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弹性波CT反演识别煤岩体空区实验研究 被引量:10

Empty area recognition technology of coal and rock mass by elastic wave ct inversion
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摘要 通过利用多通道超声测试系统和弹性波CT反演方法对内含空区的混凝土试样开展试验研究,试验证明了弹性波CT反演探测空区的可行性,且通过几种不同激发行列间距的排布方式,得到了该试样反演网格长度的最优值。依据反演得到的波速分布,建立了空区识别的相关系数指标及偏差指标,通过8种不同测试方案得出超声波激发传感器在体分布下空区识别效果优于面分布。基于数理统计方法,建立了空区判识数学模型和评价指标,整体空区位置识别准确,空区识别率在30%~40%之间,误识别率在3%以下,判识结果可靠。 The multi-channel ultrasonic test system and the elastic wave CT inversion method have been used to conduct an experimental study on the concrete samples containing voids.The results have shown that the feasibility of elastic wave CT inversion is proved to detect the void area,and the optimal value of the inversion grid length of the samples is obtained through several different arrangements of excitation row and column spacing.According to the wave velocity distribution obtained by inversion,the correlation coefficient index and deviation index for the identification of empty area recognition are established.Through eight different test schemes,it is concluded that the recognition effect of ultrasonic excited sensor under three-dimensional distribution is more optimized than that of plane distribution.Based on the mathematical statistics,a mathematical model and evaluation index of empty area recognition are established.And the overall empty position recognition is accurate.The recognition rate is between 30%and 40%and the false recognition rate is less than 3%.And the recognition result is reliable.
作者 巩思园 田鑫元 郑有雷 白金正 李勋达 赵猛 GONG Siyuan;TIAN Xinyuan;ZHENG Youlei;BAI Jinzheng;LI Xunda;ZHAO Meng(School of Mines,China University of Mining and Technology,Xuzhou,Jiangsu 221116,China;Jining No.3 Mine,Yanzhou Coal Mining Co Ltd,Zoucheng,Shandong 272069,China;Linyi Shandong Energy Group Co Ltd,Production Technology Department,Linyi,Shandong 276017,China)
出处 《采矿与安全工程学报》 EI CSCD 北大核心 2020年第4期759-766,共8页 Journal of Mining & Safety Engineering
基金 江苏省研究生科研与实践创新计划项目(KYCX19_2176) 中国矿业大学研究生科研与实践创新计划项目(ZGKD19_2176)
关键词 空区 弹性波CT反演 波速分布 判识 empty area elastic wave CT inversion velocity distribution identification
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