{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T07:40:06Z","timestamp":1765438806755,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031133206"},{"type":"electronic","value":"9783031133213"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-13321-3_24","type":"book-chapter","created":{"date-parts":[[2022,8,6]],"date-time":"2022-08-06T17:03:55Z","timestamp":1659805435000},"page":"269-279","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Deep Learning Approaches for\u00a0Image-Based Detection and\u00a0Classification of\u00a0Structural Defects in\u00a0Bridges"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3313-4817","authenticated-orcid":false,"given":"Angelo","family":"Cardellicchio","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5119-8967","authenticated-orcid":false,"given":"Sergio","family":"Ruggieri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8133-6830","authenticated-orcid":false,"given":"Andrea","family":"Nettis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8624-5444","authenticated-orcid":false,"given":"Cosimo","family":"Patruno","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6408-167X","authenticated-orcid":false,"given":"Giuseppina","family":"Uva","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1830-4961","authenticated-orcid":false,"given":"Vito","family":"Ren\u00f2","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,7]]},"reference":[{"unstructured":"Ministero delle Infrastrutture e dei Trasporti. Linee Guida per la Classificazione e Gestione del Rischio, la Valutazione della Sicurezza ed il Monitoraggio dei Ponti Esistenti (2020). (in Italian)","key":"24_CR1"},{"issue":"4","key":"24_CR2","doi-asserted-by":"publisher","first-page":"1769","DOI":"10.1177\/8755293020919419","volume":"36","author":"Y Xie","year":"2020","unstructured":"Xie, Y., Ebad Sichani, M., Padgett, J.E., DesRoches, R.: The promise of implementing machine learning in earthquake engineering: a state-of-the-art review. Earthq. Spectra 36(4), 1769\u20131801 (2020). https:\/\/doi.org\/10.1177\/8755293020919419","journal-title":"Earthq. Spectra"},{"key":"24_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jobe.2020.101816","volume":"33","author":"H Sun","year":"2020","unstructured":"Sun, H., Burton, H.V., Huang, H.: Machine learning applications for building structural design and performance assessment: state-of-the-art review. J. Build. Eng. 33, 101816 (2020). https:\/\/doi.org\/10.1016\/j.jobe.2020.101816","journal-title":"J. Build. Eng."},{"key":"24_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2021.103936","volume":"132","author":"S Ruggieri","year":"2021","unstructured":"Ruggieri, S., Cardellicchio, A., Leggieri, V., Uva, G.: Machine-learning based vulnerability analysis of existing buildings. Autom. Constr. 132, 103936 (2021). https:\/\/doi.org\/10.1016\/j.autcon.2021.103936","journal-title":"Autom. Constr."},{"issue":"1","key":"24_CR5","doi-asserted-by":"publisher","first-page":"4","DOI":"10.3390\/data7010004","volume":"7","author":"A Cardellicchio","year":"2022","unstructured":"Cardellicchio, A., Ruggieri, S., Leggieri, V., Uva, G.: View VULMA: data set for training a machine-learning tool for a fast vulnerability analysis of existing buildings. Data. 7(1), 4 (2022). https:\/\/doi.org\/10.3390\/data7010004","journal-title":"Data."},{"key":"24_CR6","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1111\/mice.12263","volume":"32","author":"YJ Cha","year":"2017","unstructured":"Cha, Y.J., Choi, W., B\u00fcy\u00fck\u00f6zt\u00fcrk, O.: Deep learning-based crack damage detection using convolutional neural networks. Comput. Civ. Infrastruct. Eng. 32, 361\u2013378 (2017). https:\/\/doi.org\/10.1111\/mice.12263","journal-title":"Comput. Civ. Infrastruct. Eng."},{"key":"24_CR7","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1111\/mice.12334","volume":"33","author":"YJ Cha","year":"2018","unstructured":"Cha, Y.J., Choi, W., Suh, G., Mahmoudkhani, S., B\u00fcy\u00fck\u00f6zt\u00fcrk, O.: Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types. Comput. Civ. Infrastruct. Eng. 33, 731\u201347 (2018). https:\/\/doi.org\/10.1111\/mice.12334","journal-title":"Comput. Civ. Infrastruct. Eng."},{"issue":"7","key":"24_CR8","doi-asserted-by":"publisher","first-page":"1037","DOI":"10.1080\/15732479.2019.1680709","volume":"16","author":"J Zhu","year":"2020","unstructured":"Zhu, J., Zhang, C., Qi, H., Lu, Z.: Vision-based defects detection for bridges using transfer learning and convolutional neural networks. Struct. Infrastruct. Eng. 16(7), 1037\u20131049 (2020). https:\/\/doi.org\/10.1080\/15732479.2019.1680709","journal-title":"Struct. Infrastruct. Eng."