{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T10:42:55Z","timestamp":1753872175507,"version":"3.41.2"},"reference-count":50,"publisher":"American Society of Civil Engineers (ASCE)","issue":"3","content-domain":{"domain":["ascelibrary.org"],"crossmark-restriction":true},"short-container-title":["J. Comput. Civ. Eng."],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1061\/jccee5.cpeng-6178","type":"journal-article","created":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T10:19:26Z","timestamp":1737109166000},"update-policy":"https:\/\/doi.org\/10.1061\/do.news.20190416.0001","source":"Crossref","is-referenced-by-count":1,"title":["Computer Vision-Based Intelligent Monitoring of Disruptions due to Construction Machinery Arrival Delay"],"prefix":"10.1061","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1675-2368","authenticated-orcid":true,"given":"Xuzhong","family":"Yan","sequence":"first","affiliation":[{"name":"Zhejiang Univ. of Technology","place":["China"]}]},{"given":"Rui","family":"Jin","sequence":"additional","affiliation":[{"name":"Zhejiang Construction Investment Group Co., Ltd.","place":["China"]}]},{"given":"Hong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhejiang Univ.","place":["China"]}]},{"given":"Hui","family":"Gao","sequence":"additional","affiliation":[{"name":"Zhejiang Univ. of Technology","place":["China"]}]},{"given":"Shuyuan","family":"Xu","sequence":"additional","affiliation":[{"name":"Zhejiang Sci-Tech Univ.","place":["China"]}]}],"member":"30","reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)CO.1943-7862.0001017"},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2023.104980"},{"key":"e_1_3_3_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2020.103308"},{"key":"e_1_3_3_5_1","doi-asserted-by":"publisher","DOI":"10.1023\/B:VISI.0000011205.11775.fd"},{"key":"e_1_3_3_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-020-05453-y"},{"key":"e_1_3_3_7_1","doi-asserted-by":"publisher","DOI":"10.1155\/2008\/246309"},{"key":"e_1_3_3_8_1","doi-asserted-by":"crossref","unstructured":"Bewley A. Z. Ge L. Ott F. Ramos and B. Upcroft. 2016. \u201cSimple online and realtime tracking.\u201d In Proc. 2016 IEEE Int. Conf. Image Processing (ICIP) 3464\u20133468. Phoenix: IEEE. https:\/\/doi.org\/10.1109\/icip.2016.7533003.","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"e_1_3_3_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2015.07.022"},{"key":"e_1_3_3_10_1","doi-asserted-by":"crossref","unstructured":"Bosser J. D. E. Sorstadius and M. H. Chehreghani. 2021. \u201cModel-centric and data-centric aspects of active learning for deep neural networks.\u201d In Proc. 2021 IEEE Int. Conf. Big Data 5053\u20135062. Orlando FL: IEEE. https:\/\/doi.org\/10.1109\/bigdata52589.2021.9671795.","DOI":"10.1109\/BigData52589.2021.9671795"},{"key":"e_1_3_3_11_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)CP.1943-5487.0000901"},{"key":"e_1_3_3_12_1","doi-asserted-by":"crossref","unstructured":"Cai Z. and N. Vasconcelos. 2018. \u201cCascade R-CNN: Delving into high quality object detection.\u201d In Proc. 2018 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 6154\u20136162. Salt Lake City: IEEE. https:\/\/doi.org\/10.1109\/cvpr.2018.00644.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"e_1_3_3_13_1","unstructured":"Changali S. A. Mohammad and M. Nieuwland. 2015. \u201cThe construction productivity imperative.\u201d Accessed February 25 2022. https:\/\/www.mckinsey.com\/business-functions\/operations\/our-insights\/the-construction-productivity-imperative."