Multi-source information fusion methodology for short-term rock failure prediction based on integrated acoustic-thermal-deformation monitoring

Abstract

The monitoring and prevention of geological hazards constitute a critical component in safeguarding public safety, property protection, and socio-economic stability. Short-term prediction methodologies for rock failure serve as the fundamental prerequisite for precise geological disaster mitigation. The rock deformation and failure process inherently involve energy accumulation, dissipation, and release, with the released signals useful for rupture prediction (including deformation, acoustic, thermal, and optical variations) exhibiting varying energy release ratios. This study proposes a multi-source fusion monitoring approach based on existing technologies: the inflection point method of rock volumetric strain, the multi-parameter trend and threshold method for acoustic emission waveforms, and the infrared radiation thermography time-space statistical analysis with standard deviation integration. Subsequently, an analytic hierarchy process-entropy weight method is established to comprehensively predict rock failure timing. The rock fracture energy is calculated through mechanical parameters derived from elastic solid mechanics theory and integrated prediction time. Acoustic emission localization coupled with infrared thermography enables comprehensive characterization of rock fracturing processes, providing technical support for final rupture pattern prediction. Experimental results demonstrate a 9.91% prediction error for failure timing and 0.902 energy prediction accuracy. The combined application of acoustic emission localization and infrared thermography effectively predicts principal rupture surfaces: granite exhibits typical X-shaped conjugate shear failure, while sandstone displays conventional shear failure. Different lithology has different precursory characteristics. Compared with the conventional crack development model of sandstone, the random micro crack propagation of granite brings greater prediction challenges. These findings validate that the proposed multi-source data fusion methodology offers novel insights into early warning systems in rock engineering, significantly enhancing geological hazard prevention capabilities.

Keywords

Rock failure / Short-term prediction / Rock mechanics / Geological disaster prevention / Multi-source fusion monitoring / Competition

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