Reed B. Freeman

Imaging indices for quantification of shape, angularity, and surface texture of aggregates

Three image indices are presented that characterize aggregates in a hierarchical scheme involving overall shape (or form), roundness of corners, and particle surface texture. The corresponding dimensionless image indices are called aspect ratio, angularity, and roughness, respectively. Calculating these indices requires that image-analysis techniques be used to find the following data for a sample of two-dimensional particle images: shortest and longest dimensions, perimeters, convex perimeters, and perimeters of ellipses with areas equivalent to those of the associated particles. To demonstrate the usefulness of these three dimensionless indices, selected sieve-size fractions for 25 fine-aggregate sources were subjected to image-analysis testing and to uncompacted void testing (ASTM C1252). Both uncompacted void contents and image-analysis indices could generally differentiate between natural sands and crushed fine aggregates. However, the image-analysis indices provided additional useful information. For example, the image-analysis results identified that two sources of crushed fine aggregates had relatively low uncompacted void contents because they had relatively smooth surface textures or relatively low aspect ratios. In addition, the image-analysis results were able to distinguish morphological differences between aggregate sources that were measured to have similar uncompacted void contents.

USING ARTIFICIAL NEURAL NETWORKS AS A FORWARD APPROACH TO BACKCALCULATION

In recent years, artificial neural networks have successfully been trained to backcalculate pavement layer moduli from the results of falling weight deflectometer (FWD) tests. These neural networks provide the same solutions as existing programs, only thousands of times faster. Unfortunately, their use is constrained to the test conditions assumed during network training. These limitations arise from practical aspects of neural network training and cannot be circumvented easily. The goal of this research was to develop a backcalculation program combining the speed of neural networks and the flexibility of conventional programs to produce the same solutions as existing programs. This was accomplished by forgoing neural network backcalculation in favor of neural network forward-calculation, that is, using neural networks in place of complex numerical models for computing the forward-problem solutions used by the conventional backcalculation programs. A suite of neural networks, covering a range of flexible pavement structures, was trained using data generated by WESLEA, the forward-problem solver used in the WESDEF backcalculation program. When tested on 110 experimental FWD results, a version of WESDEF augmented by the neural networks provided statistically identical answers 42 times faster, on average, than the original. Provisions have been made for periodic upgrades as additional networks are trained for other pavement types and test conditions. Meanwhile, the original WESLEA can still be used when an appropriate network is unavailable. This preserves the flexibility of the original program while taking maximum advantage of the speed gains afforded by the neural networks.

Image Analysis Evaluation of Aggregates for Asphalt Concrete Mixtures

The performance of asphalt concrete mixtures is influenced by the properties of the included aggregates, such as grading, shape (angularity and elongation), and texture (roughness). Complete and accurate quantification of aggregate properties is essential for understanding their influence on asphalt concrete and for selecting aggregates to produce high-quality paving mixtures. Recent developments in the use of digital image analysis techniques for quantifying aggregate morphological characteristics in asphalt concrete are summarized. Image morphological characteristics were used to quantify flatness and elongation of coarse aggregates, to estimate the proportion of natural sand in fine aggregates, and to correlate aggregate characteristics with engineering properties of asphalt concrete mixtures. Image analysis of sections also revealed information about the grading, shape, and orientation of coarse aggregates in a mixture. An overview is presented of the broad range of useful pavement engineering applications of this relatively new approach for evaluating aggregate characteristics.

COST-EFFECTIVENESS OF CRACK SEALING MATERIALS AND TECHNIQUES FOR ASPHALT PAVEMENTS

Sealing or filling cracked asphalt pavements to prevent the intrusion of water into the pavement structure has long been an accepted practice of the Montana Department of Transportation. Attempts were made to establish the most economical and effective method of sealing pavement cracks for Montana and to better determine crack sealing’s role within Montana’s pavement management system (PvMS). Four experimental test sites were constructed within crack-sealing projects. The test sites included combinations of nine sealant materials and six sealing techniques. Monitoring of the test sites includes visual inspections (for all of the sites) and nondestructive structural readings and surface distress identification under Montana’s PvMS (for one test location). One expectation of the inspections is an estimation of crack sealing’s useful life. Information on project history and project methodology, including the methods used for evaluating the performance of sealed cracks, is presented. Interim conclusions are presented, most of which have been obtained from the two test sites that have been in service for 3 years. Similar performance has been observed for all materials with ASTM D5329 cone penetrations in excess of 90. In general, routing of transverse cracks improved the performance of the sealants. Routing did not appear necessary for centerline longitudinal cracks. Observations from the first year of service for the most recent installation are noted. Notably, router operators appear to prefer the shallow reservoir configuration over square reservoirs. The highest failure rates occur during the coldest period of the year, and much of this distress “heals” after exposure to the summer heat.

Determining Rapid-Setting Material Suitability for Expedient Pavement Repairs: Full-Scale Traffic Tests and Laboratory Testing Protocol

Numerous commercial off-the-shelf products have become available for small surface repairs in portland cement concrete (PCC) pavements, providing short set times, high early strengths, and good durability to withstand heavy loads. These materials have been used successfully for both small repairs in the transportation industry and industrial applications in which the repair was less than 0.03 m3 (1 ft3). Applying these products for larger-volume repairs while still achieving a traffickable surface within 3 h of pavement repair has been a challenge. Experience gained from testing these products for full-depth repairs provides guidance for repair techniques that will expedite opening airfield or highway pavements to traffic and minimize the frequency of maintenance activities. An investigation conducted at the U.S. Army Engineer Research and Development Center, in Vicksburg, Mississippi, examined nine rapid-setting materials for repair of PCC pavements through laboratory characterization and full-scale traffic tests. Standard laboratory tests were performed to characterize the material properties over time and to provide a mechanism for assessing the material suitability for field repairs. Repairs with approximate volumes of 0.7 m3 (1.5 yd3) were constructed and evaluated under controlled traffic conditions to determine the ability of the repairs to support 100 simulated passes of an F-15E aircraft within 3 h of repair. Results of traffic tests identified seven repair materials that met these criteria. A laboratory protocol for selection of rapid-setting materials was developed based on the laboratory and full-scale test results. Use of the protocol will help prevent the selection of materials that are unlikely to meet performance expectations.