{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:28:49Z","timestamp":1740148129084,"version":"3.37.3"},"reference-count":57,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,1,3]],"date-time":"2022-01-03T00:00:00Z","timestamp":1641168000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52079073","2020KSD10","2021SSPY088"],"award-info":[{"award-number":["52079073","2020KSD10","2021SSPY088"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Foundation of the Hubei Key Laboratory of Construction and Management in Hydropower Engineering","award":["52079073","2020KSD10","2021SSPY088"],"award-info":[{"award-number":["52079073","2020KSD10","2021SSPY088"]}]},{"DOI":"10.13039\/501100002861","name":"China Three Gorges University","doi-asserted-by":"publisher","award":["52079073","2020KSD10","2021SSPY088"],"award-info":[{"award-number":["52079073","2020KSD10","2021SSPY088"]}],"id":[{"id":"10.13039\/501100002861","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2022,1,3]]},"abstract":"<jats:p>Accidents of various types in the construction of hydropower engineering projects occur frequently, which leads to significant numbers of casualties and economic losses. Identifying and eliminating near misses are a significant means of preventing accidents. Mining near-miss data can provide valuable information on how to mitigate and control hazards. However, most of the data generated in the construction of hydropower engineering projects are semi-structured text data without unified standard expression, so data association analysis is time-consuming and labor-intensive. Thus, an artificial intelligence (AI) automatic classification method based on a convolutional neural network (CNN) is adopted to obtain structured data on near-miss locations and near-miss types from safety records. The apriori algorithm is used to further mine the associations between \u201clocations\u201d and \u201ctypes\u201d by scanning structured data. The association results are visualized using a network diagram. A Sankey diagram is used to reveal the information flow of near-miss specific objects using the \u201clocation\u2009\u27f6\u2009type\u201d strong association rule. The proposed method combines text classification, association rules, and the Sankey diagrams and provides a novel approach for mining semi-structured text. Moreover, the method is proven to be useful and efficient for exploring near-miss distribution laws in hydropower engineering construction to reduce the possibility of accidents and efficiently improve the safety level of hydropower engineering construction sites.<\/jats:p>","DOI":"10.1155\/2022\/4851615","type":"journal-article","created":{"date-parts":[[2022,1,3]],"date-time":"2022-01-03T21:20:15Z","timestamp":1641244815000},"page":"1-16","source":"Crossref","is-referenced-by-count":1,"title":["Association Mining of Near Misses in Hydropower Engineering Construction Based on Convolutional Neural Network Text Classification"],"prefix":"10.1155","volume":"2022","author":[{"given":"Shu","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Engineering Management, College of Hydraulic and Environmental Engineering, China Three Gorges University, Yichang, Hubei 443002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junbo","family":"Xi","sequence":"additional","affiliation":[{"name":"Department of Engineering Management, College of Economics and Management, China Three Gorges University, Yichang, Hubei 443002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3706-9883","authenticated-orcid":true,"given":"Yun","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Engineering Management, College of Hydraulic and Environmental Engineering, China Three Gorges University, Yichang, Hubei 443002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinfan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Engineering Management, College of Hydraulic and Environmental Engineering, China Three Gorges University, Yichang, Hubei 443002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1080\/01446193.2013.816435"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssci.2013.12.008"},{"year":"2020","author":"National Energy Administration (China)","key":"3"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2009.11.017"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1061\/(asce)co.1943-7862.0001505"},{"issue":"23","key":"6","doi-asserted-by":"crossref","first-page":"8631","DOI":"10.3390\/app10238631","article-title":"Comparison of deep learning models and various text pre-processing techniques for the toxic comments classification","volume":"10","author":"V. Maslej-Kre\u0161\u0148\u00e1kov\u00e1","year":"2020","journal-title":"Applied Sciences"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1061\/9780784480847.051"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1061\/41182(416)16"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2019.02.009"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2020.101060"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.3390\/app10175754"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1080\/17509653.2015.1100525"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1080\/17445647.2018.1473815"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.3390\/app10217913"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/7908737"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.3846\/jcem.2018.1647"},{"key":"17","first-page":"2493","article-title":"Natural