Rapid visual screening of soft-story buildings from street view images using deep learning classification
Rapid and accurate identification of potential structural deficiencies is a crucial task in evaluating seismic vulnerability of large building inventories in a region. In the case of multi-story structures, abrupt vertical variations of story stiffness are known to significantly increase the likelih...
Uložené v:
| Vydané v: | Earthquake Engineering and Engineering Vibration Ročník 19; číslo 4; s. 827 - 838 |
|---|---|
| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Harbin
Institute of Engineering Mechanics, China Earthquake Administration
01.10.2020
Springer Nature B.V International Computer Science Institute, University of California, Berkeley, CA, USA %Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA %Civil and Environmental Engineering, University of California, Los Angeles, CA, USA %Civil and Environmental Engineering, Stanford University, CA, USA |
| Predmet: | |
| ISSN: | 1671-3664, 1993-503X |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Rapid and accurate identification of potential structural deficiencies is a crucial task in evaluating seismic vulnerability of large building inventories in a region. In the case of multi-story structures, abrupt vertical variations of story stiffness are known to significantly increase the likelihood of collapse during moderate or severe earthquakes. Identifying and retrofitting buildings with such irregularities—generally termed as soft-story buildings—is, therefore, vital in earthquake preparedness and loss mitigation efforts. Soft-story building identification through conventional means is a labor-intensive and time-consuming process. In this study, an automated procedure was devised based on deep learning techniques for identifying soft-story buildings from street-view images at a regional scale. A database containing a large number of building images and a semi-automated image labeling approach that effectively annotates new database entries was developed for developing the deep learning model. Extensive computational experiments were carried out to examine the effectiveness of the proposed procedure, and to gain insights into automated soft-story building identification. |
|---|---|
| AbstractList | Rapid and accurate identification of potential structural deficiencies is a crucial task in evaluating seismic vulnerability of large building inventories in a region. In the case of multi-story structures, abrupt vertical variations of story stiffness are known to significantly increase the likelihood of collapse during moderate or severe earthquakes. Identifying and retrofitting buildings with such irregularities—generally termed as soft-story buildings—is, therefore, vital in earthquake preparedness and loss mitigation efforts. Soft-story building identification through conventional means is a labor-intensive and time-consuming process. In this study, an automated procedure was devised based on deep learning techniques for identifying soft-story buildings from street-view images at a regional scale. A database containing a large number of building images and a semi-automated image labeling approach that effectively annotates new database entries was developed for developing the deep learning model. Extensive computational experiments were carried out to examine the effectiveness of the proposed procedure, and to gain insights into automated soft-story building identification. |
| Author | McKenna, Frank Yu, Qian Wang, Chaofeng Law, Kincho H. Yu, Stella X. Taciroglu, Ertugrul Cetiner, Barbaros |
| AuthorAffiliation | International Computer Science Institute, University of California, Berkeley, CA, USA %Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA %Civil and Environmental Engineering, University of California, Los Angeles, CA, USA %Civil and Environmental Engineering, Stanford University, CA, USA |
