中国安全生产科学技术
中國安全生產科學技術
중국안전생산과학기술
JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY
2015年
8期
119-125
,共7页
露天采矿安全%爆破振动%极限学习机%主成分分析%民房破坏
露天採礦安全%爆破振動%極限學習機%主成分分析%民房破壞
로천채광안전%폭파진동%겁한학습궤%주성분분석%민방파배
open-pit mining safety%blasting vibration%extreme learning machine%principal component analysis%damage to residential house
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。
針對露天採礦爆破振動對民房破壞的預測問題，採用主成分分析（ PCA ）和極限學習機（ ELM）方法，選取爆破振幅、主頻率、主頻率持續時間、灰縫彊度、磚牆麵積率、房屋高度、屋蓋形式、圈樑立柱、施工質量、場地條件10箇主要影響因素。引入相關性分析在主成分分析過程中，對相關性高的指標進行降維，把得到的3箇綜閤因子和爆破振幅、主頻率、主頻率持續時間、磚牆麵積率作為輸入變量，構建露天煤礦PCA－ELM預測模型。選取露天礦實際爆破過程中測量的100組數據作為模型訓練樣本，用另外20組數據作為測試樣本進行預測。結果錶明：對民房破壞影響因素中灰縫彊度、房屋高度、屋蓋形式、圈樑立柱、施工質量、場地條件之間具有較高的關聯度。該模型處理高維數據時較傳統的ELM算法具有預測精度高、穩定性好等特點，可準確預測爆破振動對民房的破壞程度，誤判率為1／20。
침대로천채광폭파진동대민방파배적예측문제，채용주성분분석（ PCA ）화겁한학습궤（ ELM）방법，선취폭파진폭、주빈솔、주빈솔지속시간、회봉강도、전장면적솔、방옥고도、옥개형식、권량립주、시공질량、장지조건10개주요영향인소。인입상관성분석재주성분분석과정중，대상관성고적지표진행강유，파득도적3개종합인자화폭파진폭、주빈솔、주빈솔지속시간、전장면적솔작위수입변량，구건로천매광PCA－ELM예측모형。선취로천광실제폭파과정중측량적100조수거작위모형훈련양본，용령외20조수거작위측시양본진행예측。결과표명：대민방파배영향인소중회봉강도、방옥고도、옥개형식、권량립주、시공질량、장지조건지간구유교고적관련도。해모형처리고유수거시교전통적ELM산법구유예측정도고、은정성호등특점，가준학예측폭파진동대민방적파배정도，오판솔위1／20。
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 .
