{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,6]],"date-time":"2022-04-06T00:02:01Z","timestamp":1649203321803},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T00:00:00Z","timestamp":1635465600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,10,29]]},"abstract":"<jats:p>With the rapid development of underground engineering in China, more metro tunnels are being constructed, the mileage of subway tunnels is increasing, and the corresponding problems of tunnel structure diseases are becoming more prominent. At present, the treatment of tunnel structural diseases mainly relies on manual inspection and identification, and research on defects prediction is still lacking. Because of the complexity of the factors affecting tunnel structure diseases, it is difficult to analyze the causes and development trend of the diseases comprehensively by manual analysis. Fortunately, machine learning methods have gained popularity in classification and regression tasks in recent decades. Many algorithms, such as decision tree algorithms, the random forest algorithm, and XGBoost, have been applied in fields including finance, engineering, and transportation. This study aimed to analyze the prediction effect of machine learning models by feeding 68055 segment lining rings of six subway lines in a city. According to the disease records from 2014 to 2016 and the corresponding convergence and characteristic data, defect conditions in 2017 were predicted and compared with real defect conditions in 2017. The accuracy rates and F1 values of the predicted results were all above 80%. The prediction results can help tunnel maintenance departments and relevant government regulators make auxiliary decisions to control tunnel structure diseases, and can help them focus on the tunnel interval of severe diseases to clarify the development trend of tunnel disease.<\/jats:p>","DOI":"10.3233\/faia210263","type":"book-chapter","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T19:09:22Z","timestamp":1635880162000},"source":"Crossref","is-referenced-by-count":0,"title":["Research on and Application of Tunnel Structure Defects Prediction Using Machine Learning Methods"],"prefix":"10.3233","author":[{"given":"Bo","family":"Shi","sequence":"first","affiliation":[{"name":"SGIDI Engineering Consulting (Group) Co., Ltd, Shanghai 200093, China"},{"name":"School of Computer Science and Technology, Fudan University, Shanghai 2000438, China"},{"name":"Shanghai Engineering Research Center of Geotechnical Test for Underground Space, Shanghai 200093, China"},{"name":"Shanghai Professional Technology Service Platform of Geotechnical Engineering, Shanghai 200093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Su","sequence":"additional","affiliation":[{"name":"SGIDI Engineering Consulting (Group) Co., Ltd, Shanghai 200093, China"},{"name":"Shanghai Engineering Research Center of Geotechnical Test for Underground Space, Shanghai 200093, China"},{"name":"Shanghai Professional Technology Service Platform of Geotechnical Engineering, Shanghai 200093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Du","sequence":"additional","affiliation":[{"name":"SGIDI Engineering Consulting (Group) Co., Ltd, Shanghai 200093, China"},{"name":"Shanghai Engineering Research Center of Geotechnical Test for Underground Space, Shanghai 200093, China"},{"name":"Shanghai Professional Technology Service Platform of Geotechnical Engineering, Shanghai 200093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bao","family":"Jiao","sequence":"additional","affiliation":[{"name":"SGIDI Engineering Consulting (Group) Co., Ltd, Shanghai 200093, China"},{"name":"Shanghai Engineering Research Center of Geotechnical Test for Underground Space, Shanghai 200093, China"},{"name":"Shanghai Professional Technology Service Platform of Geotechnical Engineering, Shanghai 200093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Wang","sequence":"additional","affiliation":[{"name":"SGIDI Engineering Consulting (Group) Co., Ltd, Shanghai 200093, China"},{"name":"Shanghai Engineering Research Center of Geotechnical Test for Underground Space, Shanghai 200093, China"},{"name":"Shanghai Professional Technology Service Platform of Geotechnical Engineering, Shanghai 200093, China"},{"name":"Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Modern Management based on Big Data II and Machine Learning and Intelligent Systems III"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA210263","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T19:09:28Z","timestamp":1635880168000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA210263"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,29]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia210263","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,29]]}}}