| AuthorAffiliation_xml | – name: International Computer Science Institute, University of California, Berkeley, CA, USA %Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA %Civil and Environmental Engineering, University of California, Los Angeles, CA, USA %Civil and Environmental Engineering, Stanford University, CA, USA |
| Author_xml | – sequence: 1 givenname: Qian surname: Yu fullname: Yu, Qian organization: International Computer Science Institute, University of California – sequence: 2 givenname: Chaofeng surname: Wang fullname: Wang, Chaofeng email: c_w@berkeley.edu organization: Department of Civil and Environmental Engineering, University of California – sequence: 3 givenname: Frank surname: McKenna fullname: McKenna, Frank organization: Department of Civil and Environmental Engineering, University of California – sequence: 4 givenname: Stella X. surname: Yu fullname: Yu, Stella X. organization: International Computer Science Institute, University of California – sequence: 5 givenname: Ertugrul surname: Taciroglu fullname: Taciroglu, Ertugrul organization: Civil and Environmental Engineering, University of California – sequence: 6 givenname: Barbaros surname: Cetiner fullname: Cetiner, Barbaros organization: Civil and Environmental Engineering, University of California – sequence: 7 givenname: Kincho H. surname: Law fullname: Law, Kincho H. organization: Civil and Environmental Engineering, Stanford University |
| BookMark | eNp9kVtrHSEURqWk0CTtD-ib0Neabi9z8bGEXgKBQEmgb2IcHQwTPXU7DSe_vp5MIVBonhRZ61P3d0KOUk6ekPcczjjA8Ak5H0EyEMCg0yMTr8gx11qyDuTPo7bvB85k36s35ATxDqBXQvbHJP6wuzjR3xFXu1B0xfsU00xzoJhDZVhz2dPbNS5TO0YaSr6nWBtWm-QfaLy3s0e64sGavN_RxdvylOEWixhDdLbGnN6S18Eu6N_9XU_Jzdcv1-ff2eXVt4vzz5fMyU5W1nNw4-iD0FJZ57yUVoOQTslwKyHo3g6DGAYAb5XzfFJKCy6nTk9DH0CDPCUft9wHm4JNs7nLa0ntRjM9zm4_u8fJeNEGBQpANvzDhu9K_rV6rM-8UJ3gMIpRNWrYKFcyYvHBuFifvlWLjYvhYA41mK0G09LNoQYjmsn_MXelzazsX3TE5mBj0-zL85v-L_0B4TCcuw |
| CitedBy_id | crossref_primary_10_1016_j_isprsjprs_2023_09_001 crossref_primary_10_1080_23311916_2022_2065900 crossref_primary_10_1061__ASCE_ST_1943_541X_0003392 crossref_primary_10_1016_j_istruc_2025_108332 crossref_primary_10_1061_JPCFEV_CFENG_4930 crossref_primary_10_3390_app11167540 crossref_primary_10_1007_s11803_022_2074_7 crossref_primary_10_1007_s11803_022_2079_2 crossref_primary_10_1177_01655515231202761 crossref_primary_10_1016_j_strusafe_2022_102260 crossref_primary_10_1007_s11803_023_2167_y crossref_primary_10_3390_su142316063 crossref_primary_10_1016_j_buildenv_2023_110215 crossref_primary_10_1016_j_ress_2023_109104 crossref_primary_10_1111_mice_12747 crossref_primary_10_3390_buildings12081167 crossref_primary_10_1007_s42452_024_06070_2 crossref_primary_10_1061__ASCE_CP_1943_5487_0001025 crossref_primary_10_3390_s23229118 crossref_primary_10_1061__ASCE_CP_1943_5487_0001034 crossref_primary_10_1002_eqe_3805 crossref_primary_10_3390_buildings14082348 crossref_primary_10_1016_j_landurbplan_2021_104217 crossref_primary_10_1061_NHREFO_NHENG_1649 crossref_primary_10_1007_s10518_024_01927_8 crossref_primary_10_1016_j_istruc_2025_109951 crossref_primary_10_1177_23998083241247870 crossref_primary_10_1016_j_cities_2022_103787 crossref_primary_10_1016_j_autcon_2020_103474 crossref_primary_10_3390_buildings14010014 crossref_primary_10_1007_s11069_022_05553_y crossref_primary_10_1177_01655515221074336 crossref_primary_10_3390_app14125350 crossref_primary_10_3390_app14166992 crossref_primary_10_1061_JCCEE5_CPENG_6030 crossref_primary_10_3390_ijgi12070264 crossref_primary_10_1061__ASCE_CP_1943_5487_0000968 crossref_primary_10_1016_j_autcon_2021_103968 crossref_primary_10_1016_j_resconrec_2023_107140 |
| Cites_doi | 10.1109/WACV.2017.42 10.1109/ICCV.2015.169 10.1016/j.istruc.2015.03.002 10.1139/cjce-2012-0055 10.1061/(ASCE)NH.1527-6996.0000246 10.1016/j.soildyn.2017.02.001 10.1109/CVPRW.2014.121 10.1609/aaai.v31i1.11231 10.1109/CVPR.2016.308 10.1016/j.ijdrr.2018.01.033 10.1061/(ASCE)1084-0680(2008)13:4(189) 10.1109/CVPR.2016.319 10.1109/CVPR.2016.90 10.5281/zenodo.3463676 10.1109/CVPR.2009.5206848 10.1145/3123266.3123271 10.1016/j.isprsjprs.2018.02.006 10.1073/pnas.1700035114 10.1061/(ASCE)CP.1943-5487.0000472 |
