Weak form equation–based finite-element modeling of viscoelastic asphalt mixtures

The objective of this study is to demonstrate using weak form partial differential equation (PDE) method for a finite-element (FE) modeling of a new constitutive relation without the need of user subroutine programming. The viscoelastic asphalt mixtures were modeled by the weak form PDE-based FE method as the examples in the paper. A solid-like generalized Maxwell model was used to represent the deforming mechanism of a viscoelastic material, the constitutive relations of which were derived and implemented in the weak form PDE module of Comsol Multiphysics, a commercial FE program. The weak form PDE modeling of viscoelasticity was verified by comparing Comsol and Abaqus simulations, which employed the same loading configurations and material property inputs in virtual laboratory test simulations. Both produced identical results in terms of axial and radial strain responses. The weak form PDE modeling of viscoelasticity was further validated by comparing the weak form PDE predictions with real laboratory test results of six types of asphalt mixtures with two air void contents and three aging periods. The viscoelastic material properties such as the coefficients of a Prony series model for the relaxation modulus were obtained by converting from the master curves of dynamic modulus and phase angle. Strain responses of compressive creep tests at three temperatures and cyclic load tests were predicted using the weak form PDE modeling and found to be comparable with the measurements of the real laboratory tests. It was demonstrated that the weak form PDE-based FE modeling can serve as an efficient method to implement new constitutive models and can free engineers from user subroutine programming.

Rutting-induced permeability loss of open graded friction course mixtures

Functionality of an open graded friction course (OGFC) depends on the high interconnected air voids or pores of the OGFC mixture. The authors' previous study indicated that the pores in the OGFC mixture were easily clogged by rutting deformation. Such a deformation-related clogging can cause a significant rutting-induced permeability loss in the OGFC mixture. The objective of this study was to control and reduce the rutting-induced permeability loss of the OGFC based on mixture design and layer thickness. Eight types of the OGFC mixtures with different air void contents, gradations, and nominal maximum aggregate sizes were fabricated in the laboratory. Wheel-tracking rutting tests were conducted on the OGFC slabs to simulate the deformation-related clogging. Permeability tests after different wheel load applications were performed on the rutted OGFC slabs using a falling head permeameter developed in the authors' previous study. The relationships between permeability loss and rutting depth as well as dynamic stability were developed based on the eight OGFC mixtures' test results. The thickness effects of the single-layer and the two-layer OGFC slabs were also discussed in terms of deformation-related clogging and the rutting-induced permeability loss. Results showed that the permeability coefficient decreases linearly with an increasing rutting depth of the OGFC mixtures. Rutting depth was recommended as a design index to control permeability loss of the OGFC mixture rather than the dynamic stability. Permeability loss due to deformation-related clogging can be effectively reduced by using a thicker single-layer OGFC or two-layer OGFC.

Effect of mixing sequence on the workability and performance of asphalt mixtures

In Sweden, during recent years, a new type of mixing protocol has been applied, in which the order of mixing is changed from the conventional method. Improved workability and diminished mixing and compaction energy needs have been important drivers for this. Considering that it is the mastic phase, which is modified by changing the mixing order, it provides an interesting case study for explaining the mechanisms of workability in connection with the mastic phase. To do so, an analytical viscosity framework was combined with a mixture morphology framework to upscale to the mixing level and tribology principles to explain the interaction between the mastic and the aggregates. From the mastic viscosity protocol, it was found that the mixing order significantly affects the resulting mastic viscosity. To analyse the effect of this on the workability and resulting mixture performance, X-ray computed tomography was used to analyse mixtures produced by the two different mixing sequences. Mechanical testing was utilised to determine the long-term mechanical performance. In this part of the study, mastic viscosity as a function of particle concentration and distribution was directly coupled to improved mixture workability and enhanced long-term performance.

Implementation of pseudo J-integral based Paris’ law for fatigue cracking in asphalt mixtures and pavements

Pavement analysis and design for fatigue cracking involves a number of practical problems like material assessment/screening and performance prediction. A mechanics-aided method can answer these questions with satisfactory accuracy in a convenient way when it is appropriately implemented. This paper presents two techniques to implement the pseudo J-integral based Paris’ law to evaluate and predict fatigue cracking in asphalt mixtures and pavements. The first technique, quasi-elastic simulation, provides a rational and appropriate reference modulus for the pseudo analysis (i.e., viscoelastic to elastic conversion) by making use of the widely used material property: dynamic modulus. The physical significance of the quasi-elastic simulation is clarified. Introduction of this technique facilitates the implementation of the fracture mechanics models as well as continuum damage mechanics models to characterize fatigue cracking in asphalt pavements. The second technique about modeling fracture coefficients of the pseudo J-integral based Paris’ law simplifies the prediction of fatigue cracking without performing fatigue tests. The developed prediction models for the fracture coefficients rely on readily available mixture design properties that directly affect the fatigue performance, including the relaxation modulus, air void content, asphalt binder content, and aggregate gradation. Sufficient data are collected to develop such prediction models and the R2 values are around 0.9. The presented case studies serve as examples to illustrate how the pseudo J-integral based Paris’ law predicts fatigue resistance of asphalt mixtures and assesses fatigue performance of asphalt pavements. Future applications include the estimation of fatigue life of asphalt mixtures/pavements through a distinct criterion that defines fatigue failure by its physical significance.

Simple approximate MAP inference for Dirichlet processes mixtures

The Dirichlet process mixture model (DPMM) is a ubiquitous, flexible Bayesian nonparametric statistical model. However, full probabilistic inference in this model is analytically intractable, so that computationally intensive techniques such as Gibbs sampling are required. As a result, DPMM-based methods, which have considerable potential, are restricted to applications in which computational resources and time for inference is plentiful. For example, they would not be practical for digital signal processing on embedded hardware, where computational resources are at a serious premium. Here, we develop a simplified yet statistically rigorous approximate maximum a-posteriori (MAP) inference algorithm for DPMMs. This algorithm is as simple as DP-means clustering, solves the MAP problem as well as Gibbs sampling, while requiring only a fraction of the computational effort. (For freely available code that implements the MAP-DP algorithm for Gaussian mixtures see http://www.maxlittle.net/.) Unlike related small variance asymptotics (SVA), our method is non-degenerate and so inherits the “rich get richer” property of the Dirichlet process. It also retains a non-degenerate closed-form likelihood which enables out-of-sample calculations and the use of standard tools such as cross-validation. We illustrate the benefits of our algorithm on a range of examples and contrast it to variational, SVA and sampling approaches from both a computational complexity perspective as well as in terms of clustering performance. We demonstrate the wide applicabiity of our approach by presenting an approximate MAP inference method for the infinite hidden Markov model whose performance contrasts favorably with a recently proposed hybrid SVA approach. Similarly, we show how our algorithm can applied to a semiparametric mixed-effects regression model where the random effects distribution is modelled using an infinite mixture model, as used in longitudinal progression modelling in population health science. Finally, we propose directions for future research on approximate MAP inference in Bayesian nonparametrics.