Characterization of Aggregate Angularity in the Frequency Domain. Issue 10 (October 2020)

• Record Type: Journal Article
• Authors: Xu, Hongbin; Prozzi, Jorge A.; Kim, Moo Yeon; Hernandez, Joaquin Bernardo
• Is Part Of: Transportation research record. Volume 2674:Issue 10(2020)
• Page Start: 324
• Page End: 334
• Publication Date: 2020-10
• Subjects: Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.1177/0361198120939095
• Languages: English
• ISSNs: 0361-1981

Abstract

The morphology of aggregate particles used for pavement construction plays an essential role in the structural capacity and safety performance of pavement structures. Each of the three main components of aggregate morphology (form, angularity, and texture) has a distinct effect on pavement performance and corresponds to a different frequency range. Considering the challenge in segregating form, angularity, and texture in the space domain, characterizing them separately in the frequency domain would be beneficial and would allow for a more objective and detailed classification system for aggregate morphology. This study focuses on the characterization of aggregate angularity in the frequency domain with the objective of obtaining a parameter that is free of individual subjectivity. Since aggregate angularity is a subjective visual descriptor of aggregate shape variations at corners, a survey was conducted of pavement engineers to collect visual ratings of aggregate angularities using a set of aggregates. Thereafter, using the average visual ratings from the survey responses as reference, three common aggregate angularity indexes were evaluated: roundness, the University of Illinois Aggregate Image Analyzer (UIAIA) angularity index, and the Aggregate Image Measurement System (AIMS) angularity index. In addition, with the aid of the discrete Fourier transform (DFT) algorithm, the contributing frequencies were acquired for visual rating, along with roundness and the UIAIA and AIMS angularity indexes. Based on the contributing frequencies identified, prediction models were successfully established for visual rating: roundness and the UIAIA and AIMS angularity indexes. It was concluded that DFT can be accurate in objectively assessing angularity and that roundness is the more robust parameter and can be accurately predicted by the models developed.