| ContentType | Journal Article |
| Copyright | Institute of Engineering Mechanics, China Earthquake Administration 2020 Institute of Engineering Mechanics, China Earthquake Administration 2020. Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| Copyright_xml | – notice: Institute of Engineering Mechanics, China Earthquake Administration 2020 – notice: Institute of Engineering Mechanics, China Earthquake Administration 2020. – notice: Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| DBID | AAYXX CITATION 7ST 7TG 7TN 8FD C1K F1W FR3 H96 KL. KR7 L.G SOI 2B. 4A8 92I 93N PSX TCJ |
| DOI | 10.1007/s11803-020-0598-2 |
| DatabaseName | CrossRef Environment Abstracts Meteorological & Geoastrophysical Abstracts Oceanic Abstracts Technology Research Database Environmental Sciences and Pollution Management ASFA: Aquatic Sciences and Fisheries Abstracts Engineering Research Database Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources Meteorological & Geoastrophysical Abstracts - Academic Civil Engineering Abstracts Aquatic Science & Fisheries Abstracts (ASFA) Professional Environment Abstracts Wanfang Data Journals - Hong Kong WANFANG Data Centre Wanfang Data Journals 万方数据期刊 - 香港版 China Online Journals (COJ) China Online Journals (COJ) |
| DatabaseTitle | CrossRef Civil Engineering Abstracts Aquatic Science & Fisheries Abstracts (ASFA) Professional Meteorological & Geoastrophysical Abstracts Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources Oceanic Abstracts Technology Research Database ASFA: Aquatic Sciences and Fisheries Abstracts Engineering Research Database Environment Abstracts Meteorological & Geoastrophysical Abstracts - Academic Environmental Sciences and Pollution Management |
| DatabaseTitleList | Civil Engineering Abstracts |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1993-503X |
| EndPage | 838 |
| ExternalDocumentID | dzgcygczd_e202004003 10_1007_s11803_020_0598_2 |
| GrantInformation_xml | – fundername: This study is based upon work supported by the US National Science Foundation under Grant No. 1612843. NHERI DesignSafe (Rathje et al,, 2017) and Texas Advanced Computing Center funderid: (TACC) |
| GroupedDBID | -5B -5G -BR -EM -SA -S~ -Y2 -~C .86 .VR 06D 0R~ 0VY 29G 2B. 2C. 2J2 2JN 2JY 2KG 2KM 2LR 2VQ 2~H 30V 3V. 4.4 406 408 40D 40E 5GY 5VR 5VS 6NX 7XC 88I 8FE 8FG 8FH 8TC 8UJ 92E 92I 92Q 93N 95- 95. 95~ 96X AAAVM AABHQ AAHNG AAIAL AAJBT AAJKR AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAXDM AAYIU AAYQN AAYTO AAYZH ABDZT ABECU ABFTV ABHQN ABJCF ABJNI ABJOX ABKCH ABMNI ABMQK ABNWP ABQBU ABSXP ABTAH ABTEG ABTHY ABTKH ABTMW ABUWG ABWNU ABXPI ACBXY ACDTI ACGFS ACGOD ACHSB ACHXU ACIWK ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACZOJ ADHHG ADHIR ADINQ ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFQL AEGAL AEGNC AEJHL AEJRE AEMSY AENEX AEOHA AEPYU AESKC AETLH AEUYN AEVLU AEXYK AFBBN AFGCZ AFKRA AFLOW AFQWF AFRAH AFUIB AFWTZ AFZKB AGAYW AGDGC AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARMRJ ASPBG ATCPS AVWKF AXYYD AZFZN AZQEC B-. BA0 BDATZ BENPR BGLVJ BGNMA BHPHI BKSAR BPHCQ CAG CAJEA CCEZO CCPQU CCVFK CHBEP COF CS3 CSCUP CW9 D1K DDRTE DNIVK DPUIP DU5 DWQXO EBLON EBS EIOEI EJD ESBYG FA0 FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 H13 HCIFZ HF~ HG6 HLICF HMJXF HRMNR HZ~ I-F IJ- IKXTQ IWAJR IXD I~X I~Z J-C JBSCW JZLTJ K6- KOV L6V LK5 LLZTM M2P M4Y M7R M7S MA- NPVJJ NQJWS NU0 O9- O9J P2P P9P PATMY PCBAR PF0 PQQKQ PROAC PT4 PTHSS PYCSY Q-- Q2X QOS R89 R9I RNS ROL RPX RSV S16 S1Z S27 S3B SAP SCL SDH SEG SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 TCJ TGP TSG TUC TUS U1G U2A U5K UG4 UOJIU UTJUX UZXMN VC2 VFIZW W48 WK8 YLTOR Z7Z ZMTXR ZY4 ~A9 AACDK AAYXX ABAKF ABDBE ABFSG ABRTQ ACAOD ACSTC AEZWR AFDZB AFFHD AFHIU AFOHR AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PQGLB 7ST 7TG 7TN 8FD C1K F1W FR3 H96 KL. KR7 L.G SOI 4A8 PMFND PSX |
