{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T02:13:25Z","timestamp":1778120005594,"version":"3.51.4"},"reference-count":52,"publisher":"American Society of Civil Engineers (ASCE)","issue":"5","content-domain":{"domain":["ascelibrary.org"],"crossmark-restriction":true},"short-container-title":["J. Comput. Civ. Eng."],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1061\/jccee5.cpeng-6244","type":"journal-article","created":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T03:54:26Z","timestamp":1747972466000},"update-policy":"https:\/\/doi.org\/10.1061\/do.news.20190416.0001","source":"Crossref","is-referenced-by-count":2,"title":["An Integrated Real-Time Approach to Mitigate Noncompliant Behavior in Mobile Crane Lifting Operations"],"prefix":"10.1061","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9075-6511","authenticated-orcid":true,"given":"Shengyu","family":"Guo","sequence":"first","affiliation":[{"name":"China Univ. of Geosciences (Wuhan)","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gan","family":"Zhang","sequence":"additional","affiliation":[{"name":"China Univ. of Geosciences (Wuhan)","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bowen","family":"Han","sequence":"additional","affiliation":[{"name":"China Univ. of Geosciences (Wuhan)","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2072-1177","authenticated-orcid":true,"given":"Weili","family":"Fang","sequence":"additional","affiliation":[{"name":"Huazhong Univ. of Science and Technology","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"30","reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)CO.1943-7862.0001708"},{"key":"e_1_3_3_3_1","doi-asserted-by":"crossref","unstructured":"Awolusi I. C. Nnaji E. Marks and M. Hallowell. 2019. \u201cEnhancing construction safety monitoring through the application of internet of things and wearable sensing devices: A review.\u201d In Proc. Int. Conf. on Computing in Civil Engineering 2019 530\u2013538. Reston VA: ASCE. https:\/\/doi.org\/10.1061\/9780784482438.067.","DOI":"10.1061\/9780784482438.067"},{"key":"e_1_3_3_4_1","doi-asserted-by":"crossref","unstructured":"Carreira J. and A. Zisserman. 2017. \u201cQuo vadis action recognition? A new model and the kinetics dataset.\u201d In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2017 6299\u20136308. New York: IEEE.","DOI":"10.1109\/CVPR.2017.502"},{"key":"e_1_3_3_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2018.05.003"},{"key":"e_1_3_3_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2019.100980"},{"key":"e_1_3_3_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2018.12.005"},{"key":"e_1_3_3_8_1","doi-asserted-by":"publisher","DOI":"10.1080\/24705314.2018.1531348"},{"key":"e_1_3_3_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2016.08.025"},{"key":"e_1_3_3_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3307197"},{"key":"e_1_3_3_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2017.2766249"},{"key":"e_1_3_3_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/rob.21793"},{"key":"e_1_3_3_13_1","unstructured":"Japan Crane Association. 2021. \u201cStatus of occupational accidents involving cranes and mobile cranes.\u201d Accessed August 20 2023. https:\/\/cranenet.or.jp\/toukei\/graph_a.html."},{"key":"e_1_3_3_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2022.104257"},{"key":"e_1_3_3_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-41560-0_11"},{"key":"e_1_3_3_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/adfm.202005692"},{"key":"e_1_3_3_17_1","unstructured":"King R. A. 2012. \u201cAnalysis of crane and lifting accidents in North America from 2004 to 2010.\u201d M.S. thesis Dept. of Civil and Environmental Engineering Massachusetts Institute of Technology."},{"key":"e_1_3_3_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2020.3019769"},{"key":"e_1_3_3_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2012.05.002"},{"key":"e_1_3_3_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2904749"},{"key":"e_1_3_3_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2020.101100"},{"key":"e_1_3_3_22_1","doi-asserted-by":"crossref","unstructured":"Ma N. X. Zhang H.-T. Zheng and J. Sun. 2018. \u201cShuffleNet V2: Practical guidelines for efficient CNN architecture design.\u201d In Proc. European Conf. on Computer Vision (ECCV) 2018 116\u2013131. New York: Springer.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"e_1_3_3_23_1","doi-asserted-by":"publisher","DOI":"10.1061\/JCEMD4.COENG-12811"},{"key":"e_1_3_3_24_1","doi-asserted-by":"publisher","DOI":"10.1061\/JCEMD4.COENG-14458"},{"key":"e_1_3_3_25_1","doi-asserted-by":"crossref","unstructured":"Miao Q. Y. Li W. Ouyang Z. Ma X. Xu W. Shi and X. Cao. 2017. \u201cMultimodal gesture recognition based on the ResC3D network.\u201d In Proc. IEEE Int. Conf. on Computer Vision Workshops 2017 3047\u20133055. New York: IEEE.","DOI":"10.1109\/ICCVW.2017.360"},{"key":"e_1_3_3_26_1","unstructured":"National Oceanic and Atmospheric Administration. 2017. \u201cPressure altitude.\u201d Accessed August 17 2023. https:\/\/www.weather.gov\/media\/epz\/wxcalc\/pressureAltitude.pdf."},{"key":"e_1_3_3_27_1","volume-title":"Accident prevention manual for business & industry. Engineering & technology","author":"National Safety Council","year":"2015","unstructured":"National Safety Council. 2015. Accident prevention manual for business & industry. Engineering & technology. 