地下工程围岩稳定性分类的突变级数法研究

来源期刊:中南大学学报(自然科学版)2008年第5期

论文作者:宫凤强 李夕兵 高科

文章页码:1081 - 1081

关键词:突变级数法;地下工程;围岩;稳定性分类

Key words:catastrophe progression method; underground engineering; surrounding rocks; stability classification

摘    要:提出基于突变级数理论的地下工程围岩稳定性分类的评价方法。根据突变级数理论,对影响地下工程围岩稳定性的因素(包括岩石质量指标、岩石单轴饱和抗压强度、完整性系数、结构面强度系数和地下水渗水量)进行多层次分解,对数据进行归一化处理,结合突变理论与模糊数学理论,进行综合量化运算,得到最后的评价类别。利用广州抽水蓄能电站一期工程的12个实测数据和插值法得到的18个学习样本,建立地下工程围岩稳定性分级的突变级数模型,并用于电站二期工程22处围岩的稳定性分类中。研究结果表明,突变级数法的判别结果比较符合实际,并与人工神经网络法、支持向量机法判别结果较一致,为地下工程围岩稳定性分类提供了一种新方法。

Abstract: Based on the catastrophe progression theory, an integrative evaluation method for the stability classification of surrounding rock was presented. Five control factors, i.e. rock quality designation, uniaxial compressive strengthen, integrality coefficient, strengthen coefficient of structural plane and seepage measurement of groundwater,were selected in the analysis of multi-level objective decomposition. According to the catastrophe theory and the fuzzy member function, the data were normalized and the catastrophic affiliated functional value was calculated, then a catastrophe progression model was obtained to classify the stability classification of surrounding rock. 12 samples of the first stage project in Guangzhou pump accumulator electricity station and 18 samples produced by the interpolation method were taken as the training samples. 22 samples of the second stage project were selected to verify this method. Compared with the artificial neural network (ANN) method and support vector machine (SVM) method, the results show the catastrophe progression method has excellent performance and high prediction accuracy, and can be used to evaluate the stability of surrounding rock in underground engineering.

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