| ID | FETCH-LOGICAL-c353t-610c88ef2934acce33a9023c43fb30f96a7727700ea4ce1d449213d59d76f0903 |
| IEDL.DBID | RSV |
| ISICitedReferencesCount | 44 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000581065100002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1671-3664 |
| IngestDate | Thu May 29 04:11:17 EDT 2025 Thu Sep 18 00:04:17 EDT 2025 Tue Nov 18 20:31:54 EST 2025 Sat Nov 29 06:07:15 EST 2025 Fri Feb 21 02:47:54 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Keywords | deep learning soft-story building CNN rapid visual screening street view image |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c353t-610c88ef2934acce33a9023c43fb30f96a7727700ea4ce1d449213d59d76f0903 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 2452108284 |
| PQPubID | 54346 |
| PageCount | 12 |
| ParticipantIDs | wanfang_journals_dzgcygczd_e202004003 proquest_journals_2452108284 crossref_citationtrail_10_1007_s11803_020_0598_2 crossref_primary_10_1007_s11803_020_0598_2 springer_journals_10_1007_s11803_020_0598_2 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-10-01 |
| PublicationDateYYYYMMDD | 2020-10-01 |
| PublicationDate_xml | – month: 10 year: 2020 text: 2020-10-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Harbin |
| PublicationPlace_xml | – name: Harbin – name: Dordrecht |
| PublicationTitle | Earthquake Engineering and Engineering Vibration |
| PublicationTitleAbbrev | Earthq. Eng. Eng. Vib |
| PublicationTitle_FL | Earthquake Engineering and Engineering Vibration |
| PublicationYear | 2020 |
| Publisher | Institute of Engineering Mechanics, China Earthquake Administration Springer Nature B.V International Computer Science Institute, University of California, Berkeley, CA, USA %Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA %Civil and Environmental Engineering, University of California, Los Angeles, CA, USA %Civil and Environmental Engineering, Stanford University, CA, USA |
| Publisher_xml | – name: Institute of Engineering Mechanics, China Earthquake Administration – name: Springer Nature B.V – name: International Computer Science Institute, University of California, Berkeley, CA, USA %Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA %Civil and Environmental Engineering, University of California, Los Angeles, CA, USA %Civil and Environmental Engineering, Stanford University, CA, USA |
| References | Zhou B, Khosla A, Lapedriza A, Oliva A and Torralba A (2016), “Learning Deep Features for Discriminative Localization,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. He K, Zhang X, Ren S and Sun J (2016), “Deep Residual Learning for Image Recognition,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. WangCChenQA Hybrid Geotechnical and Geological Data-Based Framework for Multiscale Regional Liquefaction Hazard MappingGéotechnique2018687614625 Bency AJ, Rallapalli S, Ganti RK, Srivatsa M and Manjunath B (2017), “Beyond Spatial Auto-Regressive Models: Predicting Housing Prices with Satellite Imagery,” Proceedings of the IEEE Winter Conference on Applications of Computer Vision. Law S, Paige B and Russell C (2018), “Take a Look Around: Using Street View and Satellite Images to Estimate House Prices,” arXiv Preprint arXv.1807.07155. GebruTKrauseJWangYChenDDengJAidenELFeiFeiLUsing Deep Learning and Google Street View to Estimate the Demographic Makeup of Neighborhoods Across the United StatesProceedings of the National Academy of Sciences201711450131081311310.1073/pnas.1700035114 Girshick R (2015), “Fast r-cnn,” Proceedings of the IEEE International Conference on Computer Vision. Perrone D, Aiello MA, Pecce M and Rossi F (2015), “Rapid Visual Screening for Seismic evaluation of RC Hospital Buildings,” Structures, Vol. 3, Elsevier, 57–70. WallaceNMMillerTHSeismic Screening of Public Facilities in Oregon’s Western CountiesPractice Periodical on Structural Design and Construction200813418919710.1061/(ASCE)1084-0680(2008)13:4(189) Szegedy C, Vanhoucke V, Ioffe