属性综合评价系统在岩爆发生和烈度分级中的应用

APPLICATION OF ATTRIBUTE SYNTHETIC EVALUATION SYSTEM IN PREDICTION OF POSSIBILITY AND CLASSIFICATION OF ROCKBURST

应用属性数学理论,建立了岩爆发生预测和烈度分级的属性识别模型。选取影响岩爆的主要因素:如最大切向应力σθ、单轴抗压强度σc、单轴抗拉强度σt以及弹性能量指数Wet,以σθ/σc、σc/σt以及Wet作为岩爆评价指标和烈度分级标准;构造了属性测度函数;定义了单指标属性测度和样本综合属性测度的计算方法。利用国内外一些重大岩石地下工程实例对模型进行了验证,由于评判结果与实际情况基本一致,并且与模糊综合评判法、物元可拓评判法等的评判结果有较好的一致性,从而验证了属性识别模型应用于岩爆预测预报的可行性和有效性。此外,利用该模型对某水电站工程和秦岭隧道工程的岩爆情况进行了评判,结果与人工神经网络法、距离判别法等的评判结果一致。

Based on attribute mathematical theory, this paper establishes an attribute recognition model to predict the possibility and classification of rockburst. Firstly, the main factors of rockburst, such as the maximum tangential stress of the cavern wall σθ, uniaxial compressive strength σc, uniaxial tensile strength σt, and the elastic energy index of rock Wet, are taken into account in the analysis. Three factors, σθ/σc, σc/σtort and Wet are defined as the criterion indices for rockburst prediction in the proposed model. Secondly, attribute measurement functions are constructed. Thirdly, the calculation methods are proposed to calculate the attribute measurement of single index and the synthetic attribute measurement of samples. A series of underground rock projects are assessed with the proposed model. It indicates that the synthetic assessment results agree well with the actual records, and are consistent with those of the fuzzy synthetic evaluation model and the matter-elements model. Therefore, the feasibility of the proposed model is validated. Moreover, the proposed model is used to predict rockbursts of a hydropower station and Qinling Tunnel, the results are consistent with those of the synthetic evaluation method such as artificial neural network and distance discriminant analysis method.