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

• Published in: Case Studies in Construction Materials Vol. 19; p. e02272
• Main Authors: Varuntanya, Krittaya, Kwunjai, Sararat, Binaree, Theechalit, Jitsangiam, Peerapong, Jing, Guoqing, Aela, Peyman
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2023; Elsevier
• Subjects: Ballasted railway track; Fouled ballast; Imaging data analyses; Morphology; Used ballast; Used ballast; Fouled ballast; Ballasted railway track; Imaging data analyses; Morphology
• ISSN: 2214-5095; 2214-5095
• DOI: 10.1016/j.cscm.2023.e02272

Railway ballast deteriorates over time due to cyclic loading and environmental factors, necessitating 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.