Proportioning of the constituents of pervious concrete (PC) mixtures is an intricate task, as it is sensitive to the balance of two intrinsic properties: air voids content and compressive strength. The research presented in this paper proposes a novel volumetrics-based method for proportioning PC mixtures by evaluating the relationships between and the impact of the aggregates volume needed to form the skeleton of the mix, the mortar volume needed to coat those aggregates, and the bridge between them, in addition to the air voids content. The paper also examines the effect of mortar constituents, PC specimen diameter, and the characteristics of the mortar bridging the aggregates on the proportioning and mechanical properties of PC mixes. The study shows that the compressive strength of PC is mainly governed by the volume of mortar between the aggregates, in addition to the air voids content, and not by the mechanical properties of the mortar. The study also recommends that specimens with a diameter of 150 mm be used to measure the void content and compressive strength of PC. Finally, a mathematical model that relates the compressive strength of PC to its void content is proposed. The model can be used to predict the mean compressive strength of PC for air voids content ranging from 15% to 43%.

Pavement engineers and practitioners have come to recognize the urgent need to quantify the variability in dynamic modulus, | E*|, because of its influence on the predicted performance of asphalt pavements and to adopt realistic quality assurance or quality control measures associated with pavement construction. The objective of this study was to characterize the inherent variability in | E*| across the full spectrum of the | E*| master curve (fitted with a sigmoidal function for various reduced frequencies). The study analyzed | E*| data from six mixes that included at least eight replicates within a robust probabilistic framework that allowed for a preliminary quantification of uncertainty caused by the inherent variability in | E*|. Monte Carlo simulations were used to propagate the uncertainties of the sigmoidal model coefficients to determine the mean, coefficient of variation, and probability distribution of | E*| as a function of reduced frequency. In addition, the inherent uncertainty in | E*| was propagated through forward modeling to characterize the resulting uncertainty in the predicted rut depth in the asphalt layer for a set of pavement structures. The findings show that the values of the inherent uncertainty of | E*| are relatively small for cases with reduced frequencies that are high but increase dramatically for reduced frequencies that are in the medium to low range. This uncertainty increases as the nominal maximum aggregate size (NMAS) of the mix under investigation increases. It was found that the uncertainty significantly affects the probability distribution of rut depth and implies higher variability for cases of hot weather, slow traffic, or large NMAS.

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The main objective of this paper is to develop a realistic probabilistic framework for characterization of different types of asphalt concrete using advanced material modeling. The adopted methodology builds on and enhances a viscoelastic continuum damage (VECD) material model by utilizing a suite of associated experimental testing protocols and incorporating the uncertainties associated with the different material properties. The modeled uncertainties address the variabilities and errors associated with the linear viscoelastic (LVE) functions achieved from the complex modulus test and damage characteristic curves obtained from constant crosshead rate testing. A probabilistic scheme using First Order approximations and Monte Carlo simulations is developed to characterize the inherent uncertainty of each of the LVE functions over the time domain of their mastercurves. For damage characteristic curves, the uncertainty in normalized pseudostiffness increases as the level of damage becomes larger. The uncertainties of LVE properties are propagated along with those of C versus stress curves to yield a probabilistic viscoelastic continuum damage model (P-VECD). The P-VECD not only predicts the average viscoelastic response to a given loading input, but it can also provide its distribution, which is essential for a reliability-based pavement design.

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