Search

The utilization of CO2 mineralization fly ash (F) and coal gangue (G) technology is proposed in this research work to prepare underground backfilling materials. The test process can be divided into pre-treatment and post-treatment stages. In the pre-treatment stage, a sealed stirring vessel is used to conduct CO2 wet mineralization. The ratios of F and G were selected as follows: 20%:60% (F2G6), 30%:50% (F3G5), 40%:40% (F4G4), 50%:30% (F5G3), and 60%:20% (F6G2). The ratios were prepared into Φ50 mm × 100 mm cylindrical samples, with curing durations of 3 d, 7 d, 14 d, and 28 d. In the post-processing stage, the SANS microcomputer-controlled electronic universal testing machine and FLIR A615 infrared thermal imager were used to carry out uniaxial loading and temperature detection, respectively. The unconfined compressive strength (UCS), X-ray diffraction (XRD), average infrared radiation temperature (AIRT), variance of original infrared image temperature (VOIIT), and variance of successive minus infrared image temperature (VSMIT) of the samples were compared and analyzed. The results indicated that when curing reaches 14 d, the strength approaches its peak, with minimal changes in strength over a delayed period; furthermore, as the ratio of F to G continues to increase, the mineralization effect gradually strengthens, reaching its optimum level at a ratio of 5:3. However, when the ratio exceeds 5:3, signs of deteriorating mineralization effect start to appear. During the loading process, the AIRT of the mineralized samples showed a continuous increase, but the VOIIT and VSMIT of the mineralized sample both exhibited significant fluctuations or rapid increases during damage rupture. Moreover, the rise in the AIRT value was found to be linked to the increase in the ratio of F to G. This indicates that F has a higher thermal–mechanical conversion efficiency compared to G, so the temperature change will be greater during the loading process. The drastic changes in the VOIIT and VSMIT indicate that they can be used as sensitive response indicators for sample rupture, and can predict and warn of damage rupture in mineralized samples. Research work can provide practical guidance and reference for underground backfilling of CO2 mineralization industrial waste. [ABSTRACT FROM AUTHOR]

• A noise removal method for infrared radiation thermal image sequence of rock is proposed. • The denoising parameters in the proposed denoising method are optimized. • The variation characteristics of rock infrared radiation information after denoising are studied, and its variation principle is discussed. In order to overcome the defects of low contrast and low signal-to-noise ratio of the infrared thermal image of rock (ITIR), a joint denoising method of additive and multiplicative noise is proposed for infrared radiation thermal image of rock bearing fracture process. ITIR is de-noised using background phase subtraction, adaptive median filtering, and wavelet multi-scale decomposition threshold denoising Firstly, the noise types in ITIR are analyzed, the basic principles and implementation steps of the denoising method are described, and the denoising effects of different combinations of wavelet basis functions with different decomposition scales in the wavelet threshold denoising process are simulated. Finally, the denoising effect was evaluated in terms of peak signal-to-noise ratio (PSNR), root mean square error (RMSE), and blind/referenceless image spatial quality evaluator (BRISQE). The results show that the PSNR of ITIR is increased by 15.01 % based on removing additive noise. The image quality scores increased by 49.54 % before and after overall denoising. The studies have significance for IR signal extraction and denoising in rocks and solid materials. [ABSTRACT FROM AUTHOR]

• A new quantitative index of rock infrared radiation is proposed: infrared image entropy (IE). • The relationship between the rock's stress and IE as well as the precursor of rock failure threshold is established. • The mechanism of infrared image entropy change in the rock loading stage is revealed. During the process of force deformation, the rock will be destabilized and damaged, and a deep understanding of the rock rupture precursors is a crucial foundation for early warning of mine hazards, including mine water inrush and surrounding rock destabilization. This study investigates the properties of infrared radiation (IR) and the precursors of rupture in the process of rock fracture using a novel thermal infrared analysis method called index-infrared image entropy (IE). The IE variation mechanism is discussed together with the frequency histogram of infrared images. The results highlight that IE may be used to characterize the IR information of rock during the pre-peak loading period. The IE value increases rapidly during the pre-peak loading period, then increases slowly and eventually tends to stabilize. Pre-peak stress and IE are correlated using a logarithmic function. The rock enters the plastic stage when the change in IE reaches 2.78, which may be utilized as a precursor threshold of bearing rock failure. At this point, the precursor stress level is 0.93σ max. In addition, abnormal IR histogram changes from a single peak to a double peak form may serve as a crucial precursor to bearing rock failure. The study's findings are useful for mining and geotechnical engineering monitoring and early warning of nearby rock instability and mine water inrush. [ABSTRACT FROM AUTHOR]

• The Contrast Texture Feature Values (CTFV) is proposed to extract subtle changes in the thermal image caused by crack evolution based on the Gray Level Co-occurrence Matrix (GLCM). • The cumulative Crack Texture Thermal Image (CTTI) is reconstructed using CTFV, which can reflect the spatial evolution process of loading coal and rock cracks. • The CTFV can provide readily identifiable precursor information for the coal and rock failure, and the precursor can be divided into high risk, medium risk, and low risk levels based on different gray levels. • An anomaly detection method for CTFV was proposed based on sliding window probability density estimation to achieve real-time and adaptive recognition of rock failure precursors. Early warning of the catastrophic failure of rocks is a challenging rock mechanics problem. This article proposed a failure precursor recognition method based on infrared thermal image texture features for coal and rock. Based on spatiotemporal background noise correction for infrared thermal images, a new thermal image parameter of loading coal and rock, Contrast Texture Feature Values (CTFV), is proposed to extract subtle changes in the thermal image caused by crack evolution based on the Gray Level Co-occurrence Matrix (GLCM). The cumulative Crack Texture Thermal Image (CTTI) is reconstructed using CTFV, which can accurately reflect the spatial evolution process of loading coal and rock cracks. The CTFV remains at 0 in the early stage of loading and gradually increases with stress increase at the unstable crack propagation stage, which can serve as a reference for precursor warning of coal and rock failure. For shale, sandstone, and limestone, the precursor of CTFV at grayscale levels of 7, 8, and 9, can be classified into high failure risk, medium failure risk, and low failure risk, respectively. For coal samples, the CTFV is only applicable for the critical warning of high failure risk when the grayscale level is 7. Then, an adaptive identification method for failure precursors based on the sliding window probability density estimation method is proposed. The research results can enhance the reliability of IR monitoring technology for rock failure and instability early warning and can provide support for the prevention and control of rock engineering and geological disasters. [ABSTRACT FROM AUTHOR]