language processing (almost) from scratch","volume":"12","author":"R. Collobert","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.3390\/app10175841"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2020.010172"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1145\/505282.505283"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsr.2012.10.012"},{"key":"22","first-page":"1461","article-title":"Application of text mining techniques for classification of documents: a study of automation of complaints screening in a Brazilian federal agency","volume":"38","author":"P. Maia","year":"2014","journal-title":"Solid-State Electronics"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1109\/access.2018.2814818"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2018.11.004"},{"key":"25","doi-asserted-by":"publisher","DOI":"10.1007\/s41064-020-00129-6"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2020.102373"},{"key":"27","doi-asserted-by":"publisher","DOI":"10.1109\/tsc.2016.2622697"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-019-0557-y"},{"key":"29","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2018.05.001"},{"key":"30","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.10.003"},{"article-title":"Convolutional neural networks for sentence classification","year":"2014","author":"Y. Kim","key":"31"},{"key":"32","doi-asserted-by":"publisher","DOI":"10.1145\/170035.170072"},{"key":"33","doi-asserted-by":"publisher","DOI":"10.1016\/j.jprocont.2015.04.005"},{"key":"34","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssci.2009.12.005"},{"key":"35","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2018.09.091"},{"issue":"05","key":"36","first-page":"1625","article-title":"Knowledge discovery of construction safety accidents based on concept lattice","volume":"19","author":"G. Qiu","year":"2019","journal-title":"Journal of Safety and Environment"},{"issue":"01","key":"37","first-page":"14","article-title":"Task-driven mining of association rules for hazard sources in construction sites","volume":"19","author":"Z. Mingyuan","year":"2019","journal-title":"Journal of Safety and Environment"},{"key":"38","doi-asserted-by":"publisher","DOI":"10.3390\/jmmp4040097"},{"key":"39","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2014.08.070"},{"key":"40","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2017.09.012"},{"key":"41","doi-asserted-by":"publisher","DOI":"10.1111\/jiec.12700"},{"key":"42","doi-asserted-by":"publisher","DOI":"10.1080\/09613218.2017.1294419"},{"key":"43","doi-asserted-by":"publisher","DOI":"10.18494\/sam.2020.2794"},{"volume-title":"Classification Standard for Casualty Accidents of Enterprise Workers","year":"1986","author":"NBS (National Bureau of Standards)","key":"44"},{"article-title":"Efficient estimation of word representations in vector space","year":"2013","author":"T. Mikolov","key":"45"},{"key":"46","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.09.096"},{"key":"47","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2016.05.045"},{"key":"48","doi-asserted-by":"publisher","DOI":"10.1177\/0020294019865750"},{"key":"49","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2012.230"},{"key":"50","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2020.06.002"},{"key":"51","doi-asserted-by":"publisher","DOI":"10.1002\/(sici)1097-4571(199401)45:1<12::aid-asi2>3.0.co;2-l"},{"issue":"7","key":"52","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1080\/08839510600779688","article-title":"APRIORI-SD: adapting association rule learning to subgroup discovery","volume":"20","author":"B. Kavsek","year":"2006","journal-title":"Applied Artificial Intelligence"},{"key":"53","doi-asserted-by":"publisher","DOI":"10.1007\/s11192-018-2905-5"},{"key":"54","doi-asserted-by":"publisher","DOI":"10.3390\/en10030406"},{"key":"55","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-013-0225-5"},{"key":"56","doi-asserted-by":"publisher","DOI":"10.1142\/s2424922x20500084"},{"key":"57","doi-asserted-by":"publisher","DOI":"10.1109\/tip.2016.2547588"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2022\/4851615.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2022\/4851615.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2022\/4851615.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,3]],"date-time":"2022-01-03T21:20:23Z","timestamp":1641244823000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/cin\/2022\/4851615\/"}},"subtitle":[],"editor":[{"given":"Huihua","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2022,1,3]]},"references-count":57,"alternative-id":["4851615","4851615"],"URL":"https:\/\/doi.org\/10.1155\/2022\/4851615","relation":{},"ISSN":["1687-5273","1687-5265"],"issn-type":[{"type":"electronic","value":"1687-5273"},{"type":"print","value":"1687-5265"}],"subject":[],"published":{"date-parts":[[2022,1,3]]}}}