14th ed. Edited by P. E. Hagan, J. F. Montgomery, and J. T. O. Reilly. Itasca, IL: National Safety Council.","edition":"14"},{"key":"e_1_3_3_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2021.103552"},{"key":"e_1_3_3_29_1","doi-asserted-by":"crossref","unstructured":"Rauscher F. S. Nann and O. Sawodny. 2018. \u201cMotion control of an overhead crane using a wireless hook mounted IMU.\u201d In Proc. 2018 Annual American Control Conf. (ACC) 5677\u20135682. New York: IEEE.","DOI":"10.23919\/ACC.2018.8431170"},{"key":"e_1_3_3_30_1","doi-asserted-by":"publisher","DOI":"10.1080\/15623599.2017.1382067"},{"key":"e_1_3_3_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssci.2016.08.027"},{"key":"e_1_3_3_32_1","unstructured":"State Administration for Market Regulation. 2023. \u201cAnnouncement on the safety status of special equipment in China.\u201d Accessed February 8 2023. https:\/\/www.samr.gov.cn\/."},{"key":"e_1_3_3_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2019.08.006"},{"key":"e_1_3_3_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2022.104217"},{"key":"e_1_3_3_35_1","unstructured":"TED The Economics Daily. 2019. \u201cCrane-related work deaths trended down from 1992 to 2017.\u201d Accessed August 20 2023. https:\/\/www.bls.gov\/opub\/ted\/2019\/crane-related-work-deaths-trended-down-from-1992-to-2017.htm#:\u223c:text=There%20were%2033%20crane%2Drelated to%2042%20deaths%20a%20year."},{"key":"e_1_3_3_36_1","doi-asserted-by":"crossref","unstructured":"Tran D. L. Bourdev R. Fergus L. Torresani and M. Paluri. 2015. \u201cLearning spatiotemporal features with 3D convolutional networks.\u201d In Proc. IEEE Int. Conf. on Computer Vision (ICCV) 4489\u20134497. New York: IEEE.","DOI":"10.1109\/ICCV.2015.510"},{"key":"e_1_3_3_37_1","unstructured":"Tran D. J. Ray Z. Shou S.-F. Chang and M. Paluri. 2017. \u201cConvNet architecture search for spatiotemporal feature learning.\u201d Preprint submitted August 16 2017. http:\/\/arxiv.org\/abs\/1708.05038."},{"key":"e_1_3_3_38_1","doi-asserted-by":"crossref","unstructured":"Tran D. H. Wang L. Torresani J. Ray Y. LeCun and M. Paluri. 2018. \u201cA closer look at spatiotemporal convolutions for action recognition.\u201d In Proc. IEEE Conf. on Computer Vision and Pattern Recognition 2018 6450\u20136459. New York: IEEE.","DOI":"10.1109\/CVPR.2018.00675"},{"key":"e_1_3_3_39_1","doi-asserted-by":"publisher","DOI":"10.3390\/s23104851"},{"key":"e_1_3_3_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2907222"},{"key":"e_1_3_3_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2021.103872"},{"key":"e_1_3_3_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2021.103625"},{"key":"e_1_3_3_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2022.104475"},{"key":"e_1_3_3_44_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2019.100981"},{"key":"e_1_3_3_45_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-51425-9"},{"key":"e_1_3_3_46_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)CP.1943-5487.0000242"},{"key":"e_1_3_3_47_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2018.09.008"},{"key":"e_1_3_3_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2014.07.008"},{"key":"e_1_3_3_49_1","first-page":"31","volume-title":"Advances in neural information processing systems","author":"Zhang L.","year":"2018","unstructured":"Zhang, L., G. Zhu, L. Mei, P. Shen, S. A. A. Shah, and M. Bennamoun. 2018. \u201cAttention in convolutional LSTM for gesture recognition.\u201d In Advances in neural information processing systems, 31. New Orleans, LA: NeurIPS Foundation."},{"key":"e_1_3_3_50_1","doi-asserted-by":"crossref","unstructured":"Zhang Y. and C. Harrison. 2015. \u201cTomo: Wearable Low-Cost Electrical Impedance Tomography for Hand Gesture Recognition.\u201d In Proc. 28th Annual ACM Symp. on User Interface Software & Technology 167\u2013173. New York: Association for Computing Machinery.","DOI":"10.1145\/2807442.2807480"},{"key":"e_1_3_3_51_1","doi-asserted-by":"crossref","unstructured":"Zhao T. J. Liu Y. Wang H. Liu and Y. Chen. 2018. \u201cPPG-based finger-level gesture recognition leveraging wearables.\u201d In Proc. IEEE INFOCOM 2018\u2014IEEE Conf. on Computer Communications 1457\u20131465. New York: IEEE.","DOI":"10.1109\/INFOCOM.2018.8486006"},{"key":"e_1_3_3_52_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2018.10.017"},{"key":"e_1_3_3_53_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssci.2018.05.001"}],"container-title":["Journal of Computing in Civil Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ascelibrary.org\/doi\/pdf\/10.1061\/JCCEE5.CPENG-6244","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T03:54:36Z","timestamp":1747972476000},"score":1,"resource":{"primary":{"URL":"https:\/\/ascelibrary.org\/doi\/10.1061\/JCCEE5.CPENG-6244"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":52,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["10.1061\/JCCEE5.CPENG-6244"],"URL":"https:\/\/doi.org\/10.1061\/jccee5.cpeng-6244","relation":{},"ISSN":["0887-3801","1943-5487"],"issn-type":[{"value":"0887-3801","type":"print"},{"value":"1943-5487","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9]]},"assertion":[{"value":"2024-06-11","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-23","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-05-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"05025005"}}