{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T17:33:36Z","timestamp":1769708016071,"version":"3.49.0"},"reference-count":30,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,8,1]]},"abstract":"<jats:p>Roof collapse is the most frequent production accident in the mine production process, which seriously threatens the efficient and safe production of the mine. Therefore, it is urgent to carry out practical research on the roof collapse tendency of the roadway. After searching and analyzing the relevant documents, the primary influencing factors of roof collapse risk based on AHP are determined, namely engineering geology, rock mass support, construction management and natural environment. After refining the main influencing factors, the evaluation factor set is obtained, the fuzzy comprehensive evaluation relationship matrix is established, and the fuzzy comprehensive evaluation model of roof collapse risk is obtained. Finally, the quantitative evaluation of no collapse risk, weak collapse risk, medium collapse risk and high collapse risk is carried out. Taking a metal mine as an example, the risk of roof collapse of its C11 haulage roadway is selected for fuzzy evaluation. The evaluation result is high collapse risk, which is consistent with the evaluation result of the current specification, indicating that the model can be used for mine roof collapse risk evaluation. This method of estimating roof collapse has been applied on-site, which is consistent with the actual situation and has achieved good results. It has guiding significance for predicting the stability of tunnels and supporting operations.<\/jats:p>","DOI":"10.3233\/jifs-224146","type":"journal-article","created":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T13:23:59Z","timestamp":1685712239000},"page":"2753-2762","source":"Crossref","is-referenced-by-count":0,"title":["A multi-level analysis method for evaluating the risk of roof caving and its application"],"prefix":"10.1177","volume":"45","author":[{"given":"Jun","family":"Yang","sequence":"first","affiliation":[{"name":"State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining & Technology (Beijing), Beijing, China"}]},{"given":"Linke","family":"Qiao","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining & Technology (Beijing), Beijing, China"}]},{"given":"Changjiang","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining & Technology (Beijing), Beijing, China"}]},{"given":"Xing","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining & Technology (Beijing), Beijing, China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-224146_ref1","doi-asserted-by":"crossref","first-page":"105752.2","DOI":"10.1016\/j.enggeo.2020.105752","article-title":"A failure probability evaluation method for collapse of drill-and-blast tunnels based on multistate fuzzy Bayesian network","volume":"276","author":"Zhang","year":"2020","journal-title":"Engineering Geology"},{"key":"10.3233\/JIFS-224146_ref2","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.enggeo.2016.02.009","article-title":"Application of time series analysis and PSO\u2013SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China","volume":"204","author":"Zhou","year":"2016","journal-title":"Engineering Geology"},{"issue":"1","key":"10.3233\/JIFS-224146_ref3","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1061\/(ASCE)0887-3801(2002)16:1(59)","article-title":"Neural network forecast model in deep excavation","volume":"16","author":"Jan","year":"2002","journal-title":"Journal of Computing in Civil Engineering"},{"key":"10.3233\/JIFS-224146_ref4","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.tust.2019.02.012","article-title":"Intelligent information-based construction in tunnel engineering based on the GA and CCGPR coupled algorithm","volume":"88","author":"Liu","year":"2019","journal-title":"Tunnelling and Underground Space Technology"},{"key":"10.3233\/JIFS-224146_ref5","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.inffus.2020.02.003","article-title":"Multi-classifier Information Fusion in risk analysis","volume":"60","author":"Pan","year":"2020","journal-title":"Information Fusion"},{"key":"10.3233\/JIFS-224146_ref6","doi-asserted-by":"crossref","first-page":"103948","DOI":"10.1016\/j.tust.2021.103948","article-title":"Multi-sources Information Fusion analysis of water inrush disaster in tunnels based on improved theory of evidence","volume":"113","author":"Li","year":"2021","journal-title":"Tunnelling and Underground Space Technology"},{"key":"10.3233\/JIFS-224146_ref7","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.autcon.2016.09.003","article-title":"Perceiving safety risk of buildings adjacent to tunneling excavation: An Information Fusion approach","volume":"73","author":"Zhang","year":"2017","journal-title":"Automation in Construction"},{"issue":"6","key":"10.3233\/JIFS-224146_ref8","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1016\/j.ins.2009.11.023","article-title":"MGRS. 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