A novel experience-based learning algorithm for structural damage identification: simulation and experimental verification

A simple yet powerful optimization algorithm, named the experience-based learning (EBL) algorithm, is proposed in this article for structural damage identification based on vibration data. This algorithm is free from any algorithm-specific control parameters and requires only common control paramete...

Full description

Saved in:
Bibliographic Details
Published inEngineering optimization Vol. 52; no. 10; pp. 1658 - 1681
Main Authors Zheng, Tongyi, Luo, Weili, Hou, Rongrong, Lu, Zhongrong, Cui, Jie
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 02.10.2020
Taylor & Francis Ltd
Subjects
Cantilever beams
Computer simulation
Damage detection
Damage identification
experience-based learning algorithm
Functionally gradient materials
Machine learning
mode shape
natural frequency
Optimization algorithms
Parameters
Particle swarm optimization
Resonant frequencies
Robustness (mathematics)
Search algorithms
Structural damage
structural health monitoring
Online AccessGet full text
ISSN0305-215X
1026-745X
1029-0273
1029-0273
DOI10.1080/0305215X.2019.1668935

Cover

Abstract A simple yet powerful optimization algorithm, named the experience-based learning (EBL) algorithm, is proposed in this article for structural damage identification based on vibration data. This algorithm is free from any algorithm-specific control parameters and requires only common control parameters. The natural frequencies and/or mode shapes are utilized in establishing an objective function. The efficiency and robustness of the proposed method are demonstrated by two numerical examples, namely a television tower and a functionally graded material beam. A set of experimental work on a cantilever beam is studied for further verification. Both numerical and experimental results confirm the superiority of the proposed EBL algorithm in terms of convergence and accuracy for structural damage identification, in comparison with particle swarm optimization, the cloud model-based fruit fly optimization algorithm, squirrel search algorithm and teaching-learning-based optimization.
AbstractList A simple yet powerful optimization algorithm, named the experience-based learning (EBL) algorithm, is proposed in this article for structural damage identification based on vibration data. This algorithm is free from any algorithm-specific control parameters and requires only common control parameters. The natural frequencies and/or mode shapes are utilized in establishing an objective function. The efficiency and robustness of the proposed method are demonstrated by two numerical examples, namely a television tower and a functionally graded material beam. A set of experimental work on a cantilever beam is studied for further verification. Both numerical and experimental results confirm the superiority of the proposed EBL algorithm in terms of convergence and accuracy for structural damage identification, in comparison with particle swarm optimization, the cloud model-based fruit fly optimization algorithm, squirrel search algorithm and teaching–learning-based optimization.
