Review of artificial intelligence-based bridge damage detection

Bridges are often located in harsh environments and are thus extremely susceptible to damage. If the initial damage is not detected in time, it can develop further causing safety hazards. Therefore, accurate detection of bridge damage is an important topic. In recent years, artificial intelligence t...

Full description

Saved in:
Bibliographic Details
Published inAdvances in mechanical engineering Vol. 14; no. 9
Main Authors Zhang, Yang, Yuen, Ka-Veng
Format Journal Article
LanguageEnglish
Published London, England SAGE Publications 01.09.2022
Sage Publications Ltd
SAGE Publishing
Subjects
Algorithms
Artificial intelligence
Bridge inspection
Damage detection
Data processing
Ground penetrating radar
Infrared imagery
Infrared imaging
Infrared radar
Machine learning
Radar imaging
Structural health monitoring
Thermal imaging
Vibration response
damage detection
sensor data
machine learning
Bridge
artificial intelligence
Online AccessGet full text

Cover

Abstract Bridges are often located in harsh environments and are thus extremely susceptible to damage. If the initial damage is not detected in time, it can develop further causing safety hazards. Therefore, accurate detection of bridge damage is an important topic. In recent years, artificial intelligence technology has been developed rapidly, especially machine learning algorithms, which have shown amazing results in various fields while it also received attention in bridge inspection. This paper summarizes the progress of bridge damage detection research related to artificial intelligence techniques between 2015 and 2021. For structural health monitoring, sensing data is the basis for various data processing methods. The strength and weakness of the sensing data itself directly affect the effectiveness of subsequent processing methods. As a result, this paper classifies bridge damage detection studies into six categories from the types of sensing data: visual image, point cloud, infrared thermal imaging, ground-penetrating radar, vibration response, and other types of data. These six types of damage detection methods were reviewed and summarized respectively. Finally, challenges and future trends were discussed.
AbstractList Bridges are often located in harsh environments and are thus extremely susceptible to damage. If the initial damage is not detected in time, it can develop further causing safety hazards. Therefore, accurate detection of bridge damage is an important topic. In recent years, artificial intelligence technology has been developed rapidly, especially machine learning algorithms, which have shown amazing results in various fields while it also received attention in bridge inspection. This paper summarizes the progress of bridge damage detection research related to artificial intelligence techniques between 2015 and 2021. For structural health monitoring, sensing data is the basis for various data processing methods. The strength and weakness of the sensing data itself directly affect the effectiveness of subsequent processing methods. As a result, this paper classifies bridge damage detection studies into six categories from the types of sensing data: visual image, point cloud, infrared thermal imaging, ground-penetrating radar, vibration response, and other types of data. These six types of damage detection methods were reviewed and summarized respectively. Finally, challenges and future trends were discussed.
Author Zhang, Yang
Yuen, Ka-Veng
Author_xml – sequence: 1
  givenname: Yang
  surname: Zhang
  fullname: Zhang, Yang
– sequence: 2
  givenname: Ka-Veng
  orcidid: 0000-0002-1755-6668
  surname: Yuen
  fullname: Yuen, Ka-Veng
  email: kvyuen@um.edu.mo
BookMark eNp9kMtKxjAQhYMoeH0AdwXX1UzaJM1KRLyBIIiuwySZ_kRqo2lVfHtbfy-g6GqG4XyHM2eTrfapJ8Z2ge8DaH0AqtENVEIIACG05itsY76VDdR89WuvxDrbGYbouOSKc2XMBju8pudIL0VqC8xjbKOP2BWxH6nr4oJ6T6XDgULhcgwLKgLe4zxoJD_G1G-ztRa7gXY-5ha7PT25OT4vL6_OLo6PLktfCxhLF5xyvpFQU62F4wCgpDeBdJDeV5LqSjSGt147MCidbx2KIBX3qnLe82qLXSx9Q8I7-5DjPeZXmzDa90PKCzvn9x1ZaAQGNEE3wtTQGiRdNVJhzVtOPJjJa2_p9ZDT4xMNo71LT7mf4luhwSgpNIdJpZcqn9MwZGqtjyPOP48ZY2eB27l8-6v8iYQf5Gfe_5j9JTNM9X7n-Rt4A2pVk4g
CitedBy_id crossref_primary_10_3390_buildings13010055
crossref_primary_10_1016_j_autcon_2023_105141
crossref_primary_10_1016_j_autcon_2025_106116
crossref_primary_10_1016_j_engappai_2023_106721
crossref_primary_10_1177_16878132231169408
crossref_primary_10_3390_buildings12091463
crossref_primary_10_1016_j_measurement_2023_112465
crossref_primary_10_3390_app13010140
crossref_primary_10_1016_j_measurement_2024_116587
crossref_primary_10_1016_j_engstruct_2025_119714
crossref_primary_10_1016_j_engstruct_2023_117413
crossref_primary_10_1016_j_neucom_2025_129907
crossref_primary_10_1007_s13349_025_00920_2
crossref_primary_10_1016_j_autcon_2025_105961
crossref_primary_10_1115_1_4065268
crossref_primary_10_1177_16878132231159523
crossref_primary_10_3390_buildings14103146
crossref_primary_10_1080_15732479_2025_2456979
crossref_primary_10_1016_j_engstruct_2022_115575
crossref_primary_10_3390_w17030299
crossref_primary_10_1002_cepa_2005
crossref_primary_10_1007_s13349_024_00856_z
crossref_primary_10_3390_buildings13030800
crossref_primary_10_1016_j_ymssp_2023_110702
crossref_primary_10_1016_j_prostr_2024_09_013
crossref_primary_10_1186_s43251_023_00098_x
crossref_primary_10_3390_sym14112384
crossref_primary_10_48084_etasr_5958
crossref_primary_10_1016_j_heliyon_2024_e38104
crossref_primary_10_1007_s00158_023_03668_9
crossref_primary_10_1016_j_jsv_2024_118597
crossref_primary_10_1177_14759217241246953
crossref_primary_10_1177_14759217231166116
crossref_primary_10_1155_2024_3185640
crossref_primary_10_3390_rs16152711
crossref_primary_10_3390_app13010097
crossref_primary_10_1016_j_autcon_2024_105955
crossref_primary_10_1038_s41598_024_82612_3
crossref_primary_10_1155_2023_5687265
crossref_primary_10_3389_fphy_2023_1290880
crossref_primary_10_1016_j_autcon_2023_104937
crossref_primary_10_1016_j_istruc_2024_107048
crossref_primary_10_1109_JSEN_2024_3366346
crossref_primary_10_1016_j_prostr_2024_09_241
crossref_primary_10_3390_su15021509
crossref_primary_10_1016_j_ymssp_2023_111046
crossref_primary_10_1016_j_trgeo_2024_101273
Cites_doi 10.1016/j.autcon.2019.02.013
10.5194/nhess-17-1393-2017
10.1109/ICSAI53574.2021.9664156
10.1088/1742-6596/1626/1/012151
10.1002/stc.2966
10.1007/s13349-020-00427-y
10.1080/15732479.2020.1734632
10.1016/j.ymssp.2019.106294
10.1016/j.engstruct.2018.06.094
10.1061/(ASCE)BE.1943-5592.0001343
10.3390/rs12233852
10.1177/03611981211067978
10.12989/smm.2016.3.1.001
10.1016/j.ndteint.2020.102341
10.3390/app7050510
10.1007/s42421-020-00020-1
10.3389/fbuil.2021.627058
10.1177/1475921718821719
10.1016/j.measurement.2020.108077
10.1109/TITS.2020.2980864
10.1016/j.ssci.2022.105689
10.1109/ISC2.2018.8656971
10.1002/9781119515326.ch4
10.3389/fbuil.2017.00004
10.1061/(ASCE)CF.1943-5509.0001541
10.1177/1475921718757405
10.1007/s13735-017-0141-z
10.1016/j.ymssp.2022.108913
10.1002/stc.2416
10.1111/mice.12804
10.1177/1475921721989407
10.1111/mice.12753
10.1111/mice.12263
10.1016/j.engstruct.2021.113250
10.1016/j.conbuildmat.2015.06.065
10.3390/app11020518
10.7717/peerj-cs.783
10.1002/stc.2296
10.1142/S0219455418500256
10.1061/9780784482445.007
10.1109/CVPR.2015.7298965
10.1016/j.engstruct.2022.114418
10.1007/s13349-015-0108-9
10.1109/5.726791
10.1109/CVPR.2014.81
10.1016/j.jrmge.2014.01.007
10.1007/978-3-319-24574-4_28
10.1016/j.aei.2019.100922
10.3390/su132011359
10.3390/fractalfract5040142
10.3390/rs12223757
10.3390/s19184035
10.1080/15732479.2019.1650077
10.1109/TITS.2019.2929020
10.1201/b12352-320
10.1016/j.compind.2019.08.002
10.3390/rs13091846
10.1016/j.jappgeo.2013.04.009
10.3390/s22134964
10.1111/mice.12635
10.1117/12.2580575
10.1061/(ASCE)CO.1943-7862.0001895
10.1007/s12205-019-2012-z
10.1007/s11042-022-12703-8
10.1007/s13735-020-00195-x
10.1177/0278364911434148
10.1109/ACCESS.2021.3064205
10.1016/j.engstruct.2022.114474
10.1177/1475921720916928
10.1109/CVPR52688.2022.01641
10.1007/978-3-030-81716-9_2
10.3390/s18124206
10.1061/(ASCE)CF.1943-5509.0001712
10.1155/2015/286139
10.1016/j.engstruct.2022.114059
10.1016/j.autcon.2021.103634
10.1186/s43251-020-00006-7
10.1016/j.jii.2021.100224
10.1016/j.autcon.2021.103992
10.1016/j.conbuildmat.2019.07.293
10.1109/ICCV.2017.324
10.1109/CVPR.2016.90
10.1016/j.conbuildmat.2022.126686
10.1007/s10921-018-0546-5
10.1016/j.engappai.2012.01.012
10.1109/CVPR.2016.91
10.1016/j.aei.2019.02.007
10.1016/j.measurement.2020.108048
10.1109/CVPR.2017.195
10.15302/J-SSCAE-2017.06.005
10.1016/j.autcon.2021.103847
10.1177/1475921720924601
10.1061/(ASCE)1084-0680(2004)9:1(16)
10.1109/Confluence51648.2021.9377100
10.2478/heem-2021-0005
