In-Situ Loading Test in Deep Soft Soil and Back Analysis Based on Machine Learning Method
To investigate the effects of overlying surcharge on ground deformation in soft soil areas, in-situ loading tests are conducted to monitor soil behavior. Monitoring data, including surface settlement, lateral displacement, stratified settlement, and pore water pressure, are collected from 5th Januar...
Uložené v:
| Vydané v: | International journal of civil engineering (Tehran. Online) Ročník 23; číslo 8; s. 1701 - 1716 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Cham
Springer International Publishing
01.08.2025
Springer Nature B.V |
| Predmet: | |
| ISSN: | 1735-0522, 2383-3874 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | To investigate the effects of overlying surcharge on ground deformation in soft soil areas, in-situ loading tests are conducted to monitor soil behavior. Monitoring data, including surface settlement, lateral displacement, stratified settlement, and pore water pressure, are collected from 5th January to 22nd February. The three-dimensional finite element model is established to analyze the loading test, and a Genetic Algorithm-Backpropagation Neural Network (GA-BPNN) model is employed for the back-analysis of soil parameters. The analysis of the monitoring data reveals that the cumulative settlement initially exhibits an uplift followed by settlement, while lateral displacement progressively decreases, with the influence range of the load extending to approximately 108 m. Sensitivity analysis identifies the internal friction angle
φ
and
E
ref oed of the 2-1A mud layer as the most significant parameters influencing horizontal displacement. The back analysis shows good agreement with the monitoring data, indicating that the model’s predictions align well with the observed results. The in-situ loading tests and back analysis results offer valuable insights into the deformation behavior of soils under surcharge loading and provide a robust framework for model calibration, ultimately contributing to the safety and stability of similar infrastructure projects. |
|---|---|
| AbstractList | To investigate the effects of overlying surcharge on ground deformation in soft soil areas, in-situ loading tests are conducted to monitor soil behavior. Monitoring data, including surface settlement, lateral displacement, stratified settlement, and pore water pressure, are collected from 5th January to 22nd February. The three-dimensional finite element model is established to analyze the loading test, and a Genetic Algorithm-Backpropagation Neural Network (GA-BPNN) model is employed for the back-analysis of soil parameters. The analysis of the monitoring data reveals that the cumulative settlement initially exhibits an uplift followed by settlement, while lateral displacement progressively decreases, with the influence range of the load extending to approximately 108 m. Sensitivity analysis identifies the internal friction angle φ and Eref oed of the 2-1A mud layer as the most significant parameters influencing horizontal displacement. The back analysis shows good agreement with the monitoring data, indicating that the model’s predictions align well with the observed results. The in-situ loading tests and back analysis results offer valuable insights into the deformation behavior of soils under surcharge loading and provide a robust framework for model calibration, ultimately contributing to the safety and stability of similar infrastructure projects. To