S, Shlens J and Wojna Z (2016), “Rethinking the Inception Architecture for Computer Vision,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. RathjeEMDawsonCPadgettJEPinelliJPStanzioneDAdairAArduinoPBrandenbergS JCockerillTDeyCDesignsafe: New Cyberinfrastructure for Natural Hazards EngineeringNatural Hazards Review20171830601700110.1061/(ASCE)NH.1527-6996.0000246 Wang C (2019). “NHERI-SimCenter/SURF: v0.2.0,” <https://doi.org/10.5281/zenodo.3463676> (September). ATC (2002), “Rapid Visual Screening of Buildings for Potential Seismic Hazards: A Handbook, FEMA 154, second edition,” Applied Technology Council, National Earthquakes Hazards Reduction Program, USA. Liu X, Chen Q, Zhu L, Xu Y and Lin L (2017). “Place-Centric Visual Urban Perception with Deep Multi-Instance Regression,” Proceedings of the 25th ACM International Conference on Multimedia. Szegedy C, Ioffe S, Vanhoucke V and Alemi AA (2017), “Inception-V4, Inception-Resnet and the Impact of Residual Connections on Learning,” Proceedings of the 31st AAAI Conference on Artificial Intelligence. ATC (1988), “Rapid Visual Screening of Buildings for Potential Seismic Hazards: A Handbook, FEMA 154,” Federal Emergency Management Agency, Washington, DC, USA. WangCChenQShenMJuangCHOn the Spatial Variability of Cpt-Based Geotechnical Parameters for Regional Liquefaction EvaluationSoil Dynamics and Earthquake Engineering20179515316610.1016/j.soildyn.2017.02.001 SaatciogluMShooshtariMFooSSeismic Screening of Buildings Based on the 2010 National Building Code of CanadaCanadian Journal of Civil Engineering201340548349810.1139/cjce-2012-0055 NingthoujamMNandaRPRapid Visual Screening Procedure of Existing Building Based on Statistical AnalysisInternational Journal of Disaster Risk Reduction20182872073010.1016/j.ijdrr.2018.01.033 KangJKörnerMWangYTaubenböckHZhuXXBuilding Instance Classification Using Street View ImagesISPRS Journal of Photogrammetry and Remote Sensing2018145445910.1016/j.isprsjprs.2018.02.006 Karbassi A and Nollet M (2007), “The Adaptation of the FEMA 154 Methodology for the Rapid Visual Screening of Existing Buildings in Accordance with Nbcc-2005,” Proceedings of the 9th Canadian Conference on Earthquake Engineering, Ottawa, Ont, 27–29. PloegerSSawadaMElsabbaghASaatciogluMNastevMRosettiEUrban Rat: New Tool for Virtual and Site-Specific Mobile Rapid Data Collection for Seismic Risk AssessmentJournal of Computing in Civil Engineering20163020401500610.1061/(ASCE)CP.1943-5487.0000472 Ren S, He K, Girshick R and Sun J (2015), “Faster R-Cnn: Towards Real-Time Object Detection with Region Proposal Networks,” Advances in Neural Information Processing Systems. SrikanthTKumarRPSinghAPRastogiBKKumarSEarthquake Vulnerability Assessment of Existing Buildings in Gandhidham and Adipur Cities Kachchh, Gujarat (India)European Journal of Scientific Research2010413336353 Sun Y, Chen Y, Wang X and Tang X (2014), “Deep Learning Face Representation by Joint Identification-Verification,” Advances in Neural Information Processing Systems. Deng J, Dong W, Socher R, Li LJ, Li K and FeiFei L (2009), “ImageNet: A Large-Scale Hierarchical Image Database,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. ATC (2015), “Rapid Visual Screening of Buildings for Potential Seismic Hazards: A Handbook, FEMA 154, third edition,” Federal Emergency Management Agency, Washington DC, USA. Naik N, Philipoom J, Raskar R and Hidalgo C (2014), “Streetscore-Predicting the Perceived Safety of One Million Streetscapes,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. C Wang (598_CR27) 2017; 95 C Wang (598_CR26) 2018; 68 J Kang (598_CR9) 2018; 145 598_CR28 M Ningthoujam (598_CR14) 2018; 28 598_CR12 598_CR8 598_CR11 598_CR7 598_CR10 598_CR5 598_CR4 598_CR15 598_CR3 598_CR2 598_CR13 S Ploeger (598_CR16) 2016; 30 598_CR1 EM Rathje (598_CR17) 2017; 18 T Srikanth (598_CR20) 2010; 41 598_CR18 598_CR23 T Gebru (598_CR6) 2017; 114 598_CR22 598_CR21 598_CR25 M Saatcioglu (598_CR19) 2013; 40 NM Wallace (598_CR24) 2008; 13 |