10.3390/s21144942
10.1016/j.autcon.2019.102973
10.1109/ACCESS.2021.3105279
10.1016/j.conbuildmat.2019.07.320
10.4203/csets.32.3
10.1002/stc.2663
10.1109/CVPR.2015.7298594
10.1016/j.ymssp.2022.109049
10.1109/TPAMI.2016.2644615
10.1111/mice.12425
10.3390/jmse9060671
10.1002/stc.2991
10.1061/(ASCE)CF.1943-5509.0001530
10.1002/stc.1937
10.31814/stce.nuce2019-13(3)-02
10.1007/978-3-030-81716-9_11
10.3390/drones6030064
10.1177/1748006X20965111
10.1109/TPAMI.2017.2699184
10.1109/ICCV48922.2021.01595
10.1016/j.ymssp.2019.106495
10.1061/(ASCE)ST.1943-541X.0002535
10.1016/j.autcon.2022.104229
10.1002/stc.2591
10.4236/jtts.2020.102007
10.1061/(ASCE)BE.1943-5592.0001302
10.1002/stc.2983
10.1016/j.autcon.2022.104249
10.1260/1369433011502372
ContentType Journal Article
Copyright The Author(s) 2022
The Author(s) 2022. This work is licensed under the Creative Commons Attribution License https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: The Author(s) 2022
– notice: The Author(s) 2022. This work is licensed under the Creative Commons Attribution License https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID AFRWT
AAYXX
CITATION
7TB
8FD
8FE
8FG
ABJCF
ABUWG
AFKRA
AZQEC
BENPR
BGLVJ
CCPQU
DWQXO
FR3
H8D
HCIFZ
L6V
L7M
M7S
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
PRINS
PTHSS
DOA
DOI 10.1177/16878132221122770
DatabaseName Sage Journals GOLD Open Access 2024
CrossRef
Mechanical & Transportation Engineering Abstracts
Technology Research Database
ProQuest SciTech Collection
ProQuest Technology Collection
Materials Science & Engineering Collection
ProQuest Central
ProQuest Central UK/Ireland
ProQuest Central Essentials
ProQuest Central
Technology Collection
ProQuest One
ProQuest Central
Engineering Research Database
Aerospace Database
SciTech Premium Collection
ProQuest Engineering Collection
Advanced Technologies Database with Aerospace
Engineering Database
ProQuest Central Premium
ProQuest One Academic (New)
Publicly Available Content Database
ProQuest One Academic Middle East (New)
ProQuest One Academic Eastern Edition (DO NOT USE)
ProQuest One Applied & Life Sciences
ProQuest One Academic
ProQuest One Academic UKI Edition
ProQuest Central China
Engineering collection
DOAJ (Directory of Open Access Journals)
DatabaseTitle CrossRef
Publicly Available Content Database
Technology Collection
Technology Research Database
ProQuest One Academic Middle East (New)
Mechanical & Transportation Engineering Abstracts
ProQuest Central Essentials
ProQuest Central (Alumni Edition)
SciTech Premium Collection
ProQuest One Community College
ProQuest Central China
ProQuest Central
ProQuest One Applied & Life Sciences
Aerospace Database
ProQuest Engineering Collection
ProQuest Central Korea
ProQuest Central (New)
Advanced Technologies Database with Aerospace
Engineering Collection
Engineering Database
ProQuest One Academic Eastern Edition
ProQuest Technology Collection
ProQuest SciTech Collection
ProQuest One Academic UKI Edition
Materials Science & Engineering Collection
Engineering Research Database
ProQuest One Academic
ProQuest One Academic (New)
DatabaseTitleList
Publicly Available Content Database
CrossRef
Database_xml – sequence: 1
  dbid: DOA
  name: DOAJ: Directory of Open Access Journal (DOAJ)
  url: https://www.doaj.org/
  sourceTypes: Open Website
– sequence: 2
  dbid: AFRWT
  name: Sage Journals GOLD Open Access 2024
  url: http://journals.sagepub.com/
  sourceTypes: Publisher
– sequence: 3
  dbid: 8FG
  name: ProQuest Technology Collection
  url: https://search.proquest.com/technologycollection1
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISSN 1687-8140
ExternalDocumentID oai_doaj_org_article_182ada9d782941f9ae73856a40f0e0d9
10_1177_16878132221122770
10.1177_16878132221122770
GrantInformation_xml – fundername: Guangdong-Hong Kong-Macau Joint Laboratory Program
  grantid: 2020B1212030009
– fundername: Science and Technology Development Fund of the Macau SAR
  grantid: SKL-IOTSC(UM)-2021-2023
– fundername: Science and Technology Project of State Administration for Market Regulation
  grantid: 2021MK044
GroupedDBID .DC
0R~
188
23M
2UF
2WC
4.4
54M
5GY
5VS
8FE
8FG
8R4
8R5
AAJPV
AASGM
ABAWP
ABJCF
ABQXT
ACGFS
ACIWK
ACROE
ADBBV
ADOGD
AEDFJ
AENEX
AEUHG
AEWDL
AFCOW
AFKRA
AFKRG
AFRWT
AINHJ
AJUZI
ALMA_UNASSIGNED_HOLDINGS
AUTPY
AYAKG
BCNDV
BDDNI
BENPR
BGLVJ
C1A
CAHYU
CCPQU
CNMHZ
E3Z
EBS
EJD
GROUPED_DOAJ
H13
HCIFZ
IAO
IEA
IL9
ITC
J8X
K.F
KQ8
L6V
M7S
O9-
OK1
PHGZM
PHGZT
PIMPY
PTHSS
Q2X
RHU
ROL
SAUOL
SCDPB
SCNPE
SFC
TR2
UGNYK
AAYXX
ACHEB
CITATION
7TB
8FD
ABUWG
AZQEC
DWQXO
FR3
H8D
L7M
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
PRINS
PUEGO
ID FETCH-LOGICAL-c421t-bdb6bc8514e472b011165c9de7d5cc35e432890fc7b19a5bcfba2d560c63bcc03
IEDL.DBID BENPR
ISSN 1687-8132
IngestDate Wed Aug 27 01:19:36 EDT 2025
Fri Jul 25 12:10:16 EDT 2025
Tue Jul 01 05:26:39 EDT 2025
Thu Apr 24 23:11:51 EDT 2025
Tue Jun 17 22:29:35 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 9
Keywords damage detection
sensor data
machine learning
Bridge
artificial intelligence
Language English
License This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c421t-bdb6bc8514e472b011165c9de7d5cc35e432890fc7b19a5bcfba2d560c63bcc03
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ORCID 0000-0002-1755-6668
OpenAccessLink https://www.proquest.com/docview/2719652701?pq-origsite=%requestingapplication%
PQID 2719652701
PQPubID 237349
ParticipantIDs doaj_primary_oai_doaj_org_article_182ada9d782941f9ae73856a40f0e0d9
proquest_journals_2719652701
crossref_citationtrail_10_1177_16878132221122770
crossref_primary_10_1177_16878132221122770
sage_journals_10_1177_16878132221122770
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 20220900
2022-09-00
20220901
2022-09-01
PublicationDateYYYYMMDD 2022-09-01
PublicationDate_xml – month: 9
  year: 2022
  text: 20220900
PublicationDecade 2020
PublicationPlace London, England
PublicationPlace_xml – name: London, England
– name: New York
PublicationTitle Advances in mechanical engineering
PublicationYear 2022
Publisher SAGE Publications
Sage Publications Ltd
SAGE Publishing
Publisher_xml – name: SAGE Publications
– name: Sage Publications Ltd
– name: SAGE Publishing
References Cheng, Shang, Shen 2020; 116
Ghiasi, Moghaddam, Ng 2022; 264
Xue, Shen, Yang 2020; 146
Prendergast, Gavin 2014; 6
Zhang, Lu 2021; 23
Kim, Yoon, Sim 2020; 27
Giordano, Turksezer, Previtali
Jang, Kim, An 2019; 18
Mao, Wang, Spencer 2020; 20
Veres, Moussa 2020; 21
Karaaslan, Bagci, Catbas 2021; 125
Ali 2019
Liu, Su, Duan 2022; 2676
Li, Zhu, Huang 2021; 9
Aliyari, Droguett, Ayele 2021; 13
Jiang, Wu, Zhao 2018
Cheng, Liao, Chen 2021; 38
Hu, Li, Cai, Issa 2021
Soleimani-Babakamali, Sepasdar, Nasrollahzadeh 2022; 171
Omar 2018
Lim, Chi 2019; 41
Krizhevsky, Sutskever, Hinton 2012; 25
Deng, Zhang, Feng 2021; 17
Kim, Kim 2020; 12
Sarmadi, Yuen 2022; 173
Yang, Yang 2018; 18
Kumar, Morris, Lopez 2021 1846; 13
Sarmadi, Entezami, Saeedi Razavi 2021; 28
Riggio, Sandak, Franke 2015; 101
Chen, Truong-Hong, Laefer 2018
Lin, Su, Li 2021; 7
Lee, Park, Ryu 2021; 130
Fu, Meng, Li 2021; 9
Rocha, Póvoas, Santos 2019; 38
Li, Liu, Fang 2022; 29
Li, Li 2022; 29
Cha, Choi, Büyüköztürk 2017; 32
Li, Burgueño 2019; 15
Chen, Papandreou, Kokkinos 2018; 40
Hassan Daneshvar, Sarmadi 2022; 256
Lin, Lee, Chang 2021; 21
Hafiz, Bhat 2020; 9
Xia, Yang, Chen 2022; 133
Malekjafarian, Golpayegani, Moloney 2019; 19
Zhang, Yi, Lin 2022; 36
Li, Ren, Jia 2016; 6
Simonyan, Zisserman 2014
Shi, Dang, Cui 2021; 11
Lopez Droguett, Tapia, Yanez 2022; 236
Asadi, Gindy, Alvarez 2019; 23
Dung, Sekiya, Hirano 2019; 102
Zhang, Yuen 2021; 36
Henry, Krainin, Herbst 2012; 31
Janků, Cikrle, Grošek 2019; 225
Baker, Jordan, Pardy 2007; 432
Chen, Ruan, Liu
Li, Sun, Zhang 2021; 5
Moughty, Casas 2017; 7
Jang, Jung, An 2022; 137
Sarmadi, Yuen 2021; 36
Zhang, Han, Robinson 2021; 9
Perry, Guo, Atadero 2020; 164
Haghighat, Ravichandra-Mouli, Chakraborty 2020; 2
Bolukbasi, Mohammadi, Arditi 2004; 9
Xin, Cheng, Diender 2020; 1
Bao, Liu 2017; 24
Chen, Laefer, Mangina 2019; 24
Tang, Chen, Bao 2019; 26
Li, Bu, Sun 2018; 31
Zhang, Wang, Yuen 2022; 37
Garrido, Lagüela, Fang
Alani, Aboutalebi, Kilic 2013; 97
Liao, Chen, Lu 2021; 7
Li, Cheng 2018; 18
Biondi, Addabbo, Ullo 2020; 12
Pozzer, Rezazadeh Azar, Dalla Rosa 2021; 35
Zhou, Zheng 2017; 19
Chaurasia, Culurciello 2017
Ahmadi-Nedushan 2012; 25
An, Chatzi, Sim 2019; 26
Chen 2021; 2021
Zhang, Yuen, Cury, Ribeiro, Ubertini 2022
Rasol, Pais, Pérez-Gracia 2022; 324
Nguyen, Dinh 2019; 13
Fujino, Siringoringo 2020; 16
Ameli, Aremanda, Friess 2022; 6
Ye, Acikgoz, Pendrigh 2018; 173
Yu, He, Liu 2022; 81
Bull, Rogers, Wickramarachchi 2019; 134
Entezami, Shariatmadar, De Michele
Liang 2019; 34
He, Zheng, Liao 2020; 20
Li, Zhao, Zhou 2022; 150
Ren, He, Girshick 2015; 28