investigate the effects of overlying surcharge on ground deformation in soft soil areas, in-situ loading tests are conducted to monitor soil behavior. Monitoring data, including surface settlement, lateral displacement, stratified settlement, and pore water pressure, are collected from 5th January to 22nd February. The three-dimensional finite element model is established to analyze the loading test, and a Genetic Algorithm-Backpropagation Neural Network (GA-BPNN) model is employed for the back-analysis of soil parameters. The analysis of the monitoring data reveals that the cumulative settlement initially exhibits an uplift followed by settlement, while lateral displacement progressively decreases, with the influence range of the load extending to approximately 108 m. Sensitivity analysis identifies the internal friction angle φ and E ref oed of the 2-1A mud layer as the most significant parameters influencing horizontal displacement. The back analysis shows good agreement with the monitoring data, indicating that the model’s predictions align well with the observed results. The in-situ loading tests and back analysis results offer valuable insights into the deformation behavior of soils under surcharge loading and provide a robust framework for model calibration, ultimately contributing to the safety and stability of similar infrastructure projects. |
| Author | Zhang, Wengang Zhang, Xiaoguang Liu, Zhicheng |
| Author_xml | – sequence: 1 givenname: Zhicheng surname: Liu fullname: Liu, Zhicheng organization: School of Civil Engineering and Transportation, South China University of Technology, Guangzhou Metro Group Co., Ltd – sequence: 2 givenname: Wengang surname: Zhang fullname: Zhang, Wengang email: zhangwg@cqu.edu.cn organization: School of Civil Engineering, Chongqing University, Key Laboratory of New Technology for Construction of Cities in Mountain Area, Chongqing University, Ministry of Education, National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas, Chongqing University – sequence: 3 givenname: Xiaoguang surname: Zhang fullname: Zhang, Xiaoguang organization: Guangzhou Metro Group Co., Ltd |
| BookMark | eNp9kEtPAjEUhRuDiYj8AVdNXFf7nE6XiC-SIS7AhaumTDswiC22w4J_b3FM3Lm5N_fmnJOT7xIMfPAOgGuCbwnG8i5xrJRCmAqECWEClWdgSFnJECslH4AhkfmJBaUXYJzSFmNMVcEYE0PwPvNo0XYHWAVjW7-GS5c62Hr44NweLkLT5dHuoPEW3pv6A0682R1Tm_KVnIXBw7mpN613sHIm-lPE3HWbYK_AeWN2yY1_9wi8PT0upy-oen2eTScVqqmkHSqso4IYWhsrrallw6yxXCqLDW14YbFy1q6UI8JyYgtjeMNos1pxJWlZE8VG4KbP3cfwdcjt9TYcYm6ZNKNCFJkDJ1lFe1UdQ0rRNXof208Tj5pgfaKoe4o6U9Q_FHWZTaw3pSz2axf_ov9xfQMS6nXU |
| Cites_doi | 10.1080/17499518.2021.2010098 10.1016/s0886-7798(03)00087-7 10.1142/s021987622450066x 10.1016/s0951-8320(02)00231-4 10.1016/j.tust.2023.105199 10.1016/j.undsp.2023.05.013 10.3208/sandf.41.39 10.1061/(asce)1090-0241(2008)134:10(1531) 10.1016/j.tust.2015.10.044 10.3208/sandf.43.4_229 10.1016/j.enggeo.2014.03.008 10.1680/geot.53.7.679.37382 10.1139/cgj-2018-0892 10.1016/j.autcon.2024.105394 10.1016/j.tust.2023.105099 10.1080/17499518.2019.1641609 10.1680/geot.1977.27.2.203 10.1016/j.gr.2022.06.011 10.1007/s10462-021-09967-1 10.1016/j.tust.2019.103103 10.1016/0266-352x(96)00040-7 10.1016/j.istruc.2024.105865 10.1016/j.tust.2023.105506 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to the Iran University of Science and Technology 2025 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. The Author(s), under exclusive licence to the Iran University of Science and Technology 2025. |
