• Precursor Prediction for Early Violent Failure based on Infrared Radiation Emissions for Coal Specimens under Different Loading Rates
    ID:503 View Protection:ATTENDEE Updated Time:2022-05-21 09:38:56 Hits:410 Invited speech

    Start Time:2022-05-26 15:20 (Asia/Shanghai)

    Duration:20min

    Session:[S1] Resource Development and Utilization [S1-1] Resource Development and Utilization-Session 1

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    Abstract
    The early prediction of violent coal-bearing strata failure using effective monitoring is very crucial to avoid losses due to catastrophic failures and geological disasters to ensure safe and efficient underground deep coal mining. In this study, the early coal failure precursor was established by researching the application of Critical Slowing Down Theory (CSDT) using two Infrared Radiation (IR) indexes i.e., Variance IR Temperature (VIRT) and Variance of Differential Infrared Image Temperature (VDIIT) under different loading rates. The CSDT parameters: variance and autocorrelation, are evaluated using both indexes in different time window and lag step lengths. The test results revealed that the abrupt and significant fluctuations in variance and autocorrelation for both indexes occurred during rock deformation and before the violent damage. The autocorrelation comparatively shows an obvious reliable fluctuation due to stationarity (show no change in fluctuation before the inflection point), which can be used as a precursor for violent rock failure. It has been shown that the stress level of autocorrelation at the inflection point decreases inversely with the loading rate for both indexes. These stress levels for VIRT are 0.920, 0.890, 0.865, and 0.813 of the σmax under the corresponding loading rates of 0.1, 0.4, 0.7, and 1 strain/s, respectively. For VDIIT, at loading rates of 0.1, 0.4, 0.7, and 1 strain/s the stress levels are 0.930, 0.892, 0.870, and 0.815 σmax, respectively. Therefore, it has been recommended that the prediction performance of precursory characteristics of IR can be improved by applying the CSDT for an early prediction of rock failure.


     
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    Speaker
    Naseer Muhammad Khan
    Balochistan University of Information Technology Engineering and Management Sciences

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