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Assessing the rockburst risk for deep shafts via distance-based multi-criteria decision making approaches with hesitant fuzzy information
Abstract A rockburst is a severe and unexpected damaging seismic event caused by the violent release of accumulated strain energy. To assess the risk of rockbursting, three distance-based multi-criteria decision making (MCDM) approaches with hesitant fuzzy information are proposed. First, considering that each criterion has more than one possible value due to the anisotropic nature of rock masses, hesitant fuzzy sets (HFSs) are used to describe initial fuzzy evaluation information. To calculate comprehensive criteria weights, the traditional experts grading method and entropy weights model are extended with HFSs. Afterward, three distance-based MCDM methods are adopted to obtain the ranking orders of alternatives, and the final results and specific levels of rockburst risk are determined by the dominance theory. Finally, the proposed methodology is utilized to evaluate the rockburst risk for deep shafts in the Xincheng gold mine. The strengths of this methodology are demonstrated through comparison analysis. Results indicate that the evaluation of risk is consistent with observed field conditions, and the proposed methodology is feasible and effective for evaluating the risk of rockbursting in shafts. Based on these results, some implications for the management of rockburst risk are provided to guide the construction of deep shafts.
Highlights The hesitant fuzzy sets are adopted to describe initial assessment data of rockburst risk. The modified experts grading method and extended entropy weights model are integrated to obtain the criteria weights. Three distance-based MCDM approaches under hesitant fuzzy environment are combined to determine the evaluation results. The proposed methodology is applied to address the evaluation problem of rockburst risk for deep shafts.
Assessing the rockburst risk for deep shafts via distance-based multi-criteria decision making approaches with hesitant fuzzy information
Abstract A rockburst is a severe and unexpected damaging seismic event caused by the violent release of accumulated strain energy. To assess the risk of rockbursting, three distance-based multi-criteria decision making (MCDM) approaches with hesitant fuzzy information are proposed. First, considering that each criterion has more than one possible value due to the anisotropic nature of rock masses, hesitant fuzzy sets (HFSs) are used to describe initial fuzzy evaluation information. To calculate comprehensive criteria weights, the traditional experts grading method and entropy weights model are extended with HFSs. Afterward, three distance-based MCDM methods are adopted to obtain the ranking orders of alternatives, and the final results and specific levels of rockburst risk are determined by the dominance theory. Finally, the proposed methodology is utilized to evaluate the rockburst risk for deep shafts in the Xincheng gold mine. The strengths of this methodology are demonstrated through comparison analysis. Results indicate that the evaluation of risk is consistent with observed field conditions, and the proposed methodology is feasible and effective for evaluating the risk of rockbursting in shafts. Based on these results, some implications for the management of rockburst risk are provided to guide the construction of deep shafts.
Highlights The hesitant fuzzy sets are adopted to describe initial assessment data of rockburst risk. The modified experts grading method and extended entropy weights model are integrated to obtain the criteria weights. Three distance-based MCDM approaches under hesitant fuzzy environment are combined to determine the evaluation results. The proposed methodology is applied to address the evaluation problem of rockburst risk for deep shafts.
Assessing the rockburst risk for deep shafts via distance-based multi-criteria decision making approaches with hesitant fuzzy information
Liang, Weizhang (author) / Zhao, Guoyan (author) / Wang, Xi (author) / Zhao, Jie (author) / Ma, Chunde (author)
Engineering Geology ; 260
2019-06-22
Article (Journal)
Electronic Resource
English
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