Asphalt pavement modulus backcalculation using surface deflections under moving loads

The aggregate skeleton significantly influences the deformation resistance of asphalt mixtures. A stable skeleton has good interlocking effects and load transfer ability to resist deformation of mixtures during loading. Therefore, the methodology to accurately evaluate the morphology of aggregate skeleton based on the mechanism of load transfer inside mixtures is of great value to predict the mixture stability. The objective of this study is to predict and validate mixture stability from the perspective of evolutional morphologies of the skeleton.The methodology has three main steps, which are as follows: (1) the initial morphology of the aggregate skeleton is characterized and evaluated; (2) the topological and non-topological evolutions of the skeleton morphology during external loading are characterized; and (3) the strain energy of the mixture and the stress distribution on contact regions in the skeleton are used to analyze and validate the prediction of the mixture's stability. Simulations of uniaxial displacementcontrolled tests of three cylindrical specimens drilled in a field-compacted test track were conducted, and 7.5, 15, 22.5, and 30 s were selected as time points in the simulation to analyze mixture stability. Results indicate that a mixture has a better load-bearing capacity when its initial skeleton contains more chains with higher evaluation indices, which proves the reliability of the stability prediction using the proposed method.

Section: Introductionmentioning

confidence: 99%

Stability prediction for asphalt mixture based on evolutional characterization of aggregate skeleton

Jin

Wan

Liu

et al. 2021

“…A broad range of civil engineering problems may eventually lead to an estimation task including, but not limited to, structural system identification (Yuen et al., 2019), model updating (Song et al., 2020), structural health monitoring (SHM) (Papadimitriou et al., 2011; Sirca & Adeli, 2012), traffic engineering (Sessa et al., 2020), pavement engineering (Wu et al., 2020), water resources engineering (Shokri et al., 2018), land data assimilation (Reichle et al., 2002), and environmental engineering (Rüdiger et al., 2010). The major objective is to estimate/identify quantities of interest by a restricted number of observations from the measured system outputs, as the state of the system can oftentimes be partially observed in practice due to technical and economic limitations.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian smoothing for input‐state estimation of structural systems

Hassanabadi

Heidarpour

Azam

et al. 2021

Instantaneous output‐only inversion of a system with delayed appearance of input influences on the measured outputs via filtering methods suffer from intensive amplification of the observation noise in the estimated quantities due to the ill‐conditionedness. To remedy this issue, in this paper, a new unbiased recursive Bayesian smoothing method is developed for input‐state estimation of linear systems without direct feedthrough to reduce estimation uncertainty through an extended observation equation. By minimizing input and state estimation error variance, the optimal smoothing input and state gain matrices are derived. Moreover, a new efficient method is proposed for the recursive calculation of correlation of state estimation error with modeling and observation noise vectors.

“…Based on different traffic loadings and service requirements of pavements, various asphalt mixtures with different aggregate gradation types can be selected, including dense‐graded (e.g., asphalt concrete, known as AC), gap‐graded (e.g., stone mastic asphalt, known as SMA), and open‐graded (e.g., open‐graded friction course; Lou et al., 2021; Mota et al., 2021; Polaczyk et al., 2021; Qian et al., 2021; Tran & Takahashi, 2019). In engineering practice, these types of mixtures are generally regarded as homogeneous, since their macroscopic properties can be characterized by laboratory tests, and the performance of the pavements is further predicted using these measured properties (Wu et al., 2020). For example, researchers have proposed a great number of standard specifications for asphaltic material testing and developed the mechanical‐empirical methods for pavement design (ARA, 2004; Dong et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

Characterizing asphalt mixtures with random aggregate gradations based on the three‐dimensional locally homogeneous model

Liu

Sun

et al. 2021

A three-dimensional (3-D) locally homogeneous model for asphalt mixtures is presented in this study, which can effectively balance the simulating accuracy and computational effort. Moreover, the locally homogeneous model accounts for various aggregate gradations to allow for the mesoscale analysis of the asphalt concrete (AC) under loading. In the first step of the locally homogeneous model development, weighted Voronoi tessellation technology was employed to divide the volume of asphalt mixture into several local parts in terms of the aggregate gradations and random particle locations with each part containing one or a few aggregates particles. Subsequently, the mesomechanical Mori-Tanaka method was used to calculate the homogeneous mechanical properties for each local space. Finally, 3-D models of asphalt mixtures were developed, which were further used for numerical simulations. The finite element method was used for the asphalt mixture mesoscale investigations. Two asphalt mixtures were studied consisting of dense-and gap-graded aggregates and labeled, respectively, AC-13 and stone mastic asphalt (SMA)-11. The efficacy of the numerical analysis was demonstrated by comparing model-generated responses and performance with compressive dynamic loadings test results. The models demonstrated the ability to characterize the linear viscoelastic performance for both asphalt mixtures. Furthermore, the locally homogeneous models of AC-13 and SMA-11 mixtures were coupled into global asphalt pavements to identify the influence of aggregate particles on their mechanical responses. The results indicated that the locally homogeneous models are able to consider both the pavement structure at the global scale and the internal feature of asphalt mixtures at the local scale.