| References_xml | – reference: WangCChenQShenMJuangCHOn the Spatial Variability of Cpt-Based Geotechnical Parameters for Regional Liquefaction EvaluationSoil Dynamics and Earthquake Engineering20179515316610.1016/j.soildyn.2017.02.001 – reference: He K, Zhang X, Ren S and Sun J (2016), “Deep Residual Learning for Image Recognition,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. – reference: Wang C (2019). “NHERI-SimCenter/SURF: v0.2.0,” <https://doi.org/10.5281/zenodo.3463676> (September). – reference: ATC (1988), “Rapid Visual Screening of Buildings for Potential Seismic Hazards: A Handbook, FEMA 154,” Federal Emergency Management Agency, Washington, DC, USA. – reference: Ren S, He K, Girshick R and Sun J (2015), “Faster R-Cnn: Towards Real-Time Object Detection with Region Proposal Networks,” Advances in Neural Information Processing Systems. – reference: Zhou B, Khosla A, Lapedriza A, Oliva A and Torralba A (2016), “Learning Deep Features for Discriminative Localization,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. – reference: ATC (2002), “Rapid Visual Screening of Buildings for Potential Seismic Hazards: A Handbook, FEMA 154, second edition,” Applied Technology Council, National Earthquakes Hazards Reduction Program, USA. – reference: Karbassi A and Nollet M (2007), “The Adaptation of the FEMA 154 Methodology for the Rapid Visual Screening of Existing Buildings in Accordance with Nbcc-2005,” Proceedings of the 9th Canadian Conference on Earthquake Engineering, Ottawa, Ont, 27–29. – reference: GebruTKrauseJWangYChenDDengJAidenELFeiFeiLUsing Deep Learning and Google Street View to Estimate the Demographic Makeup of Neighborhoods Across the United StatesProceedings of the National Academy of Sciences201711450131081311310.1073/pnas.1700035114 – reference: ATC (2015), “Rapid Visual Screening of Buildings for Potential Seismic Hazards: A Handbook, FEMA 154, third edition,” Federal Emergency Management Agency, Washington DC, USA. – reference: SaatciogluMShooshtariMFooSSeismic Screening of Buildings Based on the 2010 National Building Code of CanadaCanadian Journal of Civil Engineering201340548349810.1139/cjce-2012-0055 – reference: Sun Y, Chen Y, Wang X and Tang X (2014), “Deep Learning Face Representation by Joint Identification-Verification,” Advances in Neural Information Processing Systems. – reference: KangJKörnerMWangYTaubenböckHZhuXXBuilding Instance Classification Using Street View ImagesISPRS Journal of Photogrammetry and Remote Sensing2018145445910.1016/j.isprsjprs.2018.02.006 – reference: NingthoujamMNandaRPRapid Visual Screening Procedure of Existing Building Based on Statistical AnalysisInternational Journal of Disaster Risk Reduction20182872073010.1016/j.ijdrr.2018.01.033 – reference: RathjeEMDawsonCPadgettJEPinelliJPStanzioneDAdairAArduinoPBrandenbergS JCockerillTDeyCDesignsafe: New Cyberinfrastructure for Natural Hazards EngineeringNatural Hazards Review20171830601700110.1061/(ASCE)NH.1527-6996.0000246 – reference: Liu X, Chen Q, Zhu L, Xu Y and Lin L (2017). “Place-Centric Visual Urban Perception with Deep Multi-Instance Regression,” Proceedings of the 25th ACM International Conference on Multimedia. – reference: Naik N, Philipoom J, Raskar R and Hidalgo C (2014), “Streetscore-Predicting the Perceived Safety of One Million Streetscapes,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. – reference: Szegedy C, Ioffe S, Vanhoucke V and Alemi AA (2017), “Inception-V4, Inception-Resnet and the Impact of Residual Connections on Learning,” Proceedings of the 31st AAAI Conference on Artificial Intelligence. – reference: Bency AJ, Rallapalli S, Ganti RK, Srivatsa M and Manjunath B (2017), “Beyond Spatial Auto-Regressive Models: Predicting Housing Prices with Satellite Imagery,” Proceedings of the IEEE Winter Conference on Applications of Computer Vision. – reference: Deng J, Dong W, Socher R, Li LJ, Li K and FeiFei L (2009), “ImageNet: A Large-Scale Hierarchical Image