针对露天采矿爆破振动对民房破坏的预测问题，采用主成分分析（ PCA ）和极限学习机（ ELM）方法，选取爆破振幅、主频率、主频率持续时间、灰缝强度、砖墙面积率、房屋高度、屋盖形式、圈梁立柱、施工质量、场地条件10个主要影响因素。引入相关性分析在主成分分析过程中，对相关性高的指标进行降维，把得到的3个综合因子和爆破振幅、主频率、主频率持续时间、砖墙面积率作为输入变量，构建露天煤矿PCA－ELM预测模型。选取露天矿实际爆破过程中测量的100组数据作为模型训练样本，用另外20组数据作为测试样本进行预测。结果表明：对民房破坏影响因素中灰缝强度、房屋高度、屋盖形式、圈梁立柱、施工质量、场地条件之间具有较高的关联度。该模型处理高维数据时较传统的ELM算法具有预测精度高、稳定性好等特点，可准确预测爆破振动对民房的破坏程度，误判率为1／20。%For the predicting problem of damage to residential house by blasting vibration in open pit mining , by a-dopting the principal component analysis ( PCA) and extreme learning machine ( ELM) method, 10 major influen-cing factors were selected , including blasting amplitude , main frequency , duration of main frequency , mortar joint strength, brick wall area ratio, building height, roof form, ring beam column, construction quality and site condi-tions.By introducing correlation analysis into PCA process , dimension reduction was carried out on the indexes with high correlation .Taking the obtained 3 comprehensive factors and blasting amplitude , main frequency , dura-tion of main frequency blasting and brick wall area ratio as the input variables , the PCA-ELM prediction model of the open-pit coal mine was established .100 groups of data measured at the process of blasting in open-pit mine were selected as the training samples of the model , and the other 20 groups of data were selected as the test samples to perform prediction .The results showed that in the influence factors of damage to residential house , there exists a higher correlation among the mortar strength , height of building , roof forms, beam column, the quality of construc-tion and site conditions .Compared with the traditional ELM algorithm , at the time of processing high-dimensional data, the model has the characteristics of high accuracy , good stability and so on, and it can accurately predict the damage extent of blasting vibration on the houses with the misjudgment rate as 1/20 .
展开▼
