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作 者:王吉亮[1,2] 陈剑平[1] 杨静[1,3] 阙金声[4]
机构地区:[1]吉林大学建设工程学院,长春130026 [2]长江三峡勘测研究院有限公司(武汉)岩土工程设计处,武汉430074 [3]长江三峡勘测研究院有限公司(武汉)计算机与信息中心,武汉430074 [4]北京国电华北电力工程有限公司,北京100120
出 处:《岩土力学》2009年第7期2203-2208,共6页Rock and Soil Mechanics
基 金:国家自然科学基金资助项目(No40472136);教育部资助优秀年轻教师基金资助项目(No120413133);吉林大学"985"计划资助项目(No105213200500007)资助
摘 要:将距离判别分析方法应用于岩爆等级判定问题。选用洞室围岩最大的切向应力σθ、岩石单轴抗压强度σc、抗拉强度σt、岩石弹性能量指数Wet作为岩爆等级判定的距离判别分析模型判别因子,以工程中实际岩爆情况及数据作为训练样本,进行分析计算,建立岩爆等级判定的距离判别分析模型。运用该分析模型对国内外工程实际岩爆情况进行判定,判别结果与工程实际完全相符。将该模型应用到诸(暨)永(嘉)高速公路括苍山隧道工程的岩爆情况预测中,判别结果与实际情况相符。研究表明,岩爆等级判定的距离判别分析方法,判别能力强,误判率低,是解决岩爆等级判定的一条有效途径。Rockburst is one of the main engineering geological problems greatly threatening the safety of construction. Prediction of rockburst is always an important project concerning the safety of workers and equipments in tunnels. The method of discriminant analysis is used to classify whether rockburst will happen in the underground rock projects and how much the intensity of rockburst is. Some main control factors of rockburst, such as the values of in-situ stresses σθ, uniaxial compressive strength σc and tensile strength at of rock, the elastic energy index of rock Wet, are chosen in the analysis. Linear discriminant functions and criterion are obtained through training a large set of rockburst samples which come from a series of underground rock projects in domestic and abroad. Another samples are evaluated with the model. The evaluated results agree well with the practical records. Comparing the results of support vector machine method, fuzzy comprehensive evaluation method and artificial neural network method with discriminant analysis method, the evaluating results are same. Misjudgment ratio is low. Applying the classification model to rockburst prediction of the project of Kuocangshan Tunnel in Zhuyong Expressway, the result of classification is same as the results of fuzzy comprehensive evaluation method, artificial neural network method and numerical modeling method, just same as what happened in scene. It is shown that the classification model of discriminant analysis which with a low misjudgment ratio performed excellently with high prediction accuracy is an effective path to solve the problem of rockburst grade classification.
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