| Copyright_xml | – notice: The Author(s), under exclusive licence to the Iran University of Science and Technology 2025 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. – notice: The Author(s), under exclusive licence to the Iran University of Science and Technology 2025. |
| DBID | AAYXX CITATION |
| DOI | 10.1007/s40999-025-01135-8 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2383-3874 |
| EndPage | 1716 |
| ExternalDocumentID | 10_1007_s40999_025_01135_8 |
| GrantInformation_xml | – fundername: Guangzhou Metro Grp Co Ltd grantid: JT204-100111-23001 – fundername: Major Projects of the Chinese Academy of Engineering grantid: 2022-D，FZD-10 |
| GroupedDBID | 0R~ 2XV 406 AACDK AAHNG AAIAL AAJBT AANZL AARHV AASML AATNV AATVU AAUYE AAYQN AAYTO ABAKF ABBRH ABDBE ABDZT ABECU ABFSG ABFTV ABJNI ABJOX ABKCH ABMQK ABQBU ABTEG ABTKH ABTMW ABXPI ACAOD ACDTI ACGFS ACHSB ACMLO ACOKC ACPIV ACSTC ACZOJ ADHHG ADKNI ADURQ ADYFF ADZKW AEBTG AEFQL AEJHL AEJRE AEMSY AENEX AEOHA AEPYU AESKC AEVLU AEXYK AEZWR AFBBN AFDZB AFHIU AFOHR AFQWF AFZKB AGDGC AGMZJ AGQEE AGQMX AGRTI AHKAY AHPBZ AHSBF AHWEU AIAKS AIGIU AILAN AITGF AIXLP AJRNO AJZVZ ALFXC ALMA_UNASSIGNED_HOLDINGS AMKLP AMXSW AMYLF AMYQR ASPBG ATHPR AVWKF AXYYD AYFIA BGNMA DNIVK DPUIP EBLON EBS EIOEI EJD FERAY FIGPU FINBP FNLPD FSGXE GGCAI GJIRD IAO IKXTQ ISR ITC IWAJR J-C JZLTJ KOV LLZTM M4Y NPVJJ NQJWS NU0 O9J PT4 RLLFE ROL RSV SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE TSG UG4 UOJIU UTJUX UZXMN VFIZW ZMTXR AAYXX ABJCF ABRTQ AEUYN AFFHD AFKRA BENPR BGLVJ CCPQU CITATION HCIFZ M7S PHGZM PHGZT PQGLB PTHSS |
| ID | FETCH-LOGICAL-c272t-6de251a2cad7dac7f3dad479d0a2f46d09eddb9e15d41d6aa4f32fbb49728c193 |
| IEDL.DBID | RSV |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001514261200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1735-0522 |
| IngestDate | Thu Oct 02 16:35:07 EDT 2025 Sat Nov 29 07:44:46 EST 2025 Sun Jul 06 01:15:56 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Issue | 8 |
| Keywords | Soil deformation Soft soil Genetic algorithm-backpropagation neural network Back analysis In-site loading test |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c272t-6de251a2cad7dac7f3dad479d0a2f46d09eddb9e15d41d6aa4f32fbb49728c193 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 3255638341 |
| PQPubID | 7435105 |
| PageCount | 16 |
| ParticipantIDs | proquest_journals_3255638341 crossref_primary_10_1007_s40999_025_01135_8 springer_journals_10_1007_s40999_025_01135_8 |
| PublicationCentury | 2000 |
| PublicationDate | 20250800 2025-08-00 20250801 |
| PublicationDateYYYYMMDD | 2025-08-01 |
| PublicationDate_xml | – month: 8 year: 2025 text: 20250800 |
| PublicationDecade | 2020 |
| PublicationPlace | Cham |
| PublicationPlace_xml | – name: Cham |
| PublicationTitle | International journal of civil engineering (Tehran. Online) |
| PublicationTitleAbbrev | Int J Civ Eng |
| PublicationYear | 2025 |
| Publisher | Springer International Publishing Springer Nature B.V |
| Publisher_xml | – name: Springer International Publishing – name: Springer Nature B.V |
| References | D Hu (1135_CR18) 2024 FY Liang (1135_CR2) 2024; 14 WG Zhang (1135_CR19) 2021; 54 H Li (1135_CR23) 2023; 136 JH Atkinson (1135_CR4) 1977; 27 HW Huang (1135_CR1) 2016; 51 SY Bai (1135_CR9) 2023; 44 JS Huang (1135_CR20) 2019; 13 J Zhou (1135_CR26) 2024 JZ Zhang (1135_CR6) 2023; 140 S Rampello (1135_CR11) 2003; 43 FS Niazi (1135_CR14) 2020; 57 A Kurtulus (1135_CR16) 2008; 134 B Liu (1135_CR24) 2020 A Fakhimi (1135_CR15) 2004; 19 M Khoiri (1135_CR13) 2014; 174 H Nagaoka (1135_CR17) 2001; 41 PY Hicher (1135_CR7) 1996; 19 A Francos (1135_CR25) 2003; 79 LL Hou (1135_CR22) 2024; 162 G Wei (1135_CR3) 2024; 60 SH Jiang (1135_CR12) 2022; 16 TS Nagaraj (1135_CR8) 2003; 53 ZB Gao (1135_CR10) 2010; 32 CZ Wu (1135_CR21) 2023; 123 Z Ding (1135_CR5) 2024; 144 |