Author Luo, Weili
Hou, Rongrong
Cui, Jie
Lu, Zhongrong
Zheng, Tongyi
Author_xml – sequence: 1
  givenname: Tongyi
  surname: Zheng
  fullname: Zheng, Tongyi
  organization: School of Civil Engineering, Guangzhou University
– sequence: 2
  givenname: Weili
  surname: Luo
  fullname: Luo, Weili
  email: wlluo@gzhu.edu.cn
  organization: School of Civil Engineering, Guangzhou University
– sequence: 3
  givenname: Rongrong
  surname: Hou
  fullname: Hou, Rongrong
  organization: Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University
– sequence: 4
  givenname: Zhongrong
  surname: Lu
  fullname: Lu, Zhongrong
  organization: Department of Applied Mechanics, Sun Yat-sen University
– sequence: 5
  givenname: Jie
  surname: Cui
  fullname: Cui, Jie
  organization: School of Civil Engineering, Guangzhou University
BookMark eNqFkc9LHDEYhkOx0NX2TygEPM_2S7KZH-1FEauC4KUFb-GbTGYbySRrklG3f31n3PUiWE_5Ds_z8vLmkBz44A0hXxksGdTwDQRIzuTtkgNrlqws60bID2TBgDcF8EockMXMFDP0iRymdAfABEC9IH9PqQ8PxlHztDHRGq9N0WIyHXUGo7d-TdGtQ7T5z0D7EGnKcdR5jOhohwOuDbWd8dn2VmO2wX-nyQ6je74p-m4fPEzMpDxM5wv5mXzs0SXzZf8ekd8_z3-dXRbXNxdXZ6fXhRa1zFMbDYA9ctFK1mNTYcuZbjumOWjT8krKeiWbciXKFatLBiD7tjJoNJMMSi2OSLnLHf0Gt4_onNpMhTBuFQM1L6j2Cz6peUG1X3ASj3fiJob70aSs7sIY_dRV8ZWoqpLX1UzJHaVjSCma_q3029fpP1552ubnYXJE6961T3a29dOvDPgYoutUxq0LsY_otU1K_D_iH2uiq8w
CitedBy_id crossref_primary_10_1002_pc_28470
crossref_primary_10_1002_tal_1967
crossref_primary_10_1007_s42107_020_00282_8
crossref_primary_10_1080_0305215X_2021_1919887
crossref_primary_10_1142_S0219455421501005
crossref_primary_10_1177_14759217211018114
crossref_primary_10_1007_s00158_022_03421_8
crossref_primary_10_1080_0305215X_2022_2086235
crossref_primary_10_1155_2024_2054173
crossref_primary_10_1080_15376494_2023_2164911
Cites_doi 10.1080/17415977.2018.1454445
10.1109/ACCESS.2018.2885823
10.1016/j.mspro.2014.07.442
10.1177/1475921704042680
10.1109/MHS.1995.494215
10.1163/156855101753396663
10.1155/2019/6291968
10.1016/j.compstruct.2017.12.058
10.1016/j.crme.2018.09.003
10.1016/j.tafmec.2019.102240
10.1016/j.compositesb.2016.09.093
10.1061/(ASCE)0887-3801(2002)16:3(222)
10.1080/0305215X.2016.1190350
10.1016/j.swevo.2017.04.008
10.1007/s00158-016-1637-5
10.1080/0305215X.2017.1318872
10.1016/j.apm.2016.09.008
10.2991/ismems-16.2016.35
10.1177/1475921709341011
10.1016/j.engfracmech.2018.09.032
10.1016/j.jsv.2019.02.017
10.1080/0305215X.2017.1367392
10.1016/j.engstruct.2011.07.028
10.1016/j.swevo.2015.10.010
10.1016/j.engstruct.2018.09.070
10.1016/j.jsv.2018.02.064
10.1155/2019/1589303
10.1016/j.asoc.2017.06.033
10.1016/j.cad.2010.12.015
10.1016/j.ins.2011.08.006
10.1016/S0141-0296(02)00118-9
ContentType Journal Article
Copyright 2019 Informa UK Limited, trading as Taylor & Francis Group 2019
2019 Informa UK Limited, trading as Taylor & Francis Group
Copyright_xml – notice: 2019 Informa UK Limited, trading as Taylor & Francis Group 2019
– notice: 2019 Informa UK Limited, trading as Taylor & Francis Group
DBID AAYXX
CITATION
7SC
7TB
8FD
FR3
JQ2
KR7
L7M
L~C
L~D
ADTOC
UNPAY
DOI 10.1080/0305215X.2019.1668935
DatabaseName CrossRef
