Experimental investigation for damage assessment of snow using acoustice mission data

Abstract

The study identified key research gaps, including the absence of appropriate AE parameters for snow, the lack of calibrated AE instruments specifically designed for snow monitoring, and insufficient experimental research on snow with layered structures similar to real-world scenarios. To fill these gaps, a series of compression experiments were meticulously conducted on multiple snow samples under controlled environmental conditions. Highly sensitive AE sensors captured subtle fracture signatures within the snow samples during deformation, allowing for observation and analysis of the acoustic emission signals generated during the process. The research encountered challenges in accurately estimating the power law exponent for the data derived from AE signals. To overcome this, a novel methodology for estimating the power law exponent, termed the revised b-value (rb-value), was developed, validated, and found to produce consistent and accurate results on synthetic and existing datasets. Further progression involved applying the validated methodology on empirical data sets collected from an experimental site on amount a in slope. The methodology successfully extracted detailed information about the power law exponent governing the deformation behavior of the mountain snowpack, providing crucial insights into understanding mechanical behaviour and identifying potential instability. It was discovered that herate of fall of the rb-value served as a strong indicator of developing instability in the mountain snowpack. The combination of compression experiments advanced AE sensing technology, and the novel power law exponent estimation methodology contributed to a deeper understanding of snow behavior under compression. The research outcomes offer valuable information about snow deformation and contribute to developing a more robust and accurate method for estimating power-law exponents in various scientific applications.