Rubio, Kashiwa, Laiteerapong 2019; 112
Wang, Sun, Liu 2019; 38
Guo, Liu, Georgiou 2018; 7
Parisi, Mangini, Fanti 2022; 138
Delgadillo, Casas 2022; 29
Zhang, Yuen, Mousavi 2022; 263
Sarmadi, Karamodin 2020; 140
Badrinarayanan, Kendall, Cipolla 2017; 39
Shakir Ali, Günal 2021; 68
Li, Liu, Zhou
Weinstein, Sanayei, Brenner 2018; 23
Xu, Su, Wang 2019 2867; 9
Zheng, Qian, Shen 2020; 10
Entezami, De Michele, Arslan 2022; 22
Akintunde, Eftekhar Azam, Rageh 2021; 249
Ichi, Dorafshan 2021; 19
Malekjafarian, McGetrick, OBrien 2015; 2015
Lv, Zhang, Xiu 2021; 22
Bao, Tang, Li 2019; 18
Li, Li, Ren 2016; 3
Casas, Moughty 2017; 3
Lecun, Bottou, Bengio 1998; 86
Jin Lim, Hwang, Kim 2021; 20
Arangio, Tsompanakis, Iványi, Topping 2013
Ali, Cha 2019; 226
Gao, Yuan, Tong 2020; 164
Moon, Chung, Chi 2020; 34
Wang, Kim 2019; 39
Kim, Jeon, Baek 2018 1881; 18
Rai, Maity, Yadav 2017; 6
Deng, Mou, Kashiwa 2020; 110
Sun, Shang, Xia 2020; 146
Sarmadi, Entezami, Behkamal
Wang, Chang, Fan 2001; 4
Qurishee, Wu, Atolagbe 2020; 10
bibr9-16878132221122770
bibr21-16878132221122770
Qi CR (bibr78-16878132221122770) 2017
Sarmadi H (bibr141-16878132221122770)
Wang Y (bibr81-16878132221122770) 2019; 38
bibr19-16878132221122770
bibr104-16878132221122770
bibr117-16878132221122770
bibr144-16878132221122770
bibr157-16878132221122770
bibr75-16878132221122770
bibr8-16878132221122770
bibr59-16878132221122770
bibr22-16878132221122770
Ren S (bibr45-16878132221122770) 2015; 28
Li Z (bibr155-16878132221122770) 2019; 15
bibr103-16878132221122770
bibr120-16878132221122770
Li Y (bibr79-16878132221122770) 2018; 31
bibr73-16878132221122770
bibr99-16878132221122770
bibr156-16878132221122770
bibr89-16878132221122770
bibr146-16878132221122770
bibr33-16878132221122770
bibr63-16878132221122770
Song W (bibr51-16878132221122770) 2019
bibr23-16878132221122770
bibr115-16878132221122770
bibr105-16878132221122770
Xu H (bibr43-16878132221122770) 2019; 9
Liu W (bibr46-16878132221122770)
bibr64-16878132221122770
bibr74-16878132221122770
bibr101-16878132221122770
bibr29-16878132221122770
bibr85-16878132221122770
bibr121-16878132221122770
bibr52-16878132221122770
bibr65-16878132221122770
bibr72-16878132221122770
bibr69-16878132221122770
Giordano P (bibr152-16878132221122770)
bibr12-16878132221122770
bibr60-16878132221122770
Rai M (bibr92-16878132221122770) 2017; 6
bibr123-16878132221122770
bibr113-16878132221122770
bibr133-16878132221122770
bibr143-16878132221122770
bibr30-16878132221122770
Deng J (bibr32-16878132221122770)
Chen R. (bibr41-16878132221122770) 2021; 2021
bibr90-16878132221122770
bibr20-16878132221122770
bibr70-16878132221122770
bibr40-16878132221122770
bibr151-16878132221122770
bibr128-16878132221122770
bibr10-16878132221122770
Entezami A (bibr139-16878132221122770)
bibr1-16878132221122770
Ali R. (bibr94-16878132221122770) 2019
bibr158-16878132221122770
bibr37-16878132221122770
bibr87-16878132221122770
bibr27-16878132221122770
bibr97-16878132221122770
bibr108-16878132221122770
bibr17-16878132221122770
bibr47-16878132221122770
bibr77-16878132221122770
bibr118-16878132221122770
bibr67-16878132221122770
bibr138-16878132221122770
bibr57-16878132221122770
bibr148-16878132221122770
bibr95-16878132221122770
bibr3-16878132221122770
bibr82-16878132221122770
Jiang M (bibr80-16878132221122770) 2018
bibr137-16878132221122770
bibr26-16878132221122770
bibr111-16878132221122770
bibr13-16878132221122770
bibr124-16878132221122770
bibr39-16878132221122770
bibr55-16878132221122770
bibr42-16878132221122770
bibr109-16878132221122770
bibr153-16878132221122770
Omar T. (bibr106-16878132221122770) 2018
bibr140-16878132221122770
bibr15-16878132221122770
bibr100-16878132221122770
Kim IH (bibr49-16878132221122770) 2018; 18
bibr25-16878132221122770
Baker GS (bibr110-16878132221122770) 2007; 432
bibr93-16878132221122770
bibr126-16878132221122770
bibr136-16878132221122770
Zhou J (bibr6-16878132221122770) 2017; 19
bibr53-16878132221122770
Nasrollahi M (bibr83-16878132221122770)
bibr4-16878132221122770
Garrido I (bibr102-16878132221122770)
bibr125-16878132221122770
bibr14-16878132221122770
bibr24-16878132221122770
bibr135-16878132221122770
bibr54-16878132221122770
Gal Y (bibr130-16878132221122770) 2016
bibr84-16878132221122770
bibr44-16878132221122770
bibr107-16878132221122770
bibr31-16878132221122770
bibr142-16878132221122770
Chaurasia A (bibr62-16878132221122770) 2017
bibr11-16878132221122770
bibr127-16878132221122770
bibr98-16878132221122770
bibr134-16878132221122770
bibr16-16878132221122770
bibr114-16878132221122770
bibr36-16878132221122770
bibr154-16878132221122770
bibr150-16878132221122770
Ma Z (bibr50-16878132221122770) 2020
bibr5-16878132221122770
Simonyan K (bibr35-16878132221122770) 2014
Krizhevsky A (bibr34-16878132221122770) 2012; 25
bibr28-16878132221122770
Cheng ZG (bibr147-16878132221122770) 2021; 38
bibr2-16878132221122770
bibr149-16878132221122770
bibr38-16878132221122770
bibr76-16878132221122770
bibr86-16878132221122770
bibr18-16878132221122770
bibr96-16878132221122770
bibr58-16878132221122770
bibr66-16878132221122770
bibr129-16878132221122770
bibr48-16878132221122770
bibr56-16878132221122770
bibr68-16878132221122770
Osband I. (bibr131-16878132221122770); 192
Li X (bibr119-16878132221122770)
bibr7-16878132221122770
bibr112-16878132221122770
Chen ZW (bibr145-16878132221122770)
Hu D (bibr116-16878132221122770) 2021
bibr122-16878132221122770
bibr61-16878132221122770
Chen S (bibr88-16878132221122770) 2018
bibr132-16878132221122770
bibr71-16878132221122770
bibr91-16878132221122770
References_xml – volume: 9
  start-page: 114989
  year: 2021
  end-page: 114997
  article-title: A deep learning-based fine crack segmentation network on full-scale steel bridge images with complicated backgrounds
  publication-title: IEEE Access
– volume: 41
  start-page: 100922
  year: 2019
  article-title: Xgboost application on bridge management systems for proactive damage estimation
  publication-title: Adv Eng Inform
– volume: 263
  start-page: 114418
  year: 2022
  article-title: Timber damage identification using dynamic broad network and ultrasonic signals
  publication-title: Eng Struct
– volume: 25
  start-page: 1097
  year: 2012
  end-page: 1105
  article-title: ImageNet classification with deep convolutional neural networks
  publication-title: Adv Neural Inf Process Syst
– volume: 29
  start-page: e2991
  year: 2022
  article-title: Automatic bridge crack detection using boundary refinement based on real-time segmentation network
  publication-title: Struct Control Health Monit
– volume: 39
  start-page: 306
  year: 2019
  end-page: 319
  article-title: Applications of 3D point cloud data in the construction industry: a fifteen-year review from 2004 to 2018
  publication-title: Adv Eng Inform
– volume: 6
  start-page: 138
  year: 2014
  end-page: 149
  article-title: A review of bridge scour monitoring techniques
  publication-title: J Rock Mech Geotechnical Eng
– volume: 164
  start-page: 108048
  year: 2020
  article-title: Streamlined bridge inspection system utilizing unmanned aerial vehicles (UAVs) and machine learning
  publication-title: Measurement
– article-title: Deep learning–based nondestructive evaluation of reinforcement bars using ground-penetrating radar and electromagnetic induction data
  publication-title: Comput Aided Civ Infrastruct Eng
– volume: 225
  start-page: 1098
  year: 2019
  end-page: 1111
  article-title: Comparison of infrared thermography, ground-penetrating radar and ultrasonic pulse echo for detecting delaminations in concrete bridges
  publication-title: Constr Build Mater
– volume: 20
  start-page: 1392
  year: 2020
  end-page: 1408
  article-title: Damage identification based on convolutional neural network and recurrence graph for beam bridge
  publication-title: Struct Health Monit
– volume: 17
  start-page: 233
  year: 2021
  end-page: 248
  article-title: Predicting fatigue damage of highway suspension bridge hangers using weigh-in-motion data and machine learning
  publication-title: Struct Infrastruct Eng
– volume: 38
  start-page: 1
  year: 2019
  end-page: 12
  article-title: Dynamic graph cnn for learning on point clouds
  publication-title: ACM Trans Graph
– volume: 2015
  start-page: 1
  year: 2015
  end-page: 16
  article-title: A review of indirect bridge monitoring using passing vehicles
  publication-title: Shock Vib
– volume: 6
  start-page: 64
  year: 2022
  article-title: Impact of UAV hardware options on bridge inspection mission capabilities
  publication-title: Drones
– volume: 4
  start-page: 75
  year: 2001
  end-page: 91
  article-title: Nondestructive damage detection of bridges: a status review
  publication-title: Adv Struct Eng
– volume: 81
  start-page: 18279
  year: 2022
  end-page: 18304