Computer and Information Systems Abstracts
Mechanical & Transportation Engineering Abstracts
Technology Research Database
Engineering Research Database
ProQuest Computer Science Collection
Civil Engineering Abstracts
Advanced Technologies Database with Aerospace
Computer and Information Systems Abstracts – Academic
Computer and Information Systems Abstracts Professional
Unpaywall for CDI: Periodical Content
Unpaywall
DatabaseTitle CrossRef
Civil Engineering Abstracts
Technology Research Database
Computer and Information Systems Abstracts – Academic
Mechanical & Transportation Engineering Abstracts
ProQuest Computer Science Collection
Computer and Information Systems Abstracts
Engineering Research Database
Advanced Technologies Database with Aerospace
Computer and Information Systems Abstracts Professional
DatabaseTitleList Civil Engineering Abstracts
Database_xml – sequence: 1
  dbid: UNPAY
  name: Unpaywall
  url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/
  sourceTypes: Open Access Repository
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISSN 1029-0273
EndPage 1681
ExternalDocumentID oai:figshare.com:article/9929795
10_1080_0305215X_2019_1668935
1668935
Genre Research Article
GroupedDBID -~X
.7F
.QJ
0BK
0R~
29G
2DF
30N
4.4
5GY
5VS
AAENE
AAGDL
AAHIA
AAJMT
AALDU
AAMIU
AAPUL
AAQRR
ABCCY
ABFIM
ABHAV
ABJNI
ABLIJ
ABPAQ
ABPEM
ABTAI
ABXUL
ABXYU
ACGEJ
ACGFS
ACGOD
ACIWK
ACTIO
ADCVX
ADGTB
ADXPE
AEISY
AENEX
AEOZL
AEPSL
AEYOC
AFKVX
AFRVT
AGDLA
AGMYJ
AHDZW
AIJEM
AIYEW
AJWEG
AKBVH
AKOOK
ALMA_UNASSIGNED_HOLDINGS
ALQZU
AQRUH
AQTUD
AVBZW
AWYRJ
BLEHA
CCCUG
CE4
CS3
DGEBU
DKSSO
EBS
E~A
E~B
GTTXZ
H13
HF~
HZ~
H~P
IPNFZ
J.P
KYCEM
LJTGL
M4Z
NA5
NX~
O9-
P2P
PQQKQ
RIG
RNANH
ROSJB
RTWRZ
S-T
SNACF
TASJS
TBQAZ
TDBHL
TEN
TFL
TFT
TFW
TN5
TNC
TTHFI
TUROJ
TWF
UT5
UU3
ZGOLN
~S~
AAYXX
CITATION
7SC
7TB
8FD
FR3
JQ2
KR7
L7M
L~C
L~D
07I
1TA
4B5
AAYLN
ACTTO
ADTOC
ADUMR
ADXEU
AEHZU
AEZBV
AFBWG
AFFNX
AFION
AGBKS
AGBLW
AGVKY
AGWUF
AGYFW
AKHJE
AKMBP
ALRRR
ALXIB
ARCSS
BGSSV
BWMZZ
C0-
C5H
CAG
COF
CYRSC
DAOYK
DEXXA
EJD
FETWF
IFELN
L8C
NUSFT
OPCYK
TAJZE
TAP
UB6
UNPAY
ID FETCH-LOGICAL-c385t-bac00afa23b51fa97ab21cbd1c20ceb27558459643641861005fb7eaec15106c3
IEDL.DBID UNPAY
ISSN 0305-215X
1026-745X
1029-0273
IngestDate Sun Oct 26 04:16:56 EDT 2025
Wed Aug 13 08:39:37 EDT 2025
Thu Apr 24 23:08:15 EDT 2025
Wed Oct 01 04:49:54 EDT 2025
Mon Oct 20 23:48:22 EDT 2025
IsDoiOpenAccess false
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 10
Language English
License cc-by
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c385t-bac00afa23b51fa97ab21cbd1c20ceb27558459643641861005fb7eaec15106c3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
OpenAccessLink https://proxy.k.utb.cz/login?url=https://figshare.com/articles/A_novel_experience-based_learning_algorithm_for_structural_damage_identification_simulation_and_experimental_verification/9929795
PQID 2437762875
PQPubID 53195
PageCount 24
ParticipantIDs crossref_primary_10_1080_0305215X_2019_1668935
proquest_journals_2437762875
informaworld_taylorfrancis_310_1080_0305215X_2019_1668935
unpaywall_primary_10_1080_0305215x_2019_1668935
crossref_citationtrail_10_1080_0305215X_2019_1668935