Database,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. – reference: Szegedy C, Vanhoucke V, Ioffe S, Shlens J and Wojna Z (2016), “Rethinking the Inception Architecture for Computer Vision,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. – reference: Law S, Paige B and Russell C (2018), “Take a Look Around: Using Street View and Satellite Images to Estimate House Prices,” arXiv Preprint arXv.1807.07155. – reference: WangCChenQA Hybrid Geotechnical and Geological Data-Based Framework for Multiscale Regional Liquefaction Hazard MappingGéotechnique2018687614625 – reference: Girshick R (2015), “Fast r-cnn,” Proceedings of the IEEE International Conference on Computer Vision. – reference: Perrone D, Aiello MA, Pecce M and Rossi F (2015), “Rapid Visual Screening for Seismic evaluation of RC Hospital Buildings,” Structures, Vol. 3, Elsevier, 57–70. – reference: PloegerSSawadaMElsabbaghASaatciogluMNastevMRosettiEUrban Rat: New Tool for Virtual and Site-Specific Mobile Rapid Data Collection for Seismic Risk AssessmentJournal of Computing in Civil Engineering20163020401500610.1061/(ASCE)CP.1943-5487.0000472 – reference: SrikanthTKumarRPSinghAPRastogiBKKumarSEarthquake Vulnerability Assessment of Existing Buildings in Gandhidham and Adipur Cities Kachchh, Gujarat (India)European Journal of Scientific Research2010413336353 – reference: WallaceNMMillerTHSeismic Screening of Public Facilities in Oregon’s Western CountiesPractice Periodical on Structural Design and Construction200813418919710.1061/(ASCE)1084-0680(2008)13:4(189) – ident: 598_CR4 doi: 10.1109/WACV.2017.42 – ident: 598_CR7 doi: 10.1109/ICCV.2015.169 – volume: 41 start-page: 336 issue: 3 year: 2010 ident: 598_CR20 publication-title: European Journal of Scientific Research – volume: 68 start-page: 614 issue: 7 year: 2018 ident: 598_CR26 publication-title: Géotechnique – ident: 598_CR10 – ident: 598_CR2 – ident: 598_CR15 doi: 10.1016/j.istruc.2015.03.002 – volume: 40 start-page: 483 issue: 5 year: 2013 ident: 598_CR19 publication-title: Canadian Journal of Civil Engineering doi: 10.1139/cjce-2012-0055 – volume: 18 start-page: 06017001 issue: 3 year: 2017 ident: 598_CR17 publication-title: Natural Hazards Review doi: 10.1061/(ASCE)NH.1527-6996.0000246 – volume: 95 start-page: 153 year: 2017 ident: 598_CR27 publication-title: Soil Dynamics and Earthquake Engineering doi: 10.1016/j.soildyn.2017.02.001 – ident: 598_CR13 doi: 10.1109/CVPRW.2014.121 – ident: 598_CR22 doi: 10.1609/aaai.v31i1.11231 – ident: 598_CR23 doi: 10.1109/CVPR.2016.308 – volume: 28 start-page: 720 year: 2018 ident: 598_CR14 publication-title: International Journal of Disaster Risk Reduction doi: 10.1016/j.ijdrr.2018.01.033 – volume: 13 start-page: 189 issue: 4 year: 2008 ident: 598_CR24 publication-title: Practice Periodical on Structural Design and Construction doi: 10.1061/(ASCE)1084-0680(2008)13:4(189) – ident: 598_CR28 doi: 10.1109/CVPR.2016.319 – ident: 598_CR21 – ident: 598_CR8 doi: 10.1109/CVPR.2016.90 – ident: 598_CR3 – ident: 598_CR25 doi: 10.5281/zenodo.3463676 – ident: 598_CR1 – ident: 598_CR18 – ident: 598_CR5 doi: 10.1109/CVPR.2009.5206848 – ident: 598_CR11 – ident: 598_CR12 doi: 10.1145/3123266.3123271 – volume: 145 start-page: 44 year: 2018 ident: 598_CR9 publication-title: ISPRS Journal of Photogrammetry and Remote Sensing doi: 10.1016/j.isprsjprs.2018.02.006 – volume: 114 start-page: 13108 issue: 50 year: 2017 ident: 598_CR6 publication-title: Proceedings of the National Academy of Sciences doi: 10.1073/pnas.1700035114 – volume: 30 start-page: 04015006 issue: 2 year: 2016 ident: 598_CR16 publication-title: Journal of Computing in Civil Engineering doi: 10.1061/(ASCE)CP.1943-5487.0000472 |
| SSID | ssj0064236 |
| Score | 2.406611 |
| Snippet | Rapid and accurate identification of potential structural deficiencies is a crucial task in evaluating seismic vulnerability of large building inventories in a... |
| SourceID | wanfang proquest crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 827 |
