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From cognitive bias toward advanced computational intelligence for smart infrastructure monitoring

Visual inspections have been typically used in condition assessment of infrastructure. However, they are based on human judgment and their interpretation of data can differ from acquired results. In psychology, this difference is called cognitive bias which directly affects Structural Health Monitor...

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Main Authors: Gordan, Meisam, Ong, Zhi Chao, Sabbagh-Yazdi, Saeed-Reza, Lai, Khin Wee, Ghaedi, Khaled, Ismail, Zubaidah
Format: Article
Published: Frontiers Media 2022
Subjects:
BF Psychology
Q Science (General)
TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
Online Access:http://eprints.um.edu.my/42958/
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spelling my.um.eprints.429582023-09-10T02:58:44Z http://eprints.um.edu.my/42958/ From cognitive bias toward advanced computational intelligence for smart infrastructure monitoring Gordan, Meisam Ong, Zhi Chao Sabbagh-Yazdi, Saeed-Reza Lai, Khin Wee Ghaedi, Khaled Ismail, Zubaidah BF Psychology Q Science (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery Visual inspections have been typically used in condition assessment of infrastructure. However, they are based on human judgment and their interpretation of data can differ from acquired results. In psychology, this difference is called cognitive bias which directly affects Structural Health Monitoring (SHM)-based decision making. Besides, the confusion between condition state and safety of a bridge is another example of cognitive bias in bridge monitoring. Therefore, integrated computer-based approaches as powerful tools can be significantly applied in SHM systems. This paper explores the relationship between the use of advanced computational intelligence and the development of SHM solutions through conducting an infrastructure monitoring methodology. Artificial Intelligence (AI)-based algorithms, i.e., Artificial Neural Network (ANN), hybrid ANN-based Imperial Competitive Algorithm, and hybrid ANN-based Genetic Algorithm, are developed for damage assessment using a lab-scale composite bridge deck structure. Based on the comparison of the results, the employed evolutionary algorithms could improve the prediction error of the pre-developed network by enhancing the learning procedure of the ANN. Frontiers Media 2022-03 Article PeerReviewed Gordan, Meisam and Ong, Zhi Chao and Sabbagh-Yazdi, Saeed-Reza and Lai, Khin Wee and Ghaedi, Khaled and Ismail, Zubaidah (2022) From cognitive bias toward advanced computational intelligence for smart infrastructure monitoring. Frontiers in Psychology, 13. ISSN 1664-1078, DOI https://doi.org/10.3389/fpsyg.2022.846610 <https://doi.org/10.3389/fpsyg.2022.846610>. 10.3389/fpsyg.2022.846610
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic BF Psychology
Q Science (General)
TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
spellingShingle BF Psychology
Q Science (General)
TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
Gordan, Meisam
Ong, Zhi Chao
Sabbagh-Yazdi, Saeed-Reza
Lai, Khin Wee
Ghaedi, Khaled
Ismail, Zubaidah
From cognitive bias toward advanced computational intelligence for smart infrastructure monitoring
description Visual inspections have been typically used in condition assessment of infrastructure. However, they are based on human judgment and their interpretation of data can differ from acquired results. In psychology, this difference is called cognitive bias which directly affects Structural Health Monitoring (SHM)-based decision making. Besides, the confusion between condition state and safety of a bridge is another example of cognitive bias in bridge monitoring. Therefore, integrated computer-based approaches as powerful tools can be significantly applied in SHM systems. This paper explores the relationship between the use of advanced computational intelligence and the development of SHM solutions through conducting an infrastructure monitoring methodology. Artificial Intelligence (AI)-based algorithms, i.e., Artificial Neural Network (ANN), hybrid ANN-based Imperial Competitive Algorithm, and hybrid ANN-based Genetic Algorithm, are developed for damage assessment using a lab-scale composite bridge deck structure. Based on the comparison of the results, the employed evolutionary algorithms could improve the prediction error of the pre-developed network by enhancing the learning procedure of the ANN.
format Article
author Gordan, Meisam
Ong, Zhi Chao
Sabbagh-Yazdi, Saeed-Reza
Lai, Khin Wee
Ghaedi, Khaled
Ismail, Zubaidah
author_facet Gordan, Meisam
Ong, Zhi Chao
Sabbagh-Yazdi, Saeed-Reza
Lai, Khin Wee
Ghaedi, Khaled
Ismail, Zubaidah
author_sort Gordan, Meisam
title From cognitive bias toward advanced computational intelligence for smart infrastructure monitoring
title_short From cognitive bias toward advanced computational intelligence for smart infrastructure monitoring
title_full From cognitive bias toward advanced computational intelligence for smart infrastructure monitoring
title_fullStr From cognitive bias toward advanced computational intelligence for smart infrastructure monitoring
title_full_unstemmed From cognitive bias toward advanced computational intelligence for smart infrastructure monitoring
title_sort from cognitive bias toward advanced computational intelligence for smart infrastructure monitoring
publisher Frontiers Media
publishDate 2022
url http://eprints.um.edu.my/42958/
_version_ 1778161685140340736
score 13.211869

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