the key factors (i.e., system accuracy, reparation cost, prior probability of structural failure, and manager’s behavior pattern) influencing the value of structural health monitoring information were identified and discussed. Findings from this study help to answer the question of whether a structural health monitoring system should be installed and run, thus enriching the knowledge body of structural health monitoring.http://dx.doi.org/10.1155/2020/8260909 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Baoquan Cheng Lijie Wang Jianling Huang Xu Shi Xiaodong Hu Huihua Chen |
spellingShingle |
Baoquan Cheng Lijie Wang Jianling Huang Xu Shi Xiaodong Hu Huihua Chen A Computing Model for Quantifying the Value of Structural Health Monitoring Information in Bridge Engineering Mathematical Problems in Engineering |
author_facet |
Baoquan Cheng Lijie Wang Jianling Huang Xu Shi Xiaodong Hu Huihua Chen |
author_sort |
Baoquan Cheng |
title |
A Computing Model for Quantifying the Value of Structural Health Monitoring Information in Bridge Engineering |
title_short |
A Computing Model for Quantifying the Value of Structural Health Monitoring Information in Bridge Engineering |
title_full |
A Computing Model for Quantifying the Value of Structural Health Monitoring Information in Bridge Engineering |
title_fullStr |
A Computing Model for Quantifying the Value of Structural Health Monitoring Information in Bridge Engineering |
title_full_unstemmed |
A Computing Model for Quantifying the Value of Structural Health Monitoring Information in Bridge Engineering |
title_sort |
computing model for quantifying the value of structural health monitoring information in bridge engineering |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2020-01-01 |
description |
Structural health monitoring system can provide valuable information for improving decision-making process in maintenance and management of bridges. However, managers usually lack understanding of value of structural health monitoring information. This paper developed a computing model for quantifying the value of structural health monitoring information based on Bayesian theory. Then, the model was demonstrated and validated using a simple case and the key factors (i.e., system accuracy, reparation cost, prior probability of structural failure, and manager’s behavior pattern) influencing the value of structural health monitoring information were identified and discussed. Findings from this study help to answer the question of whether a structural health monitoring system should be installed and run, thus enriching the knowledge body of structural health monitoring. |
url |
http://dx.doi.org/10.1155/2020/8260909 |
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