ESTIMATION OF PARTICLE SIZE DISTRIBUTION BY EXCAVATION SOUND USING MACHINE LEARNING

Abstract

This paper proposes a method for evaluating particle size distribution by excavation sound using machine learning. First, the mass content of artificial beads of different particle sizes was estimated from the excavation sound of the model ground mixed with the beads of different particle sizes. Second, we conducted the same tests with a silica and other sands. The test results showed that the proposed method using machine learning has the potential to estimate particle size distribution of geomaterials.