| References_xml | – volume: 16 start-page: 746 issue: 4 year: 2022 ident: 1135_CR12 publication-title: Georisk doi: 10.1080/17499518.2021.2010098 – volume: 19 start-page: 57 issue: 1 year: 2004 ident: 1135_CR15 publication-title: Tunn Undergr Sp Tech doi: 10.1016/s0886-7798(03)00087-7 – year: 2024 ident: 1135_CR18 publication-title: Int J Comput Methods doi: 10.1142/s021987622450066x – volume: 79 start-page: 205 issue: 2 year: 2003 ident: 1135_CR25 publication-title: Reliab Eng Syst Safe doi: 10.1016/s0951-8320(02)00231-4 – volume: 140 year: 2023 ident: 1135_CR6 publication-title: Tunn Undergr Sp Tech doi: 10.1016/j.tust.2023.105199 – volume: 14 start-page: 219 year: 2024 ident: 1135_CR2 publication-title: Undergr Space doi: 10.1016/j.undsp.2023.05.013 – volume: 41 start-page: 39 issue: 1 year: 2001 ident: 1135_CR17 publication-title: Soils Found doi: 10.3208/sandf.41.39 – volume: 134 start-page: 1531 issue: 10 year: 2008 ident: 1135_CR16 publication-title: J Geotech Geoenviron doi: 10.1061/(asce)1090-0241(2008)134:10(1531) – volume-title: Study on retaining structures for large deep exavations in deep soft soil under complex environment year: 2024 ident: 1135_CR26 – volume: 51 start-page: 301 year: 2016 ident: 1135_CR1 publication-title: Tunn Undergr Sp Tech doi: 10.1016/j.tust.2015.10.044 – volume: 43 start-page: 229 issue: 4 year: 2003 ident: 1135_CR11 publication-title: Soils Found doi: 10.3208/sandf.43.4_229 – volume: 174 start-page: 61 year: 2014 ident: 1135_CR13 publication-title: Eng Geol doi: 10.1016/j.enggeo.2014.03.008 – volume: 53 start-page: 679 issue: 7 year: 2003 ident: 1135_CR8 publication-title: Geotechnique doi: 10.1680/geot.53.7.679.37382 – volume: 57 start-page: 851 issue: 6 year: 2020 ident: 1135_CR14 publication-title: Can Geotech J doi: 10.1139/cgj-2018-0892 – volume: 162 year: 2024 ident: 1135_CR22 publication-title: Automat Constr doi: 10.1016/j.autcon.2024.105394 – volume: 136 year: 2023 ident: 1135_CR23 publication-title: Tunn Undergr Sp Tech doi: 10.1016/j.tust.2023.105099 – volume: 13 start-page: 320 issue: 4 year: 2019 ident: 1135_CR20 publication-title: Georisk doi: 10.1080/17499518.2019.1641609 – volume: 44 start-page: 206 issue: 1 year: 2023 ident: 1135_CR9 publication-title: Rock Soil Mech – volume: 32 start-page: 731 issue: 5 year: 2010 ident: 1135_CR10 publication-title: Chin J Geotech Eng – volume: 27 start-page: 203 issue: 2 year: 1977 ident: 1135_CR4 publication-title: Geotechnique doi: 10.1680/geot.1977.27.2.203 – volume: 123 start-page: 184 year: 2023 ident: 1135_CR21 publication-title: Gondwana Res doi: 10.1016/j.gr.2022.06.011 – volume: 54 start-page: 5633 issue: 8 year: 2021 ident: 1135_CR19 publication-title: Artif Intell Rev doi: 10.1007/s10462-021-09967-1 – year: 2020 ident: 1135_CR24 publication-title: Tunn Undergr Sp Tech doi: 10.1016/j.tust.2019.103103 – volume: 19 start-page: 153 issue: 2 year: 1996 ident: 1135_CR7 publication-title: Comput Geotech doi: 10.1016/0266-352x(96)00040-7 – volume: 60 year: 2024 ident: 1135_CR3 publication-title: Structures doi: 10.1016/j.istruc.2024.105865 – volume: 144 year: 2024 ident: 1135_CR5 publication-title: Tunn Undergr Sp Tech doi: 10.1016/j.tust.2023.105506 |
| SSID | ssj0002963335 ssib031263548 ssib045315334 |
