As high-quality aggregates become more difficult to utilize in construction projects due to cost, availability, and location, new and better performance evaluations of aggregate materials are needed. Two recent MnDOT projects focusing on the mechanistic based evaluations of best value granular materials have clearly shown that differences in shape, texture and angularity of aggregate materials, in addition to gradation
As high-quality aggregates become more difficult to utilize in construction projects due to cost, availability, and location, new and better performance evaluations of aggregate materials are needed. Two recent MnDOT projects focusing on the mechanistic based evaluations of best value granular materials have clearly shown that differences in shape, texture and angularity of aggregate materials, in addition to gradation (particle size), influence both structural , i.e., strength, modulus and deformation, as well as drainage characteristics of unbound and bound pavement layers. There is a need to implement aggregate imaging techniques that already exist to develop construction specifications, testing procedures, and performance guidelines in order to utilize the best value of the available aggregates in pavement construction – both in flexible and rigid pavements.
End-performance based aggregate selection and evaluation can be more readily established through improved testing performed using aggregate image analysis. The research approach proposed utilizes the technology employed in the University of Illinois Aggregate Image Analyzer (UIAIA) to quantify aggregate shape properties in the field at gravel pits, rock quarries, and construction sites by means of rapid, automated and accurate measurements. The UIAIA outputs quantitative indices based on image evaluation of aggregate size distributions and quantification of individual particle shape, texture and angularity properties. Instead of being limited to capturing images of aggregate particles one by one in the laboratory, this project proposes to replace this process with a machine vision based faster field imaging process, which will yield estimates of the shape property of a number of aggregates directly from color images.
The aggregate size and shape properties will be linked to performances of constructed pavement layers. In addition, new aggregate blends and improved designs can be developed taking advantage of improved quantification of aggregate property adjustments tied more directly to performance. As an example, the imaging techniques may be useful in evaluating the binder content in recycled asphalt pavements for better recycling practices as well as cost effective utilization of local and marginal aggregates in pavement projects.
Potential benefits of this project include:
- the ability to conduct aggregate size and shape property testing in the field - at the quarry, pit, or construction site;
- the ability to evaluate differences in quarry operations such as crushing methods, for classifying aggregate material sources in terms aggregate size and shape properties – better QA/QC;
- more consistent test results and better confidence in the size and shape properties of aggregates used in construction;
- significant cost savings by reducing the need for conducting costly and time consuming laboratory tests required for mechanistic based design procedures;
- optimized aggregate selection/utilization, and significant economic benefits with sustainable use of aggregate resources in building transportation infrastructure;
- better performance, primarily of pavement base layers, in terms of stability, stiffness and drainage characteristics over the life of a pavement;
- development of new aggregate blends and improved designs based on aggregate size and shape properties to demonstrate the impact of aggregate selection and aggregate property adjustments on performance improvements;
- imaging based evaluation of the binder content as coating on the reclaimed asphalt pavement (RAP) aggregates as part of RAP fractionation, property evaluation and utilization.
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