(1. 北京科技大學(xué) 土木與資源工程學(xué)院，北京 100083；
2. 包鋼鋼聯(lián)巴潤(rùn)礦業(yè)分公司，包頭 014080)
摘 要: 針對(duì)爆破振動(dòng)對(duì)礦山邊坡穩(wěn)定性評(píng)價(jià)中存在諸多不確定性因素的問題，采用距離判別分析理論并對(duì)該模型進(jìn)行加權(quán)賦值的改進(jìn)，用以區(qū)別不同參數(shù)之間的重要性。選用振動(dòng)速度峰值(水平、垂直)、振動(dòng)主頻(水平、垂直)、爆心距共5個(gè)影響因素作為判別因子，建立露天采礦受到爆破振動(dòng)時(shí)產(chǎn)生邊坡危害判別的距離判別分析模型(DDA模型)和改進(jìn)加權(quán)距離判別分析模型(改進(jìn)DDA模型)；通過6次現(xiàn)場(chǎng)爆破試驗(yàn)收集到24組爆破振動(dòng)實(shí)測(cè)數(shù)據(jù)，以其中18組現(xiàn)場(chǎng)數(shù)據(jù)作為模型的學(xué)習(xí)樣本對(duì)模型進(jìn)行訓(xùn)練，建立與之相對(duì)應(yīng)的判別函數(shù)，利用回代法進(jìn)行誤判率的回檢，并用6組現(xiàn)場(chǎng)數(shù)據(jù)作為預(yù)測(cè)樣本進(jìn)行測(cè)試。結(jié)果表明：DDA模型回檢誤判率為5.6%，對(duì)影響因素的重要性進(jìn)行加權(quán)的改進(jìn)DDA模型回檢誤判率為0%，其判別結(jié)果與實(shí)際結(jié)果完全吻合。改進(jìn)加權(quán)距離判別分析模型的算法簡(jiǎn)單、收斂速度快、預(yù)測(cè)精度高，為露天采礦爆破振動(dòng)對(duì)邊坡危害程度的判別提供了一種新思路。
關(guān)鍵字: 爆破振動(dòng)；邊坡危害；距離判別分析模型
（1. School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China;
2. BaRun Mining Branch, Baotou Steel Union Co., Ltd., Baotou 014080, China）
Abstract:Aiming at the problem that there are many uncertain factors in the evaluation of mine slope stability by blasting vibration, the distance discriminant analysis theory was adopted and the weighted value of the model was improved to distinguish the importance of different parameters. The distance discriminant analysis model (DDA model) and the improved distance discriminant analysis model (improved DDA model) of slope hazards caused by blasting vibration in open-pit mining were established by selecting five influencing factors, namely, peak vibration velocity (horizontal and vertical), dominant vibration frequency (horizontal and vertical), and distance from blasting center as discriminant factors. The 24 groups of measured blasting vibration data were collected from 6 field blasting tests, 18 groups of which were used as the learning samples to train the model, and the corresponding discriminant function was established. The misjudgment rate was checked back by the back substitution method, and 6 groups of field data were used as the prediction samples for testing. The results show that the misdiagnosis rate of the DDA model is 5.6%, and the misdiagnosis rate of the improved DDA model weighted by the importance of influencing factors is 0%. The discriminant results of the improved weighted distance discriminant analysis model are completely consistent with the actual results. Because the algorithm is simple, the convergence speed is fast, and the prediction accuracy is high, the improved DDA model provides a new idea for the discrimination of the damage degree of the slope caused by blasting vibration in open-pit mining.
Key words: blasting vibration; slope failure; distance discriminant analysis model
