This paper investigates the effect of oxidative aging of asphalt binders on their material characteristics and fatigue fracture properties during controlled-strain, dynamic mechanical testing. Cylindrical sand asphalt samples were fabricated with an unaged and two aged binders at different levels of aging. Various dynamic mechanical tests (strain sweep, frequency sweep, and time sweep) were performed to estimate viscoelastic characteristics and fatigue behavior. Test results and mechanical analyses conclude that the oxidative aging clearly increased elastic characteristics and reduced stress relaxation capability of asphalt binders. Furthermore, the aging has impact on the changes of thermo-viscoelastic properties. Due to the increasing brittleness and less viscous damping ability, the aged mixtures approached fatigue failure faster than the unaged mixtures and did not show any significant microdamage healing effects. Dynamic mechanical testing results showed good agreements with surface energy measurements that are well-known indicators representing fracture and healing potential of asphalt binders.

Fracture damage mechanisms are some of the most significant causes of structural failure in asphalt mixtures. Ye t much research is still needed to understand properly the fracture process in such complex materials. The study reported in this paper investigated several experimental testing protocols available in the literature to characterize fracture properties of asphalt mixtures. Two bending tests (i.e., semicircular bending, single-edge notched beam) and one tension test (disk-shaped compact tension) were performed. An integrated approach that combined experimental tests and numerical simulations was applied to characterize fracture properties of a fine aggregate mixture. The experimental tests were simulated with a computational model on the basis of the finite element method, which was incorporated with material viscoelasticity and cohesive zone fracture. Two cohesive zone fracture parameters (i.e., cohesive strength, fracture energy) were determined through a calibration process until a good match between experimental and numerical results was observed. To illustrate the efficiency of the integrated numerical–experimental approach, fracture properties also were determined through a traditional methodology that used globally averaged material displacements far from the actual fracture process zone. The results indicated that different fracture properties at low temperatures might be obtained from simulations of a single test, regardless of the sample geometry or loading configuration. Through further testing and analysis, it is expected that the modeling approach employed in this work can provide meaningful insights into the effects of constituents on an overall mixture's performance, with significant savings in experimental cost and time.

Cracking of bituminous materials is one of the main distresses that results in roadway failure. As bituminous media are highly rate-dependent at intermediate temperatures due to the viscoelastic nature of the binding materials, cracking is also highly rate-dependent viscoelastic. This presents a clear need to address the phenomenon in the modeling-analysis process for a more accurate design of mixtures and pavements. This study proposes an advanced computational modeling method to predict complex rate-dependent cracking in bituminous materials and pavements. In particular, we explored an extrinsic nonlinear viscoelastic cohesive zone (NVCZ) integrated with Gaussian damage evolution. To examine the modeling method and its validity, two modeling efforts in multiple length scales were made: mm-scale material-level modeling and cm-m scale hierarchical modeling that linked the mixture with pavement structure. The NVCZ modeling with Gaussian damage evolution successfully predicted material cracking and damage at different loading rates by using a single set of fracture parameters. This implies that the complex cracking behavior of bituminous materials can be characterized by material-specific fracture parameters that can be identified by a simple fracture test. The pavement modeling through a parametric analysis demonstrated the sensitivity and capability of the modeling to effectively capture the interrelated influence of several core design variables, such as traffic, material properties, and layer configurations, all of which affect pavement responses and damage evolution. The computational modeling presented in this study has the scientific rigor to predict nonlinear rate-dependent viscoelastic fracture of bituminous materials and pavements with a good modeling efficiency by significantly reducing laboratory tests.

The accurate modeling of water and heat transport in soils is crucial for both geo-environmental and geothermal engineering. Traditional modeling methods are problematic because they require well-defined boundaries and initial conditions. Recently, physics-informed neural networks (PINNs), which incorporate partial differential equations (PDEs) to solve forward and inverse problems, have attracted increasing attention in machine learning research. In this study, we applied PINNs to tackle hydraulic and thermal transport coupling forward problems in silty sands. A fully connected deep neural network was utilized for training. This neural network model leverages automatic differentiation to apply the governing equations as constraints, based on the mathematical approximations established by the neural network itself. We conducted forward problems and compared the solutions derived from PINNs with those from Finite Element Method (FEM) simulations. The forward problem results demonstrate the PINNs model’s capability in predicting hydraulic transport, heat transport, and thermal–hydraulic coupling in silty sands under various boundary conditions. The PINNs exhibited great performance in simulating the thermal–hydraulic coupling problem. The accuracy of the PINNs solutions shows its potential for simulation in geotechnical engineering.