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Quantitative description of infrared radiation characteristics of preflawed sandstone during fracturing process 被引量:1
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作者 Ke Zhang Xianghua Liu +1 位作者 Yulong Chen Heming Cheng 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2021年第1期131-142,共12页
Previous studies show that infrared radiation temperature(IRT)abnormalities are always accompanied by the crack development in rocks under external loads.In this context,experiments were conducted on preflawed sandsto... Previous studies show that infrared radiation temperature(IRT)abnormalities are always accompanied by the crack development in rocks under external loads.In this context,experiments were conducted on preflawed sandstone to investigate the infrared radiation characteristics during failure process.Two indicators were defined herein,i.e.coefficient of variation of IRT(CVIRT)and skewness of IRT(SIRT).The regression analysis shows that the IRT probability distributions during loading process fit the Gaussian model.The variations in the CVIRT are characterized by four stages:primary stage,steady stage,accelerating stage and post-peak stage.Besides,the variations in the SIRT are divided into three stages:primary stage,steady stage and failure and post-peak stage.The precursor point for preflawed rock failure is identified based on the CVIRTetime curve,with average precursor point of 83%of the peak stress.Compared with other IRT indicators,the proposed two IRT indicators have higher sensitivity to IRT abnormalities during failure process.Furthermore,the connection between the IRT indicators and the rock fracturing was investigated to interpret the IRT indicator abnormalities.Based on the Verhulst inverse function,a new quantitative model was presented to describe the primary stage,steady stage and accelerating stage of the CVIRTetime curve.The results obtained in this study can provide early-warning information for rock failure prediction. 展开更多
关键词 remote sensing rock mechanics Infrared radiation characteristics STATISTICS Precursor point Verhulst inverse model
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