Fouling refers to the condition of railroad ballast when voids in this unbound aggregate layer are filled with relatively finer materials or fouling agents, which commonly come from breakdown of the ballast aggregate, outside contamination such as coal dust from coal trains, or subgrade soil intrusion. Effects of different fouling agents on ballast aggregate shear strength were studied at the University of Illinois. Through the use of a large direct shear (shear box) device, the strength properties of both clean and fouled ballast samples were determined when three types of fine materials–-coal dust, plastic clayey soil, and mineral filler–-were added to clean ballast samples at various percentages by weight of ballast under both dry and wet [mostly optimum moisture content (OMC)] conditions. Realistic sample preparation procedures were conducted to closely simulate field-fouling scenarios. Test results showed that when the coal dust fouling percentage increased, the ballast shear strength steadily decreased. Wet fouling was found to exacerbate this trend. Results of ballast samples fouled with clay and mineral filler also showed decreasing trends in strength properties; however, coal dust was by far the worst fouling agent for its impact on track substructure and roadbed. Approximately 15% coal dust fouling by weight of ballast was statistically significant to cause considerable strength reductions. In the case of ballast fully fouled with wet coal dust at 35% OMC, the friction angles obtained were as low as the friction angle of coal dust itself.

Railroad ballast layer consists of discrete aggregate particles and Discrete Element Method (DEM) is one of the most suitable ways to simulate the deformation behaviour of particulate nature of ballast materials. An aggregate imaging based DEM simulation platform developed at the University of Illinois at Urbana–Champaign (UIUC) can simulate railroad ballast behaviour through the use of polyhedron shaped discrete elements. These ballast elements are created with realistic size and shape properties from image analyses of actual particles using an Aggregate Image Analyzer. The UIUC railroad ballast DEM model was recently put to test for predicting settlement behaviour of full-scale test sections under repeated heavy axle train loading. Field settlement data were collected from the Facility for Accelerated Service Testing (FAST) for Heavy Axle Load (HAL) applications at Transportation Technology Center (TTC) in Pueblo, Colorado, to validate the DEM model. The ballast settlement predictions due to the repeated train loading indicate that the DEM model could predict magnitudes of the field ballast settlements from both early loading cycles and over 90 Million Gross Tons (MGTs) performance trends reasonably accurately. The settlement predictions were sensitive to aggregate shape, gradation and initial compaction condition (density) of the constructed ballast layer.

Aggregate angularity affects the shear strength properties of asphalt concrete and granular base layers in pavements. It also improves aggregate interlock and load transfer properties of jointed concrete pavements. The development of a quantifiable index based on image analysis to characterize the angularity of coarse aggregate used in pavement layers is described. The new angularity index (AI) was developed as part of the University of Illinois Aggregate Image Analyzer, and the procedure was calibrated for two aggregate samples, rounded gravel and crushed stone, which possess the two extremes of particle angularity. A statistical study demonstrated that the AI computation technique is not only able to distinguish crushed stone from gravel but also is robust enough to give similar AI values regardless of the particle size and orientation. Furthermore, the crushed stone and gravel samples together with a 50–50 blend of the two samples by weight were tested for shear strength under triaxial loading conditions. The AI value computed for these samples could be correlated to the angle of internal friction and thus the shear strength properties of the samples. The AI distributions of representative aggregate samples from Illinois, crushed gravel, limestone, and dolomite, were also computed. The newly developed index has also demonstrated the capability to distinguish between crushed stone and crushed gravel samples. With this AI value as a measure of aggregate angularity, pavement engineers can objectively quantify the influence of aggregate angularity on asphalt concrete, portland cement concrete, and granular mix performance and thereby establish meaningful criteria relating aggregate properties to performance indicators.

This paper presents findings of a research study focused on investigating influences of coarse aggregate morphologies on the strength and permanent deformation behavior of unbound aggregate materials. Angularity and surface texture properties of six different types of aggregates were evaluated using two shape indices, angularity index (AI) and surface texture index (ST), based on image analysis. Twenty-one unbound aggregate blends having the same gradation and voids content but different crushed percentages were prepared by mixing the imaging evaluated aggregates and tested using triaxial tests for strength and permanent deformation. The laboratory permanent strain—load application ( εp –N) curves evaluated using a phenomenological power model showed the A and b parameters to be heavily dependent on applied stress levels. Aggregate blends with higher AI and ST indices were consistently associated with lower magnitudes of A and b . The angularity property was found to contribute mainly to the strength and ...