Simulator-Driven Sieving Data Generation for Aggregate Image Analysis

Advancements in deep learning have enhanced vision-based aggregate analysis.However, further development and studies have encountered challenges, particularly in acquiring large-scale datasets.Data collection is costly and time-consuming, posing a significant challenge in acquiring large datasets required for training neural networks.To address this issue, this study introduces a simulation that efficiently generates the necessary data and labels for training neural networks.We utilized a genetic algorithm (GA) to create optimized lists of aggregates based on the specified values of weight and particle size distribution for the aggregate sample.This enabled sample data collection without conducting sieving tests.Our evaluation of the proposed simulation and GA methodology revealed errors of 1.3% and 2.7 g for aggregate size distribution and weight, respectively.Furthermore, we assessed a segmentation model trained with data from the simulation, achieving a promising preliminary F1 score of 78.18 on the actual aggregate image.