The influence of fibers on the fatigue cracking resistance of asphalt concrete is investigated using fracture energy. Nylon, a popular facing yarn of carpets, is used for the actual recycled carpet fibers in asphalt pavement. The experimental program is designed with two phases: the single fiber pull-out test and the indirect tension strength test. Through pull-out tests of 15-denier single nylon fibers, the critical fiber embedded length is determined to be 9.2 mm. As for indirect tension strength tests, samples of asphalt concrete mixed with nylon fibers of two lengths, 6 and 12 mm, based on results of the pull-out tests (critical embedded length) and three volume fractions, 0.25, 0.5, and 1%, are prepared and tested. Asphalt concrete samples fabricated with fibers of 1% and 12 mm results in 85% higher fracture energy than non-reinforced specimens, showing improved fatigue cracking resistance. Although an optimized asphalt mix design with fibers has not been developed for this study, the increased fracture energy represents a potential for improving asphalt fatigue life, which may be facilitated through the use of recycled carpet fibers.

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Researchers developing the MEPDG implemented a hierarchical input structure in recognition of the fact that ΗE*Η values for materials used in a particular design might not be available when the analysis is performed. In the lowest level of this structure, users may choose to employ a predictive equation that is based on mixture volumetric and binder properties to predict the mixture modulus (1). In addition to the original predictive model, the Witczak model, others have developed predictive models. Those that have gained some national interest are the Hirsch model (3), a modified form of Witczak's original model (4) referred to as the modified Witczak model, and a modified form of the Hirsch model referred to as the Al-Khateeb model (5). Various researchers have reviewed these models by using independent data sets and concluded that, while both have advantages and disadvantages in relation to necessary inputs and ease of use, the predictive capabilities of each could be improved (6-9).The North Carolina State University (NCSU) research team is currently developing a more robust Η E*Η prediction algorithm with the goal of using the model to predict the moduli of certain layers in the Long-Term Pavement Performance (LTPP) database. The team's current research focuses on improving the prediction accuracy by using the artificial neural network (ANN) technique. Such an approach has been used successfully in the pavement field for such diverse problems as moduli backcalculation (10-12) and predictions of roughness deterioration (13). The primary advantage of this approach over statistical regression techniques is that the functional form of the relationship is not needed a priori. When one considers that many individual and interacting variables affect the ΗE*Η values, such an advantage may be significant and may offer the potential for capturing complicated nonlinear relationships between the ΗE*Η and mixture variables better than regression analysis. The drawback most often cited to these approaches is their inability to extrapolate when a situation arises that is beyond those used to train the ANN. This shortcoming is overcome by using an extensive training data set that covers the entire range of expected conditions. The use of ANN techniques for predicting the mixture Η E This paper presents outcomes from a research effort to develop models for estimating the dynamic modulus (ͦ E*ͦ ) of hot-mix asphalt (HMA) layers on long-term pavement performance test sections. The goal of the work is the development of a new, rational, and effective set of dynamic modulus ͦ E*ͦ predictive models for HMA mixtures. These predictive models use artificial neural networks (ANNs) trained with the same set of parameters used in other popular predictive equations: the modified Witczak and Hirsch models. The main advantage of using ANNs for predicting ͦE*ͦ is that an ANN can be created for different sets of variables without knowing the form of the predictive relationship a priori. The primary disadvantage of ANNs is the difficulty in ...

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This paper presents a set of dynamic modulus (|E*|) predictive models to estimate the |E*| of hot-mix asphalt layers in long-term pavement performance (LTPP) test sections. These predictive models use artificial neural networks (ANNs) trained with different sets of parameters. A large national data set that covers a substantial range of potential input conditions was utilized to train and verify the ANNs. The data consist of mixture dynamic moduli measured with two test protocols: the asphalt mixture performance tester and AASHTO TP-62, under different aging conditions. The data include binder dynamic moduli values measured under different aging conditions. The ANN predictive models were trained and ranked with a common independent data set that was not used for calibrating any of the ANN models. A decision tree was developed from these rankings to prioritize the models for any available inputs. Next, the models were used to estimate the |E*| for the LTPP database materials and ultimately to characterize the master curve and shift factor function. To ensure adequate data quality, a series of quality control checks was developed and applied to grade the inputs and outputs for each prediction. Approximately 30% to 50% of all LTPP layers contained enough information to obtain reliable moduli predictions.