  article-title: Engineering-oriented bridge multiple-damage detection with damage integrity using modified faster region-based convolutional neural network
  publication-title: Multimed Tools Appl
– volume: 36
  start-page: 04022011
  year: 2022
  article-title: Automatic corrosive environment detection of RC bridge decks from ground-penetrating radar data based on deep learning
  publication-title: J Perform Constr Facil
– volume: 36
  start-page: 1568
  year: 2021
  end-page: 1584
  article-title: Crack detection using fusion features-based broad learning system and image processing
  publication-title: Comput Aided Civ Infrastruct Eng
– volume: 21
  start-page: 4942
  year: 2021
  article-title: The artificial intelligence of things sensing system of real-time bridge scour monitoring for early warning during floods
  publication-title: Sensors
– volume: 432
  start-page: 1
  year: 2007
  article-title: An introduction to ground penetrating radar (GPR)
  publication-title: Geol Soc Am Spec Pap
– volume: 173
  start-page: 109049
  year: 2022
  article-title: Structural health monitoring by a novel probabilistic machine learning method based on extreme value theory and mixture quantile modeling
  publication-title: Mech Syst Signal Process
– volume: 20
  start-page: 3424
  year: 2021
  end-page: 3435
  article-title: Steel bridge corrosion inspection with combined vision and thermographic images
  publication-title: Struct Health Monit
– volume: 5
  start-page: 142
  year: 2021
  article-title: Surface cracking and fractal characteristics of bending fractured polypropylene fiber-reinforced geopolymer mortar
  publication-title: Fractal and Fractional
– volume: 26
  start-page: e2416
  year: 2019
  article-title: Recent progress and future trends on damage identification methods for bridge structures
  publication-title: Struct Control Health Monit
– volume: 38
  start-page: 1
  year: 2019
  end-page: 12
  article-title: Detection of delaminations in sunlight-unexposed concrete elements of bridges using infrared thermography
  publication-title: J Nondestruct Eval
– volume: 9
  start-page: 171
  year: 2020
  end-page: 189
  article-title: A survey on instance segmentation: state of the art
  publication-title: Int J Multimed Inf Retr
– volume: 6
  start-page: 3
  year: 2016
  end-page: 16
  article-title: State-of-the-art in structural health monitoring of large and complex civil infrastructures
  publication-title: J Civ Struct Health Monit
– volume: 2676
  start-page: 460
  year: 2022
  end-page: 479
  article-title: Recommendations for refined preventive maintenance management of concrete bridges in China based on environmental risk zoning
  publication-title: Transp Res Rec: J Trans Res Board
– volume: 138
  start-page: 104249
  year: 2022
  article-title: Automated location of steel truss bridge damage using machine learning and raw strain sensor data
  publication-title: Autom Constr
– volume: 20
  start-page: 1609
  year: 2020
  end-page: 1626
  article-title: Toward data anomaly detection for automated structural health monitoring: exploiting generative adversarial nets and autoencoders
  publication-title: Struct Health Monit
– volume: 36
  start-page: 1150
  year: 2021
  end-page: 1167
  article-title: Early damage detection by an innovative unsupervised learning method based on kernel null space and peak-over-threshold
  publication-title: Comput Aided Civ Infrastruct Eng
– volume: 171
  start-page: 108913
  year: 2022
  article-title: A system reliability approach to real-time unsupervised structural health monitoring without prior information
  publication-title: Mech Syst Signal Process
– volume: 110
  start-page: 102973
  year: 2020
  article-title: Vision based pixel-level bridge structural damage detection using a link ASPP network
  publication-title: Autom Constr
– volume: 26
  start-page: e2296
  year: 2019
  article-title: Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring
  publication-title: Struct Control Health Monit
– year: 2014
  article-title: Very deep convolutional networks for large-scale visual recognition
  publication-title: arXiv preprint arXiv:1409.1556
– volume: 31
  start-page: 1
  year: 2018
  end-page: 11
  article-title: Pointcnn: convolution on X-transformed points
  publication-title: Adv Neural Inf Process Syst
– article-title: Introduction of the combination of thermal fundamentals and deep learning for the automatic thermographic inspection of thermal bridges and water-related problems in infrastructures
  publication-title: Quant InfraRed Thermogr J
– volume: 25
  start-page: 1073
  year: 2012
  end-page: 1081
  article-title: An optimized instance based learning algorithm for estimation of compressive strength of concrete
  publication-title: Eng Appl Artif Intell
– volume: 38
  start-page: 230
  year: 2021
  end-page: 246
  article-title: A vibration recognition method based on deep learning and signal processing
  publication-title: Engineering mechanics
– volume: 22
  start-page: 4281
  year: 2021
  end-page: 4290
  article-title: Solving the security problem of intelligent transportation system with deep learning
  publication-title: IEEE Trans Intell Transp Syst
– volume: 256
  start-page: 114059
  year: 2022
  article-title: Unsupervised learning-based damage assessment of full-scale civil structures under long-term and short-term monitoring
  publication-title: Eng Struct
– volume: 19
  start-page: 4035
  year: 2019
  article-title: A machine learning approach to bridge-damage detection using responses measured on a passing vehicle
  publication-title: Sensors
– volume: 24
  start-page: e1937
  year: 2017
  article-title: Vibration-based bridge scour detection: a review
  publication-title: Struct Control Health Monit
– volume: 97
  start-page: 45
  year: 2013
  end-page: 54
  article-title: Applications of ground penetrating radar (GPR) in bridge deck monitoring and assessment
  publication-title: J Appl Geophy
– volume: 249
  start-page: 113250
  year: 2021
  article-title: Unsupervised machine learning for robust bridge damage detection: full-scale experimental validation
  publication-title: Eng Struct
– volume: 29
  start-page: e2966
  year: 2022
  article-title: Bridge damage detection via improved completed ensemble empirical mode decomposition with adaptive noise and machine learning algorithms
  publication-title: Struct Control Health Monit
– volume: 16
  start-page: 3
  year: 2020
  end-page: 25
  article-title: Recent research and development programs for infrastructures maintenance, renovation and management in Japan
  publication-title: Struct Infrastruct Eng
– volume: 34
  start-page: 04020119
  year: 2020
  article-title: Bridge damage recognition from inspection reports using NER based on recurrent neural network with active learning
  publication-title: J Perform Constr Facil
– volume: 164
  start-page: 108077
  year: 2020
  article-title: Autonomous pavement distress detection using ground penetrating radar and region-based deep learning
  publication-title: Measurement
– volume: 18
  start-page: 1850025
  year: 2018
  article-title: State-of-the-art review on modal identification and damage detection of bridges by moving test vehicles
  publication-title: Int J Struct Stab Dyn
– start-page: 1
  year: 2017
  end-page: 4
  article-title: Linknet: exploiting encoder representations for efficient semantic segmentation
  publication-title: 2017 IEEE visual communications and image processing (VCIP)
– start-page: 74
  year: 2021
  end-page: 82
  article-title: A machine learning-based framework for automatic bridge deck condition assessment using ground penetrating radar
  publication-title: Computing in civil engineering
– volume: 18
  start-page: 1722
  year: 2019
  end-page: 1737
  article-title: Deep learning–based autonomous concrete crack evaluation through hybrid image scanning
  publication-title: Struct Health Monit
– volume: 130
  start-page: 103847
  year: 2021
  article-title: Semantic segmentation of bridge components based on hierarchical point cloud model
  publication-title: Autom Constr
– article-title: Fully automated natural frequency identification based on deep-learning-enhanced computer vision and power spectral density transmissibility
  publication-title: Adv Struct Eng
– volume: 29
  start-page: e2983
  year: 2022
  article-title: A high-frequency feature enhancement network for the surface defect detection of welded rebar
  publication-title: Struct Control Health Monit
– volume: 125
  start-page: 103634
  year: 2021
  article-title: Attention-guided analysis of infrastructure damage with semi-supervised deep learning
  publication-title: Autom Constr
– volume: 27
  start-page: e2591
  year: 2020