| SubjectTerms | Automation Buildings Civil Engineering Computer applications Control Deep learning Disaster management Dynamical Systems Earth Sciences Earthquakes Emergency preparedness Geotechnical Engineering & Applied Earth Sciences Identification Image classification Labour Machine learning Mitigation Multistory buildings Procedures Recent progress in evaluation and improvement on seismic resilience of engineering structures Retrofitting Seismic activity Seismic hazard Seismic surveys Special Section: Recent Progress in Evaluation and Improvement on Seismic Resilience of Engineering Structures Stiffness Vibration Vulnerability |
| Title | Rapid visual screening of soft-story buildings from street view images using deep learning classification |
| URI | https://link.springer.com/article/10.1007/s11803-020-0598-2 https://www.proquest.com/docview/2452108284 https://d.wanfangdata.com.cn/periodical/dzgcygczd-e202004003 |
| Volume | 19 |
| WOSCitedRecordID | wos000581065100002&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 Contemporary 1997-Present customDbUrl: eissn: 1993-503X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0064236 issn: 1671-3664 databaseCode: RSV dateStart: 20020601 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/eLvHCXMwnV3NS8MwFA86PejBb3E6JQe9KIW0SdPkKKJ4GjI_2K00X6Wg3Vi3wfbXm3TtOkEGes5LKO-9vPfS3_sA4BpTwRPfMI9SjTxCg8jjSCiPGmEMkQyTQJbDJqJul_X7_KWq4y7qbPcakiwtdVPs5jOX-xM48JZb8W6CLevtmLuNvdeP2vzaeLqcC-jTyPcwpaSGMn874qczaiLMJShalvLkJsnTFa_ztP-v7z0Ae1WQCe8XWnEINnR-BHZXWg8eg6yXDDMFp1kxsZTWdtj3rF2AAwMLa5k9lzQ5g6Iaml1AV4YCixLChg5NgNmXtUQFdHnzKVRaD2E1gCKF0kXkLgWplPoJeH96fHt49qqxC57EIR7bxySSjGljAwGSSKkxTrj17JJgIzAynCY2Io8ihHRCpPYVITzwsQq5iqhxv31OQSsf5PoMwDBMhBBYI2qpWKS4EZxhpuw7xaea4TZANf9jWfUkd6MxPuOmm7LjYmy5GDsuxkEb3C63DBcNOdYRd2qhxtXdLGKHNfuucx9pg7tadM3ymsNuKl1oiNU8lbNUzlWsAxSUFhGf_-nUC7Djdi7yAzugNR5N9CXYltNxVoyuStX-BpEM8qU |
| linkProvider | Springer Nature |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3dS9xAEB_sWbB9sFYrPWvtPrQvlcBudm-z-yhFUbweYm3xbcl-hUCNhzkP9K_vbi65KIhgn3cyhJnJzGx-8wHwlXItc-JFwrnDCeNplkisbcK99p4ZQVlqmmUT2WQiLi_lWdvHXXfV7h0k2XjqvtmNiFj7k0bwVgb1voJVFgJWrOM7__Wnc78hn272AhKekYRyzjoo8ykWj4NRn2EuQdGmlafyeVU8iDpH7_7rfTdgvU0y0cHCKt7Diqs24e2D0YNbUJ7n09KieVnfBsrgO8J9Nhyga4_q4JmTWDR5h3S7NLtGsQ0F1Q2EjSKagMqr4IlqFOvmC2Sdm6J2AUWBTMzIYwlSo_UP8Pvo8OLHcdKuXUgMHdFZuExiI4TzIRFguTGO0lyGyG4Y9ZpiL3keMvIsw9jlzDhiGZMpoXYkbcZ9_O2zDYPqunIfAY1GudaaOswDlcis9FoKKmy4pxDuBB0C7uSvTDuTPK7G-Kv6acpRiipIUUUpqnQI35ePTBcDOZ4j3u2Uqtpvs1YRayZxch8bwn6nuv74GWbfWlvoie19Ye4Kc2-VS3HaeES68yKuX2Dt-OLnWI1PJqef4E3ksqgV3IXB7ObWfYbXZj4r65u9xsz_Afh49Yk |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3da9RAEB_sKaU-aLUtnla7D_qihG6ye5vdR9EeiuUo9YO-LdmvEGjTo7krtH-9O7mkqSCF0uedDGFnMh-Zjx_AeyaMKtIgEyE8TbjI8kRR4xIRTAjcSsYz24JN5LOZPDlRRx3OadN3u_clydVMA25pqhf7cxf2h8G3VGIfUIaFXBVFvQaPOWIGYbr-809vimNs3WIEpiJPEyYE78ua_2Pxr2Maos2bAmk71lOHoi5veaDp8we_-yY864JP8nmlLS_gka9fwtNbKwm3oDou5pUjl1WzjJTRpsQ8Nx6Q80CaaLETbKa8IqYD024IjqeQpi1tE6wykOosWqiGYD99SZz3c9IBU5TEYqSOrUmtNmzD7-nBry_fkg6OIbFswhYxyaRWSh9igMALaz1jhYoe33IWDKNBiSJG6nlOqS-49anjXGUpcxPlchHwd9AOjOrz2r8CMpkUxhjmqYhUMncqGCWZdDF_SYWXbAy0l4W23a5yhMw41cOWZbxFHW9R4y3qbAwfbx6ZrxZ13EW82wtYd99so7EGneJGPz6GT70Yh-M7mH3o9GIgdtelvSrttdM-o1lrKdnre3Hdg_Wjr1N9-H324w1sIJNVC-EujBYXS_8WntjLRdVcvGs1_i-Ol_5t |
| 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=Rapid+visual+screening+of+soft-story+buildings+from+street+view+images+using+deep+learning+classification&rft.jtitle=Earthquake+Engineering+and+Engineering+Vibration&rft.au=Yu%2C+Qian&rft.au=Wang%2C+Chaofeng&rft.au=McKenna%2C+Frank&rft.au=Yu%2C+Stella+X.&rft.date=2020-10-01&rft.pub=Institute+of+Engineering+Mechanics%2C+China+Earthquake+Administration&rft.issn=1671-3664&rft.eissn=1993-503X&rft.volume=19&rft.issue=4&rft.spage=827&rft.epage=838&rft_id=info:doi/10.1007%2Fs11803-020-0598-2&rft.externalDocID=10_1007_s11803_020_0598_2 |
| thumbnail_s | http://cvtisr.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fdzgcygczd-e%2Fdzgcygczd-e.jpg |