基于主成分分析—支持向量机模型的矿岩可爆性等级预测研究

Research on Prediction of Rock Mass Blastability Classification Based on PCA-SVM Model

矿岩可爆性等级的准确评判对爆破开挖设计以及岩土工程的安全稳定具有重要意义。通过综合考虑岩体固有属性与实际爆破效果对矿岩可爆性等级的影响,选取了岩体声波、波阻抗、爆破漏斗体积、大块率、小块率和平均合格率共6类指标,进行可爆性预测研究。为消除影响指标之间的信息重叠,针对55个矿岩样本数据集的大量信息,采用主成分分析法降维,提取得到包含98.38%原始信息的4类主成分,最后引入支持向量机模型对可爆性等级进行预测研究。研究结果表明:(1)与原始SVM模型相比,基于主成分分析法—支持向量机的预测模型不仅降低了数据的维度,同时使得矿岩可爆性等级预测准确率由78.5%提高至90.1%;(2)基于主成分分析法—支持向量机预测模型的评判结果与实际情况较为吻合,少量误判主要发生在部分特征差异性较小的矿岩样本之间。基于分层随机抽样技术的PCA-SVM预测模型,保证了训练集与测试集样本数据的随机性和差异性,对研究指标维数较多且部分指标间相关性较强的数据模型具有较强的适用性,对相似工程的研究具有一定的借鉴意义。

The accurate evaluation of the classification of rock mass blast ability is of great significance to the design of blasting excavation and the safety and stability of geotechnical engineering.At present,scholars at home and abroad in the field of blasting have not reached a consensus on the evaluation method of rock blast ability.The factors affecting rock blasting are very complex,and the research indicators adopted by different scholars are different.By comprehensively considering the influence of the inherent properties of the rock mass and the actual blasting effect on the rock mass blast ability,six kinds of attributes including acoustic wave and wave impedance of rock mass,volume of blasting funnel,percentage of large blocks,percentage of small blocks and average eligibility rate were selected to predict the classification of rock mass blast ability.Considering the complex and varied geological conditions of engineering rock masses,according to the actual situation of several mines,55 sample data were selected,which cover quartzite,magnetite,skarn,diorite,gneiss,the lithology of marble,granite,limestone,metamorphic schist,dolomite,sandstone,etc..The sample raw data are from many rock mass engineering with different geographical locations and different geological conditions,which are representative.The PCA-SVM model and the SVM model were used to compare and analyze the explosive rock prediction results.Firstly,the same random seeds were used to ensure that the number of ore samples of the two models is the same as that of the training set and forecast set.Then,according to the data extracted by the principal component analysis and the raw data that have not been processed,the SVM models were established respectively,and the 11 prediction results were compared and analyzed.In the 11 randomized trials,the accuracy of PCA-SVM model was 100%at one time,there were 8 cases where one sample was misjudged,and 2 cases where two samples were misjudged.The number of misjudgments in the PCA-SVM model in a single random experiment is less than or equal to the SVM model.The average prediction accuracy of the PCA-SVM model is 90.1%,which was significantly higher than the SVM model.The results show that:(1)The prediction model based on principal component analysis and support vector machine eliminates the information overlap between the impact indicators and extracts four principal components which contain 98.38%of the original information.Compared with the standard SVM model,the PCA-SVM model not only reduces the dimension of the data,but also improves the accuracy of the rock explode grade from 78.5%to 90.1%.(2)The prediction results of PCA-SVM model are in good agreement with the actual situation.A small number of misjudgments mainly occur between some rock samples with small differences in characteristics.(3)The PCA-SVM prediction model based on the stratified random sampling technique ensures the randomness and difference of the sample data between the training set and the test set.This method is more scientific and reasonable than the general research model,and has a certain reference significance for the research of similar engineering.