  article-title: Automated bridge component recognition from point clouds using deep learning
  publication-title: Struct Control Health Monit
– volume: 146
  start-page: 04020073
  year: 2020
  article-title: Review of bridge structural health monitoring aided by big data and artificial intelligence: from condition assessment to damage detection
  publication-title: J Struct Eng
– volume: 10
  start-page: 957
  year: 2020
  end-page: 972
  article-title: Mitigating effects of temperature variations through probabilistic-based machine learning for vibration-based bridge scour detection
  publication-title: J Civ Struct Health Monit
– volume: 23
  start-page: 100224
  year: 2021
  article-title: Study on artificial intelligence: the state of the art and future prospects
  publication-title: J Ind Inf Integr
– volume: 10
  start-page: 110
  year: 2020
  end-page: 127
  article-title: Bridge girder crack assessment using faster RCNN inception V2 and infrared thermography
  publication-title: J Transport Technol
– volume: 140
  start-page: 106495
  year: 2020
  article-title: A novel anomaly detection method based on adaptive mahalanobis-squared distance and one-class kNN rule for structural health monitoring under environmental effects
  publication-title: Mech Syst Signal Process
– volume: 18
  start-page: 401
  year: 2019
  end-page: 421
  article-title: Computer vision and deep learning–based data anomaly detection method for structural health monitoring
  publication-title: Struct Health Monit
– volume: 264
  start-page: 114474
  year: 2022
  article-title: Damage classification of in-service steel railway bridges using a novel vibration-based convolutional neural network
  publication-title: Eng Struct
– volume: 34
  start-page: 415
  year: 2019
  end-page: 430
  article-title: Image-based post-disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization
  publication-title: Comput Aided Civ Infrastruct Eng
– year: 2018
  article-title: Pointsift: a sift-like network module for 3D point cloud semantic segmentation
  publication-title: arXiv preprint arXiv:1807.00652
– year: 2019
  publication-title: Deep learning-and infrared thermography-based subsurface damage detection in a steel bridge
– volume: 9
  start-page: 16
  year: 2004
  end-page: 25
  article-title: Estimating the future condition of highway bridge components using national bridge inventory data
  publication-title: Pract Period Struct Design Constr
– volume: 21
  start-page: 3152
  year: 2020
  end-page: 3168
  article-title: Deep learning for intelligent transportation systems: a survey of emerging trends
  publication-title: IEEE Trans Intell Transp Syst
– volume: 134
  start-page: 106294
  year: 2019
  article-title: Probabilistic active learning: an online framework for structural health monitoring
  publication-title: Mech Syst Signal Process
– volume: 3
  start-page: 1
  year: 2016
  end-page: 32
  article-title: Structural health monitoring of innovative civil engineering structures in Mainland China
  publication-title: Struct Monit Maint
– volume: 101
  start-page: 1241
  year: 2015
  end-page: 1252
  article-title: Application of imaging techniques for detection of defects, damage and decay in timber structures on-site
  publication-title: Constr Build Mater
– volume: 35
  start-page: 04020131
  year: 2021
  article-title: Semantic segmentation of defects in infrared thermographic images of highly damaged concrete structures
  publication-title: J Perform Constr Facil
– volume: 18
  year: 2018 1881
  article-title: Application of crack identification techniques for an aging concrete bridge inspection using an unmanned aerial vehicle
  publication-title: Sensors
– volume: 23
  start-page: 2618
  year: 2019
  end-page: 2627
  article-title: A machine learning based approach for automatic rebar detection and quantification of deterioration in concrete bridge deck ground penetrating radar B-scan images
  publication-title: KSCE J Civil Eng
– volume: 23
  start-page: 04018084
  year: 2018
  article-title: Bridge damage identification using artificial neural networks
  publication-title: J Bridge Eng
– volume: 7
  start-page: 10
  year: 2021
  article-title: Deep transfer learning and time-frequency characteristics-based identification method for structural seismic response
  publication-title: Front Built Environ
– volume: 19
  start-page: 27
  year: 2017
  end-page: 37
  article-title: Strategic thinking on ensuring bridge safety in China
  publication-title: Strategic Study CAE
– volume: 68
  start-page: 87
  year: 2021
  end-page: 101
  article-title: Artificial neural network for estimation of local scour depth around bridge piers
  publication-title: Arch Hydro-Eng Environ Mech
– start-page: 254
  year: 2018
  publication-title: Condition assessment of concrete bridge decks using ground and airborne infrared thermography
– volume: 226
  start-page: 376
  year: 2019
  end-page: 387
  article-title: Subsurface damage detection of a steel bridge using deep learning and uncooled micro-bolometer
  publication-title: Constr Build Mater
– volume: 7
  start-page: e783
  year: 2021
  article-title: PlaneNet: an efficient local feature extraction network
  publication-title: PeerJ Comput Sci
– volume: 1
  start-page: 1
  year: 2020
  end-page: 16
  article-title: Fracture acoustic emission signals identification of stay cables in bridge engineering application using deep transfer learning and wavelet analysis
  publication-title: Advances in Bridge Engineering
– volume: 137
  start-page: 104229
  year: 2022
  article-title: Automated bridge crack evaluation through deep super resolution network-based hybrid image matching
  publication-title: Autom Constr
– volume: 31
  start-page: 647
  year: 2012
  end-page: 663
  article-title: RGB-D mapping: using Kinect-style depth cameras for dense 3D modeling of indoor environments
  publication-title: Int J Rob Res
– volume: 40
  start-page: 834
  year: 2018
  end-page: 848
  article-title: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
  publication-title: IEEE Trans Pattern Anal Mach Intell
– volume: 13
  year: 2021 1846
  article-title: Identifying spatial and temporal variations in concrete bridges with ground penetrating radar attributes
  publication-title: Remote Sens
– start-page: 735
  year: 2018
  end-page: 740
  article-title: Automated bridge deck evaluation through UAV derived point cloud
  publication-title: CERI-ITRN2018
– volume: 2021
  start-page: 1
  year: 2021
  end-page: 10
  article-title: Migration Learning-based bridge structure damage detection algorithm
  publication-title: Sci Program
– start-page: 227
  year: 2022
  end-page: 245
  article-title: Applications of deep learning in intelligent construction
  publication-title: Structural health monitoring based on data science techniques
– article-title: Damage detection on a historic iron bridge using satellite DInSAR data
  publication-title: Struct Health Monit
– volume: 39
  start-page: 2481
  year: 2017
  end-page: 2495
  article-title: Segnet: a deep convolutional encoder-decoder architecture for image segmentation
  publication-title: IEEE Trans Pattern Anal Mach Intell
– volume: 7
  start-page: 510
  year: 2017
  article-title: A state of the art review of modal-based damage detection in bridges: development, challenges, and solutions
  publication-title: Appl Sci
– volume: 24
  start-page: 05019001
  year: 2019
  article-title: UAV bridge inspection through evaluated 3D reconstructions
  publication-title: J Bridge Eng
– volume: 13
  start-page: 11359
  year: 2021
  article-title: UAV-based bridge inspection via transfer learning
  publication-title: Sustainability
– volume: 3
  start-page: 4
  year: 2017
  article-title: Bridge damage detection based on vibration data: past and new developments
  publication-title: Front Built Environ
– volume: 2
  start-page: 115
  year: 2020
  end-page: 145
  article-title: Applications of deep learning in intelligent transportation systems
  publication-title: J Big Data Anal Transp
– volume: 37
  start-page: 1450
  year: 2022
  end-page: 1467
  article-title: Construction site information decentralized management using blockchain and smart contracts
  publication-title: Comput Aided Civ Infrastruct Eng
– volume: 9
  start-page: 671
  year: 2021
  article-title: Bridge crack semantic segmentation based on improved Deeplabv3+
  publication-title: J Mar Sci Eng
– volume: 102
  start-page: 217
  year: 2019
  end-page: 229
  article-title: A vision-based method for crack detection in gusset plate welded joints of steel bridges using deep convolutional neural networks
  publication-title: Autom Constr
– volume: 18
  start-page: 4206
  year: 2018
