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As part of the validation effort to evaluate the consistency of a new cold in-place recycling using foamed asphalt (CIR–foam) mix design process for the Iowa Department of Transportation, the dynamic modulus and repeated load tests were conducted to evaluate the performance characteristics of CIR–foam mixtures over a wide range of loading and temperature conditions. The main objective of this research is to study the impacts of the foamed asphalt contents and reclaimed asphalt pavement (RAP) material characteristics on dynamic modulus and flow number. Dynamic modulus and repeated load tests were conducted on CIR–foam mixtures with RAP materials collected from seven different CIR project sites located throughout Iowa. Dynamic modulus values of the RAP materials varied depending on the sources and foamed asphalt contents. Coarse RAP materials with a small amount of residual asphalt content exhibited a higher modulus value at 4.4°C but exhibited a lower dynamic modulus value at 37.8°C than fine RAP materials with a large amount of residual asphalt content. Flow numbers were varied significantly depending on their RAP sources and foamed asphalt contents. Fine RAP materials with a hard residual asphalt binder exhibited a higher flow number than coarse RAP materials with a soft residual binder. The relative rankings of RAP materials in terms of the flow number did not change when the foamed asphalt was increased from 1.0 to 3.0%, which supports the repeated load test being consistent in evaluating a rutting potential of RAP materials. [ABSTRACT FROM AUTHOR]

This research work investigates approaches for computing the average asphalt film thickness in asphalt mixtures and examines whether the in-place asphalt film thickness has a rational relationship with the performance of hot-mix asphalt mixtures. Two methods for calculating the aggregate surface areas, the surface area factor and index methods, are discussed in this research and the analytical results show that both the fine aggregate particles and aggregate shapes significantly affect the calculation and should be taken into consideration to obtain greater accuracy for the surface area. A computation method is proposed and significantly improves the current conventional method for the asphalt film thickness calculation by incorporating shape factors and flat surface factors for all sieve sizes. Field performance data from MnROAD and rutting data from laboratory fabricated mixtures, including both coarse and fine gradations, were analyzed to investigate the asphalt film thickness and corresponding performance of asphalt mixtures. Both the field data and laboratory experimental results show that the asphalt film thickness is a significant factor affecting the rutting performance for asphalt mixtures. However, more research work is needed to investigate the relationship between the asphalt film thickness and the other performance parameters of asphalt mixtures, such as fatigue cracking, before a film thickness specification can be proposed. [ABSTRACT FROM AUTHOR]

Various models have been developed over the past several decades to predict the dynamic modulus |E*| of hot-mix asphalt (HMA) based on regression analysis of laboratory measurements. The models most widely used in the asphalt community today are the Witczak 1999 and 2006 predictive models. Although the overall predictive accuracies for these existing models as reported by their developers are quite high, the models generally tend to overemphasize the influence of temperature and understate the influence of other mixture characteristics. Model accuracy also tends to fall off at the low and high temperature extremes. Recently, researchers at Iowa State Univ. have developed a novel approach for predicting HMA |E*| using an artificial neural network (ANN) methodology. This paper discusses the accuracy and robustness of the various predictive models (Witczak 1999 and 2006 and ANN-based models) for estimating the HMA |E*| inputs needed for the new mechanistic-empirical pavement design guide. The ANN-based |E*| models using the same input variables exhibit significantly better overall prediction accuracy, better local accuracy at high and low temperature extremes, less prediction bias, and better balance between temperature and mixture influences than do their regression-based counterparts. As a consequence, the ANN models as a group are better able to rank mixtures in the same order as measured |E*| for fixed (e.g., project-specific) environmental and design traffic conditions. The ANN models as a group also produced the best agreement between predicted rutting and alligator cracking computed using predicted versus measured |E*| values for a typical pavement scenario. [ABSTRACT FROM AUTHOR]