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 2020-10-02
PublicationDateYYYYMMDD 2020-10-02
PublicationDate_xml – month: 10
  year: 2020
  text: 2020-10-02
  day: 02
PublicationDecade 2020
PublicationPlace Abingdon
PublicationPlace_xml – name: Abingdon
PublicationTitle Engineering optimization
PublicationYear 2020
Publisher Taylor & Francis
Taylor & Francis Ltd
Publisher_xml – name: Taylor & Francis
– name: Taylor & Francis Ltd
References CIT0030
CIT0010
CIT0032
CIT0031
CIT0012
CIT0034
CIT0011
Rao R. V. (CIT0022) 2013; 20
Fallahian S. (CIT0007) 2017; 25
CIT0014
CIT0013
CIT0035
CIT0016
CIT0015
CIT0018
CIT0017
CIT0019
CIT0020
CIT0001
CIT0023
Zheng T. (CIT0033) 2018; 67
Rao R. V. (CIT0021) 2016; 7
CIT0003
CIT0025
CIT0002
CIT0024
CIT0005
CIT0027
CIT0004
CIT0026
CIT0029
CIT0006
CIT0028
CIT0009
CIT0008
References_xml – volume: 20
  start-page: 710
  issue: 3
  year: 2013
  ident: CIT0022
  publication-title: Scientia Iranica
– ident: CIT0004
  doi: 10.1080/17415977.2018.1454445
– ident: CIT0024
  doi: 10.1109/ACCESS.2018.2885823
– ident: CIT0030
  doi: 10.1016/j.mspro.2014.07.442
– ident: CIT0015
  doi: 10.1177/1475921704042680
– ident: CIT0006
  doi: 10.1109/MHS.1995.494215
– ident: CIT0001
  doi: 10.1163/156855101753396663
– ident: CIT0035
  doi: 10.1155/2019/6291968
– volume: 7
  start-page: 19
  issue: 1
  year: 2016
  ident: CIT0021
  publication-title: International Journal of Industrial Engineering Computations
– ident: CIT0027
  doi: 10.1016/j.compstruct.2017.12.058
– ident: CIT0032
  doi: 10.1016/j.crme.2018.09.003
– ident: CIT0012
  doi: 10.1016/j.tafmec.2019.102240
– volume: 67
  start-page: 245
  issue: 3
  year: 2018
  ident: CIT0033
  publication-title: Structural Engineering and Mechanics
– ident: CIT0028
  doi: 10.1016/j.compositesb.2016.09.093
– volume: 25
  start-page: 3088
  issue: 6
  year: 2017
  ident: CIT0007
  publication-title: Scientia Iranica
– ident: CIT0008
  doi: 10.1061/(ASCE)0887-3801(2002)16:3(222)
– ident: CIT0019
  doi: 10.1080/0305215X.2016.1190350
– ident: CIT0023
  doi: 10.1016/j.swevo.2017.04.008
– ident: CIT0010
  doi: 10.1007/s00158-016-1637-5
– ident: CIT0011
  doi: 10.1080/0305215X.2017.1318872
– ident: CIT0017
  doi: 10.1016/j.apm.2016.09.008
– ident: CIT0020
  doi: 10.2991/ismems-16.2016.35
– ident: CIT0031
  doi: 10.1177/1475921709341011
– ident: CIT0013
  doi: 10.1016/j.engfracmech.2018.09.032
– ident: CIT0014
  doi: 10.1016/j.jsv.2019.02.017
– ident: CIT0005
  doi: 10.1080/0305215X.2017.1367392
– ident: CIT0002
  doi: 10.1016/j.engstruct.2011.07.028
– ident: CIT0003
  doi: 10.1016/j.swevo.2015.10.010
– ident: CIT0029
  doi: 10.1016/j.engstruct.2018.09.070
– ident: CIT0009
  doi: 10.1016/j.jsv.2018.02.064
– ident: CIT0034
  doi: 10.1155/2019/1589303
– ident: CIT0018
  doi: 10.1016/j.asoc.2017.06.033
– ident: CIT0025
  doi: 10.1016/j.cad.2010.12.015
– ident: CIT0026
  doi: 10.1016/j.ins.2011.08.006
– ident: CIT0016
  doi: 10.1016/S0141-0296(02)00118-9
SSID ssj0013008
Score 2.3399668
Snippet A simple yet powerful optimization algorithm, named the experience-based learning (EBL) algorithm, is proposed in this article for structural damage...