  article-title: Comparison of different feature sets for tls point cloud classification
  publication-title: Sensors
– volume: 22
  start-page: 4964
  year: 2022
  article-title: Detection of partially structural collapse using long-term small displacement data from satellite images
  publication-title: Sensors
– volume: 7
  start-page: 87
  year: 2018
  end-page: 93
  article-title: A review of semantic segmentation using deep neural networks
  publication-title: Int J Multimed Inf Retr
– volume: 150
  start-page: 105689
  year: 2022
  article-title: Standardized use inspection of workers’ personal protective equipment based on deep learning
  publication-title: Saf Sci
– volume: 324
  start-page: 126686
  year: 2022
  article-title: GPR monitoring for road transport infrastructure: a systematic review and machine learning insights
  publication-title: Constr Build Mater
– volume: 112
  start-page: 103121
  year: 2019
  article-title: Multi-class structural damage segmentation using fully convolutional networks
  publication-title: Comput Ind
– volume: 28
  start-page: e2663
  year: 2021
  article-title: Ensemble learning-based structural health monitoring by Mahalanobis distance metrics
  publication-title: Struct Control Health Monit
– article-title: Partially online damage detection using long-term modal data under severe environmental effects by unsupervised feature selection and local metric learning
  publication-title: J Civ Struct Health Monit
– volume: 173
  start-page: 530
  year: 2018
  end-page: 545
  article-title: Mapping deformations and inferring movements of masonry arch bridges using point cloud data
  publication-title: Eng Struct
– volume: 12
  start-page: 3757
  year: 2020
  article-title: Deep-Learning-based classification of point clouds for bridge inspection
  publication-title: Remote Sens
– volume: 116
  start-page: 102341
  year: 2020
  article-title: Automatic delamination segmentation for bridge deck based on encoder-decoder deep learning through UAV-based thermography
  publication-title: NDT E Int
– start-page: 37
  year: 2013
  end-page: 60
  article-title: Neural network-based techniques for damage identification of bridges: a review of recent advances
  publication-title: Civil and structural engineering computational methods
– volume: 9
  year: 2019 2867
  article-title: Automatic bridge crack detection using a convolutional neural network
  publication-title: Appl Sci
– volume: 28
  start-page: 1
  year: 2015
  end-page: 9
  article-title: Faster R-CNN: towards real-time object detection with region proposal networks
  publication-title: Adv Neural Inf Process Syst
– volume: 11
  start-page: 518
  year: 2021
  article-title: Improvement of damage segmentation based on pixel-level data balance using VGG-Unet
  publication-title: Appl Sci
– volume: 86
  start-page: 2278
  year: 1998
  end-page: 2324
  article-title: Gradient-based learning applied to document recognition
  publication-title: Proc IEEE
– volume: 133
  start-page: 103992
  year: 2022
  article-title: Automated semantic segmentation of bridge point cloud based on local descriptor and machine learning
  publication-title: Autom Constr
– volume: 32
  start-page: 361
  year: 2017
  end-page: 378
  article-title: Deep learning-based crack damage detection using convolutional neural networks
  publication-title: Comput Aided Civ Infrastruct Eng
– volume: 13
  start-page: 15
  year: 2019
  end-page: 25
  article-title: Prediction of bridge deck condition rating based on artificial neural networks
  publication-title: J Sci Technol Civil Eng (STCE)-HUCE
– volume: 6
  start-page: 290
  year: 2017
  end-page: 303
  article-title: Thermal imaging system and its real time applications: a survey
  publication-title: J Eng Technol
– volume: 19
  year: 2021
  article-title: SDNET2021: annotated NDE dataset for structural defects. Datasets
– volume: 12
  start-page: 3852
  year: 2020
  article-title: Perspectives on the structural health monitoring of bridges by synthetic aperture radar
  publication-title: Remote Sens
– article-title: Non-parametric empirical machine learning for short-term and long-term structural health monitoring
  publication-title: Struct Health Monit
– volume: 9
  start-page: 39009
  year: 2021
  end-page: 39018
  article-title: A GANs-based deep learning framework for automatic subsurface object recognition from ground penetrating radar data
  publication-title: IEEE Access
– volume: 15
  start-page: 174
  year: 2019
  end-page: 182
  article-title: Structural information integration for predicting damages in bridges
  publication-title: J Ind Inf Integr
– volume: 146
  start-page: 04020103
  year: 2020
  article-title: Dynamic network analysis of stakeholder conflicts in megaprojects: sixteen-year case of Hong Kong-Zhuhai-Macao bridge
  publication-title: J Constr Eng Manag
– volume: 236
  start-page: 570
  year: 2022
  end-page: 583
  article-title: Semantic segmentation model for crack images from concrete bridges for mobile devices
  publication-title: Proc IMechE, Part O: J Risk and Reliability
– ident: bibr39-16878132221122770
  doi: 10.1016/j.autcon.2019.02.013
– ident: bibr65-16878132221122770
  doi: 10.5194/nhess-17-1393-2017
– ident: bibr53-16878132221122770
  doi: 10.1109/ICSAI53574.2021.9664156
– ident: bibr54-16878132221122770
  doi: 10.1088/1742-6596/1626/1/012151
– ident: bibr124-16878132221122770
  doi: 10.1002/stc.2966
– ident: bibr128-16878132221122770
  doi: 10.1007/s13349-020-00427-y
– ident: bibr125-16878132221122770
  doi: 10.1080/15732479.2020.1734632
– ident: bibr137-16878132221122770
  doi: 10.1016/j.ymssp.2019.106294
– ident: bibr91-16878132221122770
  doi: 10.1016/j.engstruct.2018.06.094
– ident: bibr109-16878132221122770
– ident: bibr89-16878132221122770
  doi: 10.1061/(ASCE)BE.1943-5592.0001343
– ident: bibr150-16878132221122770
  doi: 10.3390/rs12233852
– ident: bibr1-16878132221122770
  doi: 10.1177/03611981211067978
– ident: bibr8-16878132221122770
  doi: 10.12989/smm.2016.3.1.001
– ident: bibr101-16878132221122770
  doi: 10.1016/j.ndteint.2020.102341
– ident: bibr27-16878132221122770
  doi: 10.3390/app7050510
– ident: bibr11-16878132221122770
  doi: 10.1007/s42421-020-00020-1
– ident: bibr148-16878132221122770
  doi: 10.3389/fbuil.2021.627058
– ident: bibr103-16878132221122770
  doi: 10.1177/1475921718821719
– ident: bibr117-16878132221122770
  doi: 10.1016/j.measurement.2020.108077
– ident: bibr10-16878132221122770
  doi: 10.1109/TITS.2020.2980864
– ident: bibr13-16878132221122770
  doi: 10.1016/j.ssci.2022.105689
– ident: bibr96-16878132221122770
  doi: 10.1109/ISC2.2018.8656971
– ident: bibr19-16878132221122770
  doi: 10.1002/9781119515326.ch4
– ident: bibr28-16878132221122770
  doi: 10.3389/fbuil.2017.00004
– ident: bibr98-16878132221122770
  doi: 10.1061/(ASCE)CF.1943-5509.0001541
– start-page: 1050
  volume-title: International conference on machine learning
  year: 2016
  ident: bibr130-16878132221122770
– ident: bibr143-16878132221122770
  doi: 10.1177/1475921718757405
– ident: bibr152-16878132221122770
  publication-title: Struct Health Monit
– ident: bibr55-16878132221122770
  doi: 10.1007/s13735-017-0141-z
– ident: bibr133-16878132221122770
  doi: 10.1016/j.ymssp.2022.108913
– ident: bibr29-16878132221122770
  doi: 10.1002/stc.2416
– ident: bibr14-16878132221122770
  doi: 10.1111/mice.12804
– ident: bibr100-16878132221122770
  doi: 10.1177/1475921721989407
– ident: bibr21-16878132221122770
  doi: 10.1111/mice.12753
– start-page: 1
  volume-title: 17th world conference on earthquake engineering, Earthquake Engineering Association
  year: 2020
  ident: bibr50-16878132221122770
– start-page: 21
  volume-title: European conference on computer vision
  ident: bibr46-16878132221122770
– start-page: 735
  year: 2018
  ident: bibr88-16878132221122770
  publication-title: CERI-ITRN2018
– ident: bibr40-16878132221122770
  doi: 10.1111/mice.12263
– ident: bibr123-16878132221122770
  doi: 10.1016/j.engstruct.2021.113250
– ident: bibr93-16878132221122770
  doi: 10.1016/j.conbuildmat.2015.06.065
– ident: bibr64-16878132221122770
  doi: 10.3390/app11020518
– year: 2019
  ident: bibr94-16878132221122770
  publication-title: Deep learning-and infrared thermography-based subsurface damage detection in a steel bridge
– ident: bibr31-16878132221122770
  doi: 10.7717/peerj-cs.783
– ident: bibr144-16878132221122770
  doi: 10.1002/stc.2296
– ident: bibr26-16878132221122770
  doi: 10.1142/S0219455418500256
– ident: bibr105-16878132221122770
  doi: 10.1061/9780784482445.007
– ident: bibr58-16878132221122770
  doi: 10.1109/CVPR.2015.7298965
– ident: bibr17-16878132221122770
  doi: 10.1016/j.engstruct.2022.114418
– ident: bibr7-16878132221122770
  doi: 10.1007/s13349-015-0108-9