},{"key":"e_1_3_3_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2022.104702"},{"key":"e_1_3_3_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2021.101303"},{"key":"e_1_3_3_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2024.105389"},{"key":"e_1_3_3_17_1","doi-asserted-by":"crossref","unstructured":"Dai J. H. Qi Y. Xiong Y. Li G. Zhang H. Hu and Y. Wei. 2017. \u201cDeformable convolutional networks.\u201d In Proc. IEEE Int. Conf. on Computer Vision 764\u2013773. Venice Italy: IEEE. https:\/\/doi.org\/10.1109\/iccv.2017.89.","DOI":"10.1109\/ICCV.2017.89"},{"key":"e_1_3_3_18_1","unstructured":"Dendorfer P. H. Rezatofighi A. Milan J. Shi D. Cremers I. Reid S. Roth K. Schindler and L. Leal-Taixe. 2020. \u201cMOT20: A benchmark for multi object tracking in crowded scenes.\u201d Preprint submitted March 19 2020. http:\/\/arxiv.org\/abs\/2003.09003."},{"key":"e_1_3_3_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3240881"},{"key":"e_1_3_3_20_1","volume-title":"Deep learning","author":"Goodfellow I.","year":"2016","unstructured":"Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. Cambridge, MA: MIT Press."},{"key":"e_1_3_3_21_1","doi-asserted-by":"crossref","unstructured":"Hamid O. H. 2022. \u201cFrom model-centric to data-centric AI: A paradigm shift or rather a complementary approach?\u201d In Proc. 2022 8th Int. Conf. on Information Technology Trends (ITT) 196\u2013199. Dubai UAE: IEEE. https:\/\/doi.org\/10.1109\/itt56123.2022.9863935.","DOI":"10.1109\/ITT56123.2022.9863935"},{"key":"e_1_3_3_22_1","doi-asserted-by":"crossref","unstructured":"He K. G. Gkioxari P. Dollar and R. Girshick. 2017. \u201cMask R-CNN.\u201d In Proc. IEEE Int. Conf. on Computer Vision 2961\u20132969. Venice Italy: IEEE. https:\/\/doi.org\/10.1109\/iccv.2017.322.","DOI":"10.1109\/ICCV.2017.322"},{"key":"e_1_3_3_23_1","doi-asserted-by":"publisher","DOI":"10.1061\/JCCEE5.CPENG-5238"},{"key":"e_1_3_3_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2019.03.025"},{"key":"e_1_3_3_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2024.105504"},{"key":"e_1_3_3_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2019.01.018"},{"key":"e_1_3_3_27_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_3_3_28_1","doi-asserted-by":"crossref","unstructured":"Li Y. C. Huang and R. Nevatia. 2009. \u201cLearning to associate: Hybridboosted multi-target tracker for crowded scene.\u201d In Proc. 2009 Conf. on Computer Vision and Pattern Recognition (CVPR) 2953\u20132960. Miami FL: IEEE. https:\/\/doi.org\/10.1109\/cvpr.2009.5206735.","DOI":"10.1109\/CVPR.2009.5206735"},{"key":"e_1_3_3_29_1","doi-asserted-by":"publisher","DOI":"10.1061\/JCCEE5.CPENG-5460"},{"key":"e_1_3_3_30_1","doi-asserted-by":"crossref","unstructured":"Lin T.-Y. M. Maire S. Belongie J. Hays P. Perona D. Ramanan P. Dollar and C. L. Zitnick. 2014. \u201cMicrosoft COCO: Common objects in context.\u201d In Proc. Computer Vision\u2013ECCV 2014: 13th European Conf. 740\u2013755. Zurich Switzerland: Springer. https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"e_1_3_3_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2023.102131"},{"key":"e_1_3_3_32_1","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1007\/s11263-020-01375-2","article-title":"HOTA: A higher order metric for evaluating multi-object tracking","volume":"129","author":"Luiten J.","year":"2020","unstructured":"Luiten, J., A. Osep, P. Dendorfer, P. Torr, A. Geiger, L. Leal-Taixe, and B. Leibe. 2020. \u201cHOTA: A higher order metric for evaluating multi-object tracking.\u201d Int. J. Comput. Vis. 129 (Oct): 548\u2013578. https:\/\/doi.org\/10.1007\/s11263-020-01375-2.","journal-title":"Int. J. Comput. Vis."},{"key":"e_1_3_3_33_1","unstructured":"Milan A. L. Leal-Taixe I. Reid S. Roth and K. Schindler. 2016. \u201cMOT16: A benchmark for multi-object tracking.\u201d Preprint submitted March 6 2016. http:\/\/arxiv.org\/abs\/1603.00831."