SourceID unpaywall
proquest
crossref
informaworld
SourceType Open Access Repository
Aggregation Database
Enrichment Source
Index Database
Publisher
StartPage 1658
SubjectTerms Cantilever beams
Computer simulation
Damage detection
Damage identification
experience-based learning algorithm
Functionally gradient materials
Machine learning
mode shape
natural frequency
Optimization algorithms
Parameters
Particle swarm optimization
Resonant frequencies
Robustness (mathematics)
Search algorithms
Structural damage
structural health monitoring
Title A novel experience-based learning algorithm for structural damage identification: simulation and experimental verification
URI https://www.tandfonline.com/doi/abs/10.1080/0305215X.2019.1668935
https://www.proquest.com/docview/2437762875
https://figshare.com/articles/A_novel_experience-based_learning_algorithm_for_structural_damage_identification_simulation_and_experimental_verification/9929795
UnpaywallVersion submittedVersion
Volume 52
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVLSH
  databaseName: aylor and Francis Online
  customDbUrl:
  mediaType: online
  eissn: 1029-0273
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0013008
  issn: 1029-0273
  databaseCode: AHDZW
  dateStart: 19970501
  isFulltext: true
  providerName: Library Specific Holdings
– providerCode: PRVAWR
  databaseName: Taylor & Francis Science and Technology Library-DRAA
  customDbUrl:
  eissn: 1029-0273
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0013008
  issn: 1029-0273
  databaseCode: 30N
  dateStart: 19970101
  isFulltext: true
  titleUrlDefault: http://www.tandfonline.com/page/title-lists
  providerName: Taylor & Francis
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwzV3LbtNAFL0qyQJY8EYESjULtk78mrHNzgKqCImKBRGBzdXMeJxGdZyKpA_6M_wKn8Ydv-pUQhU7dl547GPr2PfcmTvnArzhOo4SCrxO4mbSCYXJnVgJ4xA3IhnnPE4qJ6ZPR2I6Cz_O-XwPfrV7YfLlYnMsf9QTSW1t2CTFcn1uCjSd_a9jf_IZNq0VFiiLxZqy6eMVktbD2nvV-lZgJlf0YeIya4pvqufFzXLVdMdCStix76ZvKdSdOElIPkQJvwNDwUnsD2A4O_qcfqvXJrhDAXNeraf6wonC9jixK6NBu1_IOnkHdo8sv7SlZMnYE4JUAt-JhDs-qTtq9-5ZeSp_Xsii6AW-w4fwu31ldb3Lyfhsq8b66oab5H_8Th_Bg0Z0s7QG9Rj2TPkE7vesGJ_CVcoqoOwmUNYCZR1QRkDZNVBWA2W7QN-ya6SMkLI-UtZH-gxmhx--vJs6TV8KRwcx39LdtevKXPqB4l4uk0gq39Mq87TvaqP8iJOq49boTIReTPrU5bmKjDSa5JUrdPAcBuW6NC-AacqHfSVc4-VumBlfBZSQ53bLDtckZfkIwpYjqBvTdts7pECv9XatqTVHSy1sqDWCcTfstHYtuW1A0icgbqvporzu7YLBLWP3W7Zi8wPcoPW5pDhL2fAIJh2D_wbmcueCL_95xCu459t5EFvY4e_DgBhgXpNY3KoDGKbT99-_HjQf7h-pdnOx
linkProvider Unpaywall