},{"issue":"10","key":"24_CR9","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1111\/mice.12297","volume":"32","author":"A Zhang","year":"2017","unstructured":"Zhang, A., et al.: Automated pixel-level pavement crack detection on 3D asphalt surfaces using a deep-learning network. Comput. Aided Civ. Infrastruct. Eng. 32(10), 805\u2013819 (2017). https:\/\/doi.org\/10.1111\/mice.12297","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"issue":"12","key":"24_CR10","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.1111\/mice.12412","volume":"33","author":"X Yang","year":"2018","unstructured":"Yang, X., Li, H., Yu, Y., Luo, X., Huang, T., Yang, X.: Automatic pixel-level crack detection and measurement using fully convolutional network. Comput. Aided Civ. Infrastruct. Eng. 33(12), 1090\u20131109 (2018). https:\/\/doi.org\/10.1111\/mice.12412","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"unstructured":"Yang, L., Li, B., Li, W., Liu, Z., Yang, G., Xiao, J.: Deep concrete inspection using unmanned aerial vehicle towards CSSC database. In: Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (pp. 24\u201328, September 2017","key":"24_CR11"},{"key":"24_CR12","first-page":"91","volume":"28","author":"S Ren","year":"2015","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst. 28, 91\u201399 (2015)","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"6","key":"24_CR13","doi-asserted-by":"publisher","first-page":"1881","DOI":"10.3390\/s18061881","volume":"18","author":"I Kim","year":"2018","unstructured":"Kim, I., Jeon, H., Baek, S., Hong, W., Jung, H.: Application of crack identification techniques for an aging concrete bridge inspection using an unmanned aerial vehicle. Sensors 18(6), 1881 (2018). https:\/\/doi.org\/10.3390\/s18061881","journal-title":"Sensors"},{"issue":"7","key":"24_CR14","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1111\/mice.12351","volume":"33","author":"R Li","year":"2018","unstructured":"Li, R., Yuan, Y., Zhang, W., Yuan, Y.: Unified vision-based methodology for simultaneous concrete defect detection and geolocalization. Comput. Aided Civ. Infrastruct. Eng. 33(7), 527\u2013544 (2018). https:\/\/doi.org\/10.1111\/mice.12351","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"24_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"W Liu","year":"2016","unstructured":"Liu, W., et al.: SSD: single shot MultiBox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016, Part I. LNCS, vol. 9905, pp. 21\u201337. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2"},{"doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.91","key":"24_CR16","DOI":"10.1109\/CVPR.2016.91"},{"issue":"12","key":"24_CR17","doi-asserted-by":"publisher","first-page":"1127","DOI":"10.1111\/mice.12387","volume":"33","author":"H Maeda","year":"2018","unstructured":"Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., Omata, H.: Road damage detection and classification using deep neural networks with smartphone images. Comput. Aided Civ. Infrastruct. Eng. 33(12), 1127\u20131141 (2018). https:\/\/doi.org\/10.1111\/mice.12387","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"unstructured":"https:\/\/github.com\/ultralytics\/yolov5. Accessed 23 Mar 2022","key":"24_CR18"}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Processing. ICIAP 2022 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-13321-3_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,6]],"date-time":"2022-08-06T17:06:27Z","timestamp":1659805587000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-13321-3_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031133206","9783031133213"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-13321-3_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"7 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lecce","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 May 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 May 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciap2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iciap2021.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"307","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"168","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"55% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}