},{"key":"e_1_3_3_34_1","doi-asserted-by":"publisher","DOI":"10.1108\/JICES-04-2019-0044"},{"key":"e_1_3_3_35_1","doi-asserted-by":"crossref","unstructured":"Ristani E. F. Solera R. Zou R. Cucchiara and C. Tomasi. 2016. \u201cPerformance measures and a data set for multi-target multi-camera tracking.\u201d In Proc. European Conf. on Computer Vision (ECCV) 17\u201335. Amsterdam Netherlands: Springer. https:\/\/doi.org\/10.1007\/978-3-319-48881-3_2.","DOI":"10.1007\/978-3-319-48881-3_2"},{"key":"e_1_3_3_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2019.04.006"},{"key":"e_1_3_3_37_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)CO.1943-7862.0001843"},{"key":"e_1_3_3_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2021.103670"},{"key":"e_1_3_3_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2022.112271"},{"key":"e_1_3_3_40_1","doi-asserted-by":"crossref","unstructured":"Voigtlaender P. M. Krause A. Osep J. Luiten B. B. G. Sekar A. Geiger and B. Leibe. 2019. \u201cMOTS: Multi-object tracking and segmentation.\u201d In Proc. 2019\u2009\u2009IEEE\/CVF Conf. on Computer Vision and Pattern Recognition 7942\u20137951. Long Beach CA: IEEE. https:\/\/doi.org\/10.1109\/cvpr.2019.00813.","DOI":"10.1109\/CVPR.2019.00813"},{"key":"e_1_3_3_41_1","doi-asserted-by":"crossref","unstructured":"Wojke N. A. Bewley and D. Paulus. 2017. \u201cSimple online and realtime tracking with a deep association metric.\u201d In Proc. IEEE Int. Conf. on Image Processing (ICIP) 3645\u20133649. Beijing: IEEE. https:\/\/doi.org\/10.1109\/icip.2017.8296962.","DOI":"10.1109\/ICIP.2017.8296962"},{"key":"e_1_3_3_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2021.103721"},{"key":"e_1_3_3_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2022.104148"},{"key":"e_1_3_3_44_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2023.102102"},{"key":"e_1_3_3_45_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2017.11.001"},{"key":"e_1_3_3_46_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)CP.1943-5487.0000990"},{"key":"e_1_3_3_47_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2022.104491"},{"key":"e_1_3_3_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2023.105083"},{"issue":"3","key":"e_1_3_3_49_1","first-page":"1","article-title":"Intelligent monitoring and evaluation for the prefabricated construction schedule","volume":"38","author":"Yan X.","year":"2022","unstructured":"Yan, X., H. Zhang, and W. Zhang. 2022b. \u201cIntelligent monitoring and evaluation for the prefabricated construction schedule.\u201d Comput. Aided Civ. Inf. Eng. 38 (3): 1\u201317. https:\/\/doi.org\/10.1111\/mice.12838.","journal-title":"Comput. Aided Civ. Inf. Eng."},{"key":"e_1_3_3_50_1","doi-asserted-by":"publisher","DOI":"10.1061\/JCEMD4.COENG-12096"},{"key":"e_1_3_3_51_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)CO.1943-7862.0001629"}],"container-title":["Journal of Computing in Civil Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ascelibrary.org\/doi\/pdf\/10.1061\/JCCEE5.CPENG-6178","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T10:19:34Z","timestamp":1737109174000},"score":1,"resource":{"primary":{"URL":"https:\/\/ascelibrary.org\/doi\/10.1061\/JCCEE5.CPENG-6178"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5]]},"references-count":50,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["10.1061\/JCCEE5.CPENG-6178"],"URL":"https:\/\/doi.org\/10.1061\/jccee5.cpeng-6178","relation":{},"ISSN":["0887-3801","1943-5487"],"issn-type":[{"type":"print","value":"0887-3801"},{"type":"electronic","value":"1943-5487"}],"subject":[],"published":{"date-parts":[[2025,5]]},"assertion":[{"value":"2024-05-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-09-24","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-01-17","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"04025011"}}