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT9wwEB619EB76LvqFlp86DWL83Ae3FBVtLz2BNLeLNux6arZLCqhUH59ZxJnm62EOHDPOLbz2TPjjL8P4KsweVag4w0KXqogSa0Lcp3aALGRqdyJvGiZmE6n6eQ8OZqJ2eAuDJVVUg7tOqKIdq-mxU2H0X1J3C6BFF3VjCqzinGYpuh0xVN4JjDYJxWDmE___UngrSodmQRk09_iua-ZNf-0xl66FoNuXteX6s-NqqqBOzp4BaYfSFeF8nN83eixufuP4_FxI30NL320yvY7eL2BJ7Z-Cy8GHIbv4G6f1cvftmJ2RZockGssmRekuGCqulj-mjc_FgyHyTrGWmL7YKVa4HbG5qUvWWpRsseu5gsvKsaw52woQsBw5a2efA_nB9_Pvk0CL-gQmDgXDb7dcK6cimItQqeKTOkoNLoMTcQNpviZwHBIEENYmoQ5BnZcOJ1ZZQ3GJTw18QfYqJe1_QjMYCIZ6ZTb0PGktJGOMZN1dNdFGIwBxQiS_jNK49nOSXSjkmFPiuqnVtLUSj-1IxivzC47uo-HDIohRmTTnrO4ThRFxg_YbveAkn7nuJJEEIkOCtPIEeyuQHZfZ27XGvz0iM7swObk7PREnhxOj7fgeUTHClQnEW3DBuLCfsbYq9Ff2sX1F3YkH5I
linkToPdf http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB5BkXgceKMuFPCBa7bOw07CrQJW5bXiQKXeLNux2xXZ7Kqb8uivZyZxll2kqofeM4ntfPbM2OPvA3gjbJGX6Hijklc6yqTzUWGkixAbuS68KMqOienrVB4eZZ-OxVBNuApllZRD-54ooluraXIvKz9UxO0TRtFTHVNhVjmOpUSfK27CLUmnYnSLg0__HSTwTpSOTCKyGS7xXPaaLfe0RV66FYLeOW-W-s8vXdcb3mjyAMzQj74I5cf4vDVje_EfxeO1OvoQ7odYlR304HoEN1zzGO5tMBg-gYsD1ix-upq5NWVyRI6xYkGO4oTp-mRxNmtP5wx7yXq-WuL6YJWe42LGZlUoWOow8patZvMgKcaw4WxTgoDhvFs_-RSOJh--vzuMgpxDZNNCtPh1y7n2OkmNiL0uc22S2Joqtgm3mODnAoMhQfxgMosLDOu48CZ32lmMSri06TPYaRaN2wVmMY1MjOQu9jyrXGJSzGM93XQRFiNAMYJs-IvKBq5zktyoVTxQooahVTS0KgztCMZrs2VP9nGVQbkJEdV2uyy-l0RR6RW2ewOeVFg3VoroIdE9YRI5gv01xi5rzO-tFz6_RmNew-1v7yfqy8fp5xdwN6E9BSqSSPZgB2HhXmLg1ZpX3dT6C8fbHjY
linkToUnpaywall http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwzV3LbtQwFLXKdAEseFcMFOQF28zkZSdmN0JUFRIVC0YaVle2Y09HZDIVM6WPn-FX-DSu4yRNKqGKHbss4uQkOsk9174-l5B3TOeZwMAbiLCQQcqNDXLFTYDcyGRuWS5qJ6bPJ_x4nn5asMUe-dXuhbGr5fZU_vATSW1t2HQG1eanKcF09r-B-8kX0LRWWIIslxvMpk_XgFoPvPeq862AQq7xw4RV0RTf1M8L29W66Y4FmLBD303fUag7cSpQPmSC3SP7nKHYH5H9-cmX2Te_NsECDJiLej015kGWtsfCrYwm7X4h5-SduD2y7NKVkolJxDmqBDaIhAOf1IHavX9encmrC1mWvcB39Jj8bl-Zr3f5PjnfqYm-vuUm-R-_0yfkUSO66cyDekr2TPWMPOxZMT4n1zNaA6W3gdIWKO2AUgRKb4BSD5QOgb6nN0gpIqV9pLSP9AWZH338-uE4aPpSBDrJ2Q7vrsNQWhknikVWikyqONKqiHQcaqPijKGqY87ojKdRjvo0ZFZlRhqN8irkOjkgo2pTmZeEasyHY8VDE9kwLUysEkzIrduywzRKWTYmacsR0I1pu-sdUkLUert6ai3AUQsaao3JpBt25l1L7hog-gSEXT1dZH1vF0juGHvYshWaH-AWnM8lxlnMhsdk2jH4b2AuBxd89c8jXpMHsZsHcYUd8SEZIQPMGxSLO_W2-WD_ABP3chA
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=A+novel+experience-based+learning+algorithm+for+structural+damage+identification%3A+simulation+and+experimental+verification&rft.jtitle=Engineering+optimization&rft.au=Zheng%2C+Tongyi&rft.au=Luo%2C+Weili&rft.au=Hou%2C+Rongrong&rft.au=Lu%2C+Zhongrong&rft.date=2020-10-02&rft.pub=Taylor+%26+Francis+Ltd&rft.issn=0305-215X&rft.eissn=1029-0273&rft.volume=52&rft.issue=10&rft.spage=1658&rft.epage=1681&rft_id=info:doi/10.1080%2F0305215X.2019.1668935&rft.externalDBID=NO_FULL_TEXT
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0305-215X&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0305-215X&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0305-215X&client=summon