中国安全科学学报 ›› 2017, Vol. 27 ›› Issue (1): 128-133.doi: 10.16265/j.cnki.issn1003-3033.2017.01.023

• 安全工程技术科学 • 上一篇    下一篇

基于“AHP+熵权法”的CW-TOPSIS冲击地压评判模型

朱峰, 张宏伟 教授   

  1. 辽宁工程技术大学 矿业学院,辽宁 阜新 123000
  • 收稿日期:2016-11-05 修回日期:2016-12-20 发布日期:2020-11-23
  • 作者简介:朱 峰 (1990—),男,山东邹城人,博士研究生,研究方向为矿井动力灾害防治技术。E-mail∶huafeng3966@126.com。
  • 基金资助:
    国家自然科学基金资助(51274117)。

"AHP+entropy weight method" based CW-TOPSIS model for predicting rockburst

ZHU Feng, ZHANG Hongwei   

  1. College of Mining Engineering, Liaoning Technical University, Fuxin Liaoning 123000, China)
  • Received:2016-11-05 Revised:2016-12-20 Published:2020-11-23

摘要: 针对冲击地压灾害评价系统模型中指标权重难以确定的问题,提出一种“AHP+熵权法”组合赋权方法。通过引入拉格朗日函数,建立优化决策模型,确保主客观权重和偏好系数间的一致性,进而获得各指标的组合权重(CW),并评价冲击地压预判影响因素的主次关系。基于逼近理想解排序法(TOPSIS)的基本理论,建立CW-TOPSIS冲击地压综合评判模型,分析贴近度,最终预测冲击地压等级。将此模型应用于老虎台矿83003工作面,得出该工作面有中等冲击危险,构造应力为诱发冲击的主导因素;基于“AHP+熵权法”的CW-TOPSIS冲击地压评判模型的预测结果与实际结果相吻合。

关键词: 冲击地压预测, 组合权重(CW), 偏好系数, CW-TOPSIS评判模型, 主次因素

Abstract: Seeing that proper weights are difficult to be attached to the indexes in the existing rockburst disaster assessment system, the paper was aimed at developing a combination weighting algorithm based on coupling AHP method with entropy weight method. To ensure the consistency between subjective-and-objective weights and preference coefficients, an optimization decision model was built by introducing Lagrange function, by which the proper CW of each index can be obtained and the factors influencing predicting rockburst can be ranked in order of influence . On the basis of the basic theory of TOPSIS, the CW-TOPSIS model for comprehensive evaluating rockburst was built for analyzing closeness degree of samples and predicting rockburst ranking level. The model was applied to the panel 83003 in Laohutai coal mine .The panel was predicted to have a medium risk of rockburst and the tectonic stress would be the leading factor causing the rockburst. The prediction conforms with the reality.

Key words: rockburst prediction, combined weights(CW), preference coefficient, CW-TOPSIS evaluation model, primary and secondary factors

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