深埋地下工程岩爆烈度分级预测的RS-功效系数模型

来源期刊:中南大学学报(自然科学版)2014年第6期

论文作者:王迎超 靖洪文 吉咸伟 牟泰安 张成林

文章页码:1992 - 1998

关键词:地下工程;岩爆;粗糙集;功效系数;分级预测

Key words:underground engineering; rockburst; rough set; efficacy coefficient method; classification and prediction

摘    要:岩爆是深埋地下工程施工过程中常见的动力破坏现象,易造成重大经济损失和人员伤亡,对其进行有效预测预报一直是地下工程界迫切需要解决的难题。在系统分析影响岩爆发生的相关因素的基础上,选取切向应力与岩石单轴抗压强度比sq/sc、岩石单轴抗压强度与抗拉强度比sc/st、弹性变形能指数Wet和岩石脆性指数Is建立评价指标体系,并基于粗糙集理论,将各指标的权重问题转换为由现场实测数据来求解各指标属性重要性问题。然后,基于社会经济学中广泛应用的功效系数法,针对各指标对岩爆的影响特征,构建其功效函数,最终建立岩爆烈度分级预测的RS-功效系数模型,对岩爆发生可能性及其烈度进行预测预报。运用所建模型对国内外一些典型地下工程岩爆实例进行分析计算,并与集对分析模型、物元可拓模型评判结果对比分析。研究表明:RS-功效系数模型评价结果可靠、精度较高且现场可操作性强,具有良好的工程应用前景。

Abstract: Rockburst is a common phenomenon of dynamic failures for rock mass during construction of deep underground engineering, which directly leads to huge economic losses and casualties. The classification and prediction of rock burst intensity is a global problem of underground engineering. Based on the systematic analysis on influence factors of rockburst, four factors were selected as evaluation indices, including sq/sc , sc/st , Wet and Is. Rough set was adopted to determine the weighting coefficient for each evaluation index, which was then converted into the important problems for calculating attributes of each index with field measurement data. With the consideration of the influence characteristics for each index on rockburst, efficacy function was proposed for each index, which was widely used in social economics. Then, a new model for classification and prediction on rockburst was established based on rough set and efficacy coefficient method. Furthermore, the model was tested by a series of rock projects at home and abroad. The obtained results show good agreement with the results separately from set pair analysis method and extensional method. It is found that the new model of classification on the rockburst intensity is reasonable and effective. The rough set and efficacy coefficient method is simple and easily maneuverable, which has vast prospect in rockburst prediction.

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