| Score | 2.3260524 |
| Snippet | To investigate the effects of overlying surcharge on ground deformation in soft soil areas, in-situ loading tests are conducted to monitor soil behavior.... |
| SourceID | proquest crossref springer |
| SourceType | Aggregation Database Index Database Publisher |
| StartPage | 1701 |
| SubjectTerms | Civil Engineering Engineering Research Paper |
| Title | In-Situ Loading Test in Deep Soft Soil and Back Analysis Based on Machine Learning Method |
| URI | https://link.springer.com/article/10.1007/s40999-025-01135-8 https://www.proquest.com/docview/3255638341 |
| Volume | 23 |
| WOSCitedRecordID | wos001514261200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAVX databaseName: SpringerLink customDbUrl: eissn: 2383-3874 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002963335 issn: 1735-0522 databaseCode: RSV dateStart: 20160101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3NS8MwFA86PejBb3E6JQdvGkjStEmPfg0FN8RN0VNJ8yFF6ca6-febZu2moge9FEpK2r78kvdr897vAXAslSVpSCIUpdgilnKDJOEKYSmUFSIWWEhfbIJ3u-LpKb6rksKKOtq93pL0K_Us2Y1hnzFPy2AzEoRILIIl5-5EOR3ve481igJS6qvMST5zKCvzTWd_XqjDXOArbxLuusGOgVTZND_f5qvHmtPQbzun3iG11__3KhtgrSKg8GyKmE2wYPItsPpJlnAbPN_kqJeNJ_B24CPsYd89CcxyeGnMEPbcwu0O2RuUuYbnUr3CWtnEnRVGw0EOOz5G08BKvvUFdnyl6h3w0L7qX1yjqgQDUpTTMYq0cQRIUiU111JxG2ipGY81ltSySOPYaJ3GhoSaER1JyWxAbZqymFOhHDncBY18kJs9AEMdKRpqwgwLmRRWYAceFgtjpEldYxOc1GZOhlOljWSmqewNljiDJd5giWiCVj0SSTXriiQo9dTcJzcjTXBaW37e_Htv-3-7_ACsUD94ZRxgCzTGo4k5BMvqfZwVoyOPxg953NVy |
| linkProvider | Springer Nature |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3JTsMwEB2xScCBHVFWH7iBpdhxYufIVrWirRAtqJwix3ZQBEorWvh-HJO0gOAAl0iRIycZv3jG8Zs3AMdSpSQJSIjDxEsxS7jBknCFPSlUKkQkPCFdsQne6Yh-P7opk8JGFdu92pJ0M_Uk2Y15LmOeFmQz4gdYzMI8sx6rIPLddu8rFPmk0FeZBvnMoqzIN538eaEWc76rvEm47cazEUiZTfPzbb56rGkY-m3n1Dmk-ur_XmUNVsoAFJ19IGYdZky-AcufZAk34aGZ4242fkWtgWPYo559EpTl6NKYIeraidsesmckc43OpXpClbKJPRsZjQY5ajuOpkGlfOsjartK1VtwV7_qXTRwWYIBK8rpGIfa2ABIUiU111Lx1NdSMx5pT9KUhdqLjNZJZEigGdGhlCz1aZokLOJUKBscbsNcPsjNDqBAh4oGmjDDAiZFKjwLHhYJY6RJbGMNTiozx8MPpY14oqnsDBZbg8XOYLGowX41EnH51Y1iv9BTs0tuRmpwWll-2vx7b7t_u_wIFhu9dituNTvXe7BE3UAWnMB9mBu_vJoDWFBv42z0cuiQ-Q6EE9hW |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8QwEB58IXrwLa7PHLxpsEnTJj36WhR1EVZFTyXNQxalu-xWf79ptt1V0YN4KZSUtJ2ZNl-S-b4B2JfKkiwiMY6zwGKWcYMl4QoHUigrRCICIX2xCd5qicfH5PYTi99nu9dbkkNOQ6nSlBdHPW2PRsQ3Fnj2PC0Tz0gYYTEJ06wsGlTO19sPdUSFpNRaGQN-5iKu5J6OVmGoi7_QV-Ek3HUTODRSMWt-vs3X0WsMSb_tovrBqbn4_9dagoUKmKLjYSQtw4TJV2D-k1zhKjxd5rjdKd7Qdddn3qM791Sok6MzY3qo7X7o7tB5RTLX6ESqF1QrnrizgdGom6Mbn7tpUCXr-oxufAXrNbhvnt-dXuCqNANWlNMCx9o4YCSpkpprqbgNtdSMJzqQ1LJYB4nROksMiTQjOpaS2ZDaLGMJp0I50LgOU3k3NxuAIh0rGmnCDIuYFFYELqhYIoyRJnONDTioTZ72hgoc6Uhr2RssdQZLvcFS0YDt2itp9TUO0rDUWXNTcUYacFh7Ydz8e2-bf7t8D2Zvz5rp9WXragvmqPdjmSq4DVNF_83swIx6LzqD_q4P0g9C1eE6 |
| 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=In-Situ+Loading+Test+in+Deep+Soft+Soil+and+Back+Analysis+Based+on+Machine+Learning+Method&rft.jtitle=International+journal+of+civil+engineering+%28Tehran.+Online%29&rft.au=Liu%2C+Zhicheng&rft.au=Zhang%2C+Wengang&rft.au=Zhang%2C+Xiaoguang&rft.date=2025-08-01&rft.pub=Springer+Nature+B.V&rft.issn=1735-0522&rft.eissn=2383-3874&rft.volume=23&rft.issue=8&rft.spage=1701&rft.epage=1716&rft_id=info:doi/10.1007%2Fs40999-025-01135-8&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1735-0522&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1735-0522&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1735-0522&client=summon |