Resistivity response and damage evolution characteristics of the whole failure process of water-bearing coal samples under uniaxial compression

Abstract

In recent years, resistivity-based geophysical techniques have gained prominence for their role in improving the accuracy of geological hazard prediction and early warning systems. This study explores how varying moisture levels in coal influence its resistivity response during deformation. Coal specimens with controlled water content were prepared using a gravimetric approach, and a custom-built, real-time uniaxial compression test platform was employed to simultaneously monitor resistivity and acoustic emission (AE) signals. The experimental findings reveal that resistivity evolves through distinct stages during the progressive failure of coal samples, exhibiting high sensitivity to both microcrack closure and the development of macro-fractures. Notably, an increase in water content results in a marked decline in compressive strength and elastic modulus. Concurrently, AE parameters such as cumulative ringing counts and energy exhibit downward trends, suggesting a shift from tensile-dominated to shear-dominated failure mechanisms. To quantify resistivity fluctuations, a statistical index – resistivity standard deviation – was introduced, serving as the basis for defining a new damage variable ( D _R ). Comparative analysis shows that D _R consistently exceeds the traditional AE-based damage variable, with both indicators displaying complementary trends. During the compaction phase, resistivity offers a clearer representation of internal damage; in the elastic stage, both AE and resistivity provide consistent feedback. However, in the plastic deformation and failure phases, AE data capture damage evolution more accurately, although resistivity still reflects larger fluctuation amplitudes – implying stronger early warning potential. As the coal’s moisture content increases, the overall sensitivity of the resistivity signal weakens, especially during the early compaction stage. Taken together, these observations suggest that resistivity monitoring, particularly when integrated with AE data, holds promise for improving the reliability of damage prediction in water-bearing coal pillars. Such multi-parameter approaches could significantly enhance the effectiveness of real-time monitoring and disaster prevention strategies in underground coal mining.