Asphalt mixtures show complex mechanical behavior due to their heterogeneous structure. Traditionally, the mechanical characterization of asphalt mixture is done through laboratory testing or micromechanical modeling. While laboratory tests and micromechanical models provide reliable measurements and physical interpretability, they are often resource-intensive and demand extensive calibration. Recent advances in machine learning address some of the above issues by enabling accurate predictions, though often lacking physical interpretability and stability. Hence, this study aims to present a novel micromechanics-infused neural network (MINN) framework for predicting asphalt mixture stiffness. The framework embeds micromechanical principles derived from the modified Hirsch model into the neural network's loss function, allowing the model to learn from experimental data while adhering to micromechanics-based constraints. In this study, feature selection is performed using BorutaShap, and Bayesian optimization is applied for hyperparameter tuning. Results show that MINN improves prediction accuracy, interpretability, and robustness.

A major contributor to GHG emissions is the transportation sector, particularly pavement transport. The limited understanding of tire-pavement interactions leads to inaccurate predictions of these emissions, particularly from rolling resistance (RR). Traditional methods for predicting RR are constrained by their limited applicability and inability to account for the complex dynamics of tire-pavement interactions, resulting in poor prediction accuracy. These limitations make it challenging for policymakers to make proper decisions, as existing methods are manual and labour-intensive. This study aims to develop an automated system to capture tire-pavement interaction data using the Laser Crack Measurement System (LCMS). To the best of the authors' knowledge, no robust technique currently exists for automatically calculating tire penetration-related information from LCMS data to predict RR. Therefore, this research explores machine learning (ML)-based models to reduce uncertainties in existing approaches and enhance RR predictions using automated LCMS data. It examines the relationships between RR, tire penetration volume, and the characteristics of the Dutch pavement network, comparing the results with those of commonly used RR prediction models. The study introduces an automatic tire penetration calculation approach using LCMS data to assess the impact of tire penetration volume and depth on RR in relation to surface properties. The findings reveal that traditional empirical models show poor correlations between RR and texture indicators, whereas ML-based models significantly improve the accuracy of RR predictions. These results could inform the development of strategies to reduce GHG emissions from pavement transport, supporting global efforts to combat climate change and achieve the goals of the Paris Agreement.

Asphalt binders are critical for asphalt pavement performance, and understanding their rheological behavior is essential for designing durable roadways. The complex shear modulus (G⁎) and phase angle (δ) are primary parameters characterizing binder rheology. This study introduces a novel hybrid machine learning model combining deep neural networks (DNN) and Gaussian process regression (GPR) to predict G⁎ and δ for bituminous binders and binder-filler systems (mastics). DNN excel at capturing complex, nonlinear relationships among eleven binder and thirteen mastic input parameters, including aging conditions, chemical and physical properties, and test parameters. However, standalone DNN struggle with small datasets, common at the binder scale, and lack inherent uncertainty quantification, limiting reliability in engineering applications. GPR improves DNN by refining predictions through probabilistic modeling, while providing uncertainty estimates, and enhancing accuracy with limited or noisy data. The hybrid model leverages DNN's feature extraction capabilities and GPR's ability to smooth predictions, significantly improving performance over standalone DNN. The hybrid model achieves high prediction accuracy, with R2 values of 0.997 for G⁎ and 0.947 for δ for binders, and 0.993 for G⁎ and 0.972 for δ for mastics, reducing G⁎ prediction error from 22.7% to 0.031% for fresh asphalt binder compared to standalone DNN. Feature importance analysis using random forest and SHAP techniques identifies test temperature, aging conditions, and penetration as key influencers of G⁎ and δ. This hybrid approach enhances the characterization of complex asphalt materials, offering pavement engineers a robust, reliable tool for predicting material behavior under diverse conditions.

Physics Informed Neural Networks (PINNs) have been rarely applied to solve multiphysics systems due to the inherent challenges in optimizing their complex loss functions, which typically incorporate multiple physics-based terms. This study presents a multistage PINN approach designed to efficiently solve coupled multiphysics systems with strong interdependencies. The multistage PINN progressively increases the complexity of the physical system being modeled, enabling more effective capture of coupling between different physics. The computational merits of this approach are demonstrated through two illustrative applications: prediction of asphalt aging and modeling of lid-driven cavity flow. Quantitative and qualitative comparisons with standard PINN and adaptive weight PINN approaches demonstrate the enhanced precision and computational efficiency of the proposed algorithm. The multistage PINN achieves a reduction in training time of more than 90% compared to standard PINNs while maintaining better alignment with the finite element method (FEM) solutions. The improvement in computational efficiency, coupled with enhanced accuracy, positions the multistage PINN as a powerful tool for addressing complex multiphysics problems across various engineering disciplines. The method’s ability to handle interactions between multiple physical processes, such as diffusion, chemical reactions, and fluid dynamics, makes it suitable for simulating long-term material behavior and complex fluid systems.

This research addresses the critical issue of load transfer efficiency (LTE) in jointed plain concrete pavements (JPCP), with a specific focus on the role of dowel bars in ensuring optimal load transfer and providing a comfortable ride for vehicles. While experimental studies have investigated factors like joint width, slab thickness, concrete strength, and dowel bar size that influence LTE, they are limited in their ability to accurately replicate real-world conditions and can be time-consuming. To overcome these limitations, finite element modelling (FEM) is employed as a powerful tool for simulating complex loading conditions and analyzing stress and strain distributions in pavements. The primary objective of this research is to develop an advanced FE model that incorporates the forklift tire-pavement interaction, enabling precise analysis of complex loading conditions in industrial pavements and the impact of various rigid pavement parameters on load transfer. By explicitly considering the interaction between the tire and pavement, the proposed model will provide an extensive and robust numerical tool for designers and engineers. Additionally, this study represents a novel framework to integrate concrete pavement dowel bars and complex tire modelling using FEM. The developed methodology holds significant promise in optimizing the design of dowel bar systems and back-calculating the pavement parameters for rolling weight deflectometers.

The linear viscoelastic behavior of materials is represented using mechanical models of choice, which are further utilized in different numerical investigations, such as