– ident: bibr33-16878132221122770
  doi: 10.1109/5.726791
– ident: bibr44-16878132221122770
  doi: 10.1109/CVPR.2014.81
– ident: bibr3-16878132221122770
– ident: bibr66-16878132221122770
  doi: 10.1016/j.jrmge.2014.01.007
– year: 2018
  ident: bibr80-16878132221122770
  publication-title: arXiv preprint arXiv:1807.00652
– ident: bibr59-16878132221122770
  doi: 10.1007/978-3-319-24574-4_28
– volume: 432
  start-page: 1
  year: 2007
  ident: bibr110-16878132221122770
  publication-title: Geol Soc Am Spec Pap
– volume: 192
  volume-title: NIPS workshop on bayesian deep learning
  ident: bibr131-16878132221122770
– ident: bibr154-16878132221122770
  doi: 10.1016/j.aei.2019.100922
– ident: bibr42-16878132221122770
  doi: 10.3390/su132011359
– ident: bibr16-16878132221122770
  doi: 10.3390/fractalfract5040142
– volume: 38
  start-page: 1
  year: 2019
  ident: bibr81-16878132221122770
  publication-title: ACM Trans Graph
– ident: bibr90-16878132221122770
  doi: 10.3390/rs12223757
– ident: bibr126-16878132221122770
  doi: 10.3390/s19184035
– volume: 18
  year: 2018
  ident: bibr49-16878132221122770
  publication-title: Sensors
– volume: 6
  start-page: 290
  year: 2017
  ident: bibr92-16878132221122770
  publication-title: J Eng Technol
– ident: bibr5-16878132221122770
  doi: 10.1080/15732479.2019.1650077
– ident: bibr9-16878132221122770
  doi: 10.1109/TITS.2019.2929020
– start-page: 652
  volume-title: Proceedings of the IEEE conference on computer vision and pattern recognition
  year: 2017
  ident: bibr78-16878132221122770
– ident: bibr20-16878132221122770
  doi: 10.1201/b12352-320
– ident: bibr63-16878132221122770
  doi: 10.1016/j.compind.2019.08.002
– ident: bibr114-16878132221122770
  doi: 10.3390/rs13091846
– ident: bibr111-16878132221122770
  doi: 10.1016/j.jappgeo.2013.04.009
– ident: bibr151-16878132221122770
  doi: 10.3390/s22134964
– ident: bibr135-16878132221122770
  doi: 10.1111/mice.12635
– volume: 28
  start-page: 1
  year: 2015
  ident: bibr45-16878132221122770
  publication-title: Adv Neural Inf Process Syst
– ident: bibr113-16878132221122770
  doi: 10.1117/12.2580575
– ident: bibr2-16878132221122770
  doi: 10.1061/(ASCE)CO.1943-7862.0001895
– ident: bibr115-16878132221122770
  doi: 10.1007/s12205-019-2012-z
– year: 2014
  ident: bibr35-16878132221122770
  publication-title: arXiv preprint arXiv:1409.1556
– ident: bibr52-16878132221122770
  doi: 10.1007/s11042-022-12703-8
– ident: bibr56-16878132221122770
  doi: 10.1007/s13735-020-00195-x
– ident: bibr76-16878132221122770
  doi: 10.1177/0278364911434148
– ident: bibr120-16878132221122770
  doi: 10.1109/ACCESS.2021.3064205
– ident: bibr121-16878132221122770
  doi: 10.1016/j.engstruct.2022.114474
– ident: bibr142-16878132221122770
  doi: 10.1177/1475921720916928
– ident: bibr57-16878132221122770
  doi: 10.1109/CVPR52688.2022.01641
– start-page: 248
  volume-title: 2009 IEEE conference on computer vision and pattern recognition
  ident: bibr32-16878132221122770
– ident: bibr129-16878132221122770
  doi: 10.1007/978-3-030-81716-9_2
– ident: bibr75-16878132221122770
  doi: 10.3390/s18124206
– ident: bibr118-16878132221122770
  doi: 10.1061/(ASCE)CF.1943-5509.0001712
– ident: bibr25-16878132221122770
  doi: 10.1155/2015/286139
– ident: bibr140-16878132221122770
  doi: 10.1016/j.engstruct.2022.114059
– volume: 31
  start-page: 1
  year: 2018
  ident: bibr79-16878132221122770
  publication-title: Adv Neural Inf Process Syst
– start-page: 12
  volume-title: Proceedings of the CSCE annual conference
  ident: bibr83-16878132221122770
– ident: bibr71-16878132221122770
  doi: 10.1016/j.autcon.2021.103634
– ident: bibr149-16878132221122770
  doi: 10.1186/s43251-020-00006-7
– ident: bibr18-16878132221122770
  doi: 10.1016/j.jii.2021.100224
– ident: bibr86-16878132221122770
  doi: 10.1016/j.autcon.2021.103992
– ident: bibr95-16878132221122770
  doi: 10.1016/j.conbuildmat.2019.07.293
– ident: bibr48-16878132221122770
  doi: 10.1109/ICCV.2017.324
– ident: bibr37-16878132221122770
  doi: 10.1109/CVPR.2016.90
– start-page: 475
  volume-title: Fuzzy systems and data mining V
  year: 2019
  ident: bibr51-16878132221122770
– ident: bibr112-16878132221122770
  doi: 10.1016/j.conbuildmat.2022.126686
– ident: bibr107-16878132221122770
  doi: 10.1007/s10921-018-0546-5
– ident: bibr22-16878132221122770
  doi: 10.1016/j.engappai.2012.01.012
– ident: bibr47-16878132221122770
  doi: 10.1109/CVPR.2016.91
– ident: bibr77-16878132221122770
  doi: 10.1016/j.aei.2019.02.007
– ident: bibr87-16878132221122770
  doi: 10.1016/j.measurement.2020.108048
– ident: bibr38-16878132221122770
  doi: 10.1109/CVPR.2017.195
– volume: 9
  year: 2019
  ident: bibr43-16878132221122770
  publication-title: Appl Sci
– ident: bibr102-16878132221122770
  publication-title: Quant InfraRed Thermogr J
– volume: 19
  start-page: 27
  year: 2017
  ident: bibr6-16878132221122770
  publication-title: Strategic Study CAE
  doi: 10.15302/J-SSCAE-2017.06.005
– ident: bibr85-16878132221122770
  doi: 10.1016/j.autcon.2021.103847
– ident: bibr146-16878132221122770
  doi: 10.1177/1475921720924601
– ident: bibr4-16878132221122770
  doi: 10.1061/(ASCE)1084-0680(2004)9:1(16)
– ident: bibr141-16878132221122770
  publication-title: J Civ Struct Health Monit
– ident: bibr157-16878132221122770
  doi: 10.1109/Confluence51648.2021.9377100
– start-page: 254
  year: 2018
  ident: bibr106-16878132221122770
  publication-title: Condition assessment of concrete bridge decks using ground and airborne infrared thermography
– ident: bibr145-16878132221122770
  publication-title: Adv Struct Eng
– ident: bibr156-16878132221122770
  doi: 10.2478/heem-2021-0005
– ident: bibr67-16878132221122770
  doi: 10.3390/s21144942
– ident: bibr68-16878132221122770
  doi: 10.1016/j.autcon.2019.102973
– ident: bibr70-16878132221122770
  doi: 10.1109/ACCESS.2021.3105279
– ident: bibr108-16878132221122770
  doi: 10.1016/j.conbuildmat.2019.07.320
– volume: 25
  start-page: 1097
  year: 2012
  ident: bibr34-16878132221122770
  publication-title: Adv Neural Inf Process Syst
– volume: 2021
  start-page: 1
  year: 2021
  ident: bibr41-16878132221122770
  publication-title: Sci Program
– ident: bibr24-16878132221122770
  doi: 10.4203/csets.32.3
– ident: bibr134-16878132221122770
  doi: 10.1002/stc.2663
– ident: bibr36-16878132221122770
  doi: 10.1109/CVPR.2015.7298594
– ident: bibr136-16878132221122770
  doi: 10.1016/j.ymssp.2022.109049
– ident: bibr60-16878132221122770
  doi: 10.1109/TPAMI.2016.2644615
– ident: bibr74-16878132221122770
  doi: 10.1111/mice.12425
– ident: bibr69-16878132221122770
  doi: 10.3390/jmse9060671
– start-page: 1
  year: 2017
  ident: bibr62-16878132221122770
  publication-title: 2017 IEEE visual communications and image processing (VCIP)
– ident: bibr73-16878132221122770
  doi: 10.1002/stc.2991
– volume: 15
  start-page: 174
  year: 2019
  ident: bibr155-16878132221122770
  publication-title: J Ind Inf Integr
– ident: bibr158-16878132221122770
  doi: 10.1061/(ASCE)CF.1943-5509.0001530
– ident: bibr127-16878132221122770
  doi: 10.1002/stc.1937
– ident: bibr153-16878132221122770
  doi: 10.31814/stce.nuce2019-13(3)-02
– ident: bibr139-16878132221122770
  publication-title: Struct Health Monit
– ident: bibr12-16878132221122770
  doi: 10.1007/978-3-030-81716-9_11
– ident: bibr97-16878132221122770
  doi: 10.3390/drones6030064
– start-page: 74
  year: 2021
  ident: bibr116-16878132221122770
  publication-title: Computing in civil engineering
– ident: bibr119-16878132221122770
  publication-title: Comput Aided Civ Infrastruct Eng
– ident: bibr72-16878132221122770
  doi: 10.1177/1748006X20965111
– ident: bibr61-16878132221122770
  doi: 10.1109/TPAMI.2017.2699184
– ident: bibr82-16878132221122770
  doi: 10.1109/ICCV48922.2021.01595
– ident: bibr138-16878132221122770
  doi: 10.1016/j.ymssp.2019.106495
– ident: bibr30-16878132221122770
  doi: 10.1061/(ASCE)ST.1943-541X.0002535
– ident: bibr104-16878132221122770
  doi: 10.1016/j.autcon.2022.104229
– ident: bibr84-16878132221122770
  doi: 10.1002/stc.2591
– ident: bibr99-16878132221122770
  doi: 10.4236/jtts.2020.102007
– volume: 38
  start-page: 230
  year: 2021
  ident: bibr147-16878132221122770
  publication-title: Engineering mechanics
– ident: bibr132-16878132221122770
  doi: 10.1061/(ASCE)BE.1943-5592.0001302
– ident: bibr15-16878132221122770
  doi: 10.1002/stc.2983
– ident: bibr122-16878132221122770
  doi: 10.1016/j.autcon.2022.104249
– ident: bibr23-16878132221122770
  doi: 10.1260/1369433011502372
SSID ssib050600699
ssj0000395696
ssib044728254
ssib023771143
Score 2.5141115
SecondaryResourceType review_article
Snippet Bridges are often located in harsh environments and are thus extremely susceptible to damage. If the initial damage is not detected in time, it can develop...
