Monte Carlo Simulation-Based Uncertainty Integration into Rock Particle Shape Descriptor Distributions

Abstract

The particles within the rock samples are present in extensive ranges of shapes and sizes, and their characterization and analysis exist with a considerable diversity. The prior research works have appraised the significance of the particle shape types and their effects on the geotechnical structures and deficiencies by evaluating the uncertainty-related rock particle shape descriptors (PSDs). In this work, the Monte Carlo simulation (MCS) is used in order to present a framework to integrate the inherent uncertainty associated with PSDs. A tabletop microscope is used to measure the primary particle shape distribution for the sandstone samples. An open-source processing tool, ImageJ, is used in order to analyze PSDs. The probabilistic distribution of PSDs is acquired using MCS according to the relative frequency histogram of the input parameters. Additionally, a probabilistic sensitivity analysis is performed in order to evaluate the importance of the input parameters in PSDs. The sensitivity analysis results demonstrate that the major axis and area are the most influential parameters involved. The simulation results obtained have revealed that the proposed framework is capable of integrating the inherent uncertainties related to the particle shape.