露天采矿爆破振动对砌体结构破坏效应预测的Fisher判别模型及应用

 应用Fisher判别分析理论并结合工程实际特点，从爆破振动特征参量和砌体结构自身特性这2个方面出发，选取峰值质点振动速度(PPV)、爆破振动主频率、主频率持续时间、灰缝强度、圈梁构造柱、房屋高度、屋盖形式和砖墙面积率8个影响因素作为判别因子，建立爆破振动对砌体结构破坏效应预测的Fisher判别分析模型。将该方法应用到湖北一露天采场爆破振动对砌体结构破坏效应预测问题中，利用现场实测的108组数据进行训练和检验，回判估计的误判率为0.083，通过求解判别函数，认为峰值质点振动速度为最重要的判别指标，其后依次为圈梁构造柱、屋盖形式、砖墙面积率、房屋高度、爆破振动主频率、主频率持续时间和灰缝强度，可以为同类工程在选取爆破振动对砌体结构破坏效应的判别指标方面提供参考。利用其他12组现场数据作为预测样本进行测试，预测结果与实际情况吻合较好。研究表明，该方法回判估计的误判率低，判别性能良好，是爆破振动对砌体结构破坏效应预测的一种有效新方法，可以在实际工程中进行推广应用。

FISHER DISCRIMINANT ANALYSIS MODEL AND ITS APPLICATION TO PREDICTING DESTRUCTIVE EFFECT OF MASONRY STRUCTURE UNDER BLASTING VIBRATION OF OPEN-PIT MINE

Based on the Fisher discriminant theory and the actual characteristics of the project，a Fisher discriminant analysis(FDA) model to predict the destructive effect of masonry structure under blasting vibration of open-pit mine is established. Eight major factors are taken into account from the aspects of blasting vibration and structure dynamic characteristics such as peak particle velocity(PPV)，dominant frequency，dominant frequency duration，mortar joints intensity，collar beam structure，height of housing，roof forms and the rate of brick walls. After training and testing 108 sets of measured data in a open-pit mine，the discriminant functions of FDA are solved，and the ratio of mistake-distinguish is 0.083. The functions show that PPV is the most important discrimination indicator，followed by the structural columns of collar beam，roof forms，the rate of brick walls，housing height，the main frequency，the main frequency of duration and intensity of mortar joints，which provides efficient and accurate reference to forecast the destructive effect of masonry structure from blasting vibration. Another 12 groups of measured data are tested as forecast samples，and the predicted results are consistent with the measured ones. The prediction accuracy of FDA model is acceptable，so it is a new approach to predict destructive effect of masonry structure under blasting vibration，which can be applied to practical engineering.