Laboratory investigation of railway-used ballast morphology using 3D imaging data analyses

Railway ballast deteriorates over time due to cyclic loading and environmental factors, necessi-tating its replacement to maintain track stability. However, the potential shortage of fresh ballast has posed challenges in replacing used ballast. One of the challenges in recycling used ballast is determining its useful life. This study proposes a method to characterize fresh and used ballast morphology and shape properties using digital images and advanced data analysis software. The aim is to establish a threshold that distinguishes when ballast can be considered used.. The study utilizes basic morphology parameters of flatness, elongation, roundness, sphericity, and convexity. Used ballast is generated through the Los Angeles abrasion (LAA) test method, while fresh ballast serves as the control sample. The analysis suggests that specific combinations of shape indices, such as roundness and convexity, offer insights into differentiating between the two types of ballast. Notably, significant differences are observed in the sphericity and convexity indices between fresh and used ballast samples. However, A systematic definition of used ballast based on the basic morphology indices alone was not feasible. Only the combination of sphericity index (> 0.825) and convexity index (> 0.9), by which both indices of used ballast were higher than those of fresh ballast, could roughly classify fresh and used ballast, although a minor overlap persisted. Further exploration of the data using advanced statistical techniques and machine learning could enhance the accuracy of defining used ballast.

Automated summary

Railway ballast deteriorates over time and replacing it is necessary for track stability, but the shortage of fresh ballast presents challenges in the recycling process. This study proposes a method for characterizing fresh and used ballast using digital images and data analysis software, aiming to establish a threshold for distinguishing between the two. The study analyzes morphology parameters and suggests that specific combinations of shape indices offer insights into differentiating between fresh and used ballast. Further exploration using advanced statistical techniques and machine learning could enhance the accuracy of defining used ballast.