Automation of aggregate characterization using laser profiling and digital image analysis



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Particle morphological properties such as size, shape, angularity, and texture are key properties that are frequently used to characterize aggregates. The characteristics of aggregates are crucial to the strength, durability, and serviceability of the structure in which they are used. Thus, it is important to select aggregates that have proper characteristics for each specific application. Use of improper aggregate can cause rapid deterioration or even failure of the structure. The current standard aggregate test methods are generally labor-intensive, time-consuming, and subject to human errors. Moreover, important properties of aggregates may not be captured by the standard methods due to a lack of an objective way of quantifying critical aggregate properties. Increased quality expectations of products along with recent technological advances in information technology are motivating new developments to provide fast and accurate aggregate characterization. The resulting information can enable a real time quality control of aggregate production as well as lead to better design and construction methods of portland cement concrete and hot mix asphalt. This dissertation presents a system to measure various morphological characteristics of construction aggregates effectively. Automatic measurement of various particle properties is of great interest because it has the potential to solve such problems in manual measurements as subjectivity, labor intensity, and slow speed. The main efforts of this research are placed on three-dimensional (3D) laser profiling, particle segmentation algorithms, particle measurement algorithms, and generalized particle descriptors. First, true 3D data of aggregate particles obtained by laser profiling are transformed into digital images. Second, a segmentation algorithm and a particle measurement algorithm are developed to separate particles and process each particle data individually with the aid of various kinds of digital image technologies. Finally, in order to provide a generalized, quantitative, and representative way to characterize aggregate particles, 3D particle descriptors are developed using the multi-resolution analysis feature of wavelet transforms. Verification tests show that this approach could characterize various aggregate properties in a fast, accurate, and reliable way. When implemented, this ability to automatically analyze multiple characteristics of an aggregate sample is expected to provide not only economic but also intangible strategic gains.