The Environmental Conditioning System (ECS) was designed to evaluate the water sensitivity of asphalt concrete mixtures. The ECS subjects asphalt concrete specimens to a series of conditioning cycles including water flow, elevated and/or lowered temperature, and repeated axial loading. The purpose of this research was to: (1) evaluate the ECS test apparatus and procedure, and (2) determine whether the ECS can identify asphalt concrete mixtures that will perform well, or poorly, in the field with regard to water sensitivity. Twelve primary field test sections were identified. For each section, specimens were prepared in the laboratory using the original mix design (or the mix design identified by extraction), and the original aggregates, asphalts, and admixtures. Specimens were tested using two procedures: the ECS and the Oregon State University (OSU) wheel tracker. Field cores were used to evaluate in-situ mixture performance. Nine additional mixtures that have historically experienced water damage were tested in a limited secondary test program. Analyses were performed to determine the mixture properties that were significant in the prediction of mixture performance in the ECS. Mixture type was consistently the most significant predictor of ECS modulus ratio (change in mixture stiffness), degree of visual stripping, and binder migration, which were the performance indicators for water sensitivity evaluated in the ECS. Additional analysis indicated the existence of correlations among the ECS response variables. Significant correlations were found between the coefficient of water permeability and the degree of visual stripping; and between specimen deformation and the degree of visual stripping and binder migration. Mixture performance was compared between the ECS and the OSU wheel tracker and the field. Results indicate that the ECS test procedure can distinguish the relative performance of mixtures, with regard to water sensitivity, and mixture performance in the ECS correlates well with performance in the OSU wheel tracker. No correlation was found between mixture performance in the ECS and mixture performance in the field for the primary test sections. However, the primary field sections are relatively young, and water damage is expected to manifest itself in the future in those pavements identified as water sensitive by the ECS. The ECS predicted failure in the secondary mixtures which were identified as having had poor performance with regard to water sensitivity. / Graduation date: 1994

Moisture from concrete has been estimated to be responsible for over $1 billion annually from damages in floor coverings. To prevent damages, flooring manufacturers require installers to test concrete moisture levels to determine if the concrete has dried sufficiently to receive flooring or covering. Two of the main tests used in the United States to determine concrete moisture levels are moisture vapor emissions rate (MVER) tests and relative humidity (RH) tests. Changes in ambient temperature can affect the results of both RH and MVER tests. The goal of this study was to investigate the effects of ambient temperature changes on the RH of concrete, and compare the sensitivity of RH measurements to the results of MVER tests at the same ambient temperature. The RH of concrete was measured at 20%, 40%, 60%, and 80% of depth in each sample and tracked over a period of 24 days to develop drying curves at each depth, and drying profiles of each sample. The changes in concrete RH due to a change in ambient temperature were predicted using the psychrometric process and a model developed during this study. Due to size constraints on the concrete samples, ASTM 1869 had to be altered during the MVER tests. Typical RH change in the concrete samples was under 4% RH after either an increase or decrease in an ambient temperature of 5.5°C (10°F). The psychrometric process predicted that the concrete RH would change between 20% - 40% RH after the ambient temperature changed by 5.5°C. Psychrometric properties were not able to full describe the behavior of air in concrete pores so a new model was created to better predict the change in concrete RH after a change in ambient temperature. The developed model was able to predict concrete RH change within 5% error over the range of tested temperatures. / Master of Science