SourceID doaj
proquest
crossref
sage
SourceType Open Website
Aggregation Database
Enrichment Source
Index Database
Publisher
SubjectTerms Algorithms
Artificial intelligence
Bridge inspection
Damage detection
Data processing
Ground penetrating radar
Infrared imagery
Infrared imaging
Infrared radar
Machine learning
Radar imaging
Structural health monitoring
Thermal imaging
Vibration response
SummonAdditionalLinks – databaseName: DOAJ (Directory of Open Access Journals)
  dbid: DOA
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LS8QwEA6yJz2IT6yu0oMgCMXm0aQ5iYrLIujJhb2VvAqCdkXr_3cm7WpF1IunQjst6UyT-dKZ-YaQY2WZA9zuMuNhNonclpkxzGTBBCnAPWhOsTj59k5OZ-JmXswHrb4wJ6yjB-4Udwb413ijPXgyLWitTUD-FWlEXuch97F0D3zeYDMV12AOuF_LPoyJDEtUlqqMcQVAGExhc-KBI4p8_V9A5iCvK7qayQZZ7zFietGNbZOshGaLrA2YA7fJefdPP13UKb5CRwORPgz4NTP0Tz7tCrJSb54MHkIbU6-aHTKbXN9fTbO-F0LmBKNtZr2V1gE8EkEoZrFDvCyc9kH5wjleBMExZFg7Zak2hXW1NcwDnHGSW-dyvktGzaIJeyQFyGQ0s4Krwgiq4cGmViDPLSx-mpUJyZeKqVxPFI79Kh4r2nODf9NlQk4_bnnuWDJ-E75EbX8IIsF1PAFmr3qzV3-ZPSHjpa2qfta9VkwhPyJTOU3ICdrv89KPo9n_j9EckFWGZREx92xMRu3LWzgEsNLao_hdvgNg-99_
  priority: 102
  providerName: Directory of Open Access Journals
– databaseName: Sage Journals GOLD Open Access 2024
  dbid: AFRWT
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3dSxwxEB_0fKkPUvuBV63sgyAUUvO1yeVJbOkhBfsglfq25GuL0LsTu_fgf28mm72e1EqfFrLZZZhMkl8yM78BONKO-4TbPbEhzSZJ3YRYyy2JNiqZtgcjGCYnX3xT51fy63V9vQGLIRemaPD3RwyrShLlxRpnN95GnxQn4wlTEz3JToIEF7jW9HTZzZr-unuoqoEt6J9eztC17TEg8p4M6W2bsMW1qvkIts6mlz9WU4ALrdka452UGpM7VyaLdHxUFQSfF3uRDhi5ChjKRFCo4jt9Us5Hu18uEvAI2a4Fk-X9bfoSdgowrc56S9qFjTh_BdtrdIWv4bR3JFSLtkKD67knqps1Uk-Cm2Ko-iywKtiZxUfscrzX_A1cTb98_3xOSgEG4iVnHXHBKecTJpMx6cFhWXpVexOiDrX3oo5SoJ-y9doxY2vnW2d5SBjKK-G8p-ItjOaLedyDKuE0a7iTQtdWMpN-bFud-guXVlzDJ2Ogg2IaX9jJsUjGr4YVQvK_dDmGD6tPbntqjuc6f0Jtrzoiq3ZuWNz9bMokbdJZywZrQkJNRrLW2IhcP8pK2tJIgxnDwTBWzWCoDddIysg1ZWM4xvH78-qf0rz775778IJjwkWOajuAUXe3jO8TDOrcYTHdBxYm918
  priority: 102
  providerName: SAGE Publications
Title Review of artificial intelligence-based bridge damage detection
URI https://journals.sagepub.com/doi/full/10.1177/16878132221122770
https://www.proquest.com/docview/2719652701
https://doaj.org/article/182ada9d782941f9ae73856a40f0e0d9
Volume 14
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3dS-QwEB909-XuQTz1uD29pQ8HglBsPto0T6LiKsKJiKJvJV89DnTX0_X_dybNrpXjfCqkaSiTycwvmclvAH4qyx3idpcbj6tJFrbOjeEmDyZUEt2DFowuJ_-6qM5u5PldeZcO3J5TWuXCJkZD7WeOzsj3uSLuO64KdvD4N6eqURRdTSU0VmGIJriuBzA8Orm4vFpoFBdKsR6BnZSK7mouNZDY9YoqAfJouwXuF2JRL1bh6qtxr5ZCocTSRG11jE0gSuGKChz3nFnk_H8HVHu5YdFdTdZhLeHM7LBTjC-wEqYb8LnHPrgJB11cIJu1GelPRyWR_elxdObk43zWXerKvHkw9AjzmL413YKbycn18Vme6inkTnI2z623lXUIsWRAOViqMl-VTvugfOmcKIMUFHZsnbJMm9K61hruERK5SljnCvEVBtPZNHyDDGGX0dxKoUojmcaBTauwv7BoQDWvR1AsBNO4RDZONS_uG5b4xf-R5Qj2lp88dkwbH3U-ImkvOxJJdmyYPf1u0pprcOtkvNEeQZCWrNUmEHVPZWTRFqHwegQ7i7lq0sp9bt70bAS7NH9vr_77N98_HmgbPnG6NBEz03ZgMH96CT8QysztGFbryekYhoeTq9vrcdLecTwYeAXovOiz
linkProvider ProQuest
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LT9wwEB7R7aHlgKAPsTxKDq0qVYoaPxKvDwjRx3YpjxNI3FK_gpBgF9hFiD_Fb2TGSSBVVW6cojiOFY3Hnm8ynm8APirLHeJ2lxqPq0lmdpAaw00aTCgkmgctGCUn7x8UoyP5-zg_noO7NheGjlW2e2LcqP3E0T_yr1wR9x1XGdu6uEypahRFV9sSGrVa7IbbG3TZpps7P3B-P3E-_Hn4fZQ2VQVSJzmbpdbbwjoEGjJIxS3VWi9yp31QPndO5EEKCr5VTlmmTW5dZQ33CAxcIaxzmcBxX8BLKdCSU2b68Ferv1woxTp0eRLHJwesvScuv6xo4H-0FAK9k1hCjBW41gfoGTaBV-KEorZBjIQgJuKKyil3TGesMPAXLO6cRIvGcbgICw2qTbZrNVyCuTB-A_MdrsO3sFVHIZJJlZC21sQVyWmHETQli-qTOoUs8ebc0CXM4mGx8Ts4ehY5v4feeDIOy5AgyDOaWylUbiTTOLCpFPYXFrdrzQd9yFrBlK6hNqcKG2cla9jM_5FlH748vHJR83o81fkbSfuhI1Fyx4bJ1UnZrPASHTXjjfYIubRklTaBiIIKI7MqC5nXfVhr56ps9olp-ajVffhM8_f46L9fs_L0QBvwanS4v1fu7RzsrsJrTuka8UzcGvRmV9dhHUHUzH6ImpvAn-deKvc_piHO
linkToPdf http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1ba9VAEB7qKYg-iFd6bK15UAQhNHtJ9uyDFHs59KKHIhb6FveWUtBzejki_jV_nTObTZtS2rc-hSSbTdid2fkmM_sNwDtluUPc7nLjUZtkYUe5MdzkwYRKonnQgtHm5K-TaudQ7h2VRwvwr9sLQ2mV3ZoYF2o_c_SPfI0r4r7jqmBrTUqLONgar5-e5VRBiiKtXTmNVkT2w98_6L5dfNrdwrl-z_l4-_vmTp4qDOROcjbPrbeVdQg6ZJCKW6q7XpVO-6B86ZwogxQUiGucskyb0rrGGu4RJLhKWOcKgf0-gEVFXtEAFje2JwffOmnmQinWI8-T-AZyx7pzYvYrquQMRLsh0FeJBcVYhZo_Qj8xhWGJIYqujWJcBBESV1RcuWdIY72BayC5l5cWTeX4KTxJGDf73ArlM1gI0-fwuMd8-ALW25hENmsykt2WxiI76fGD5mRffdZuKMu8-WXoEOYxdWz6Eg7vZaRfwWA6m4YlyBDyGc2tFKo0kmns2DQK2wuLi7fmoyEU3cDULhGdU72NnzVL3OY3xnIIHy8fOW1ZPu5qvEGjfdmQCLrjhdn5cZ30vUa3zXijPQIwLVmjTSDaoMrIoilC4fUQVrq5qtOqcVFfyfgQPtD8Xd269Wte393RW3iIalJ_2Z3sL8MjTns3YoLcCgzm57_DG0RUc7uaRDeDH_etLf8BSD0nYA
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Review+of+artificial+intelligence-based+bridge+damage+detection&rft.jtitle=Advances+in+mechanical+engineering&rft.au=Zhang%2C+Yang&rft.au=Ka-Veng%2C+Yuen&rft.date=2022-09-01&rft.pub=Sage+Publications+Ltd&rft.issn=1687-8132&rft.eissn=1687-8140&rft.volume=14&rft.issue=9&rft_id=info:doi/10.1177%2F16878132221122770&rft.externalDBID=HAS_PDF_LINK
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1687-8132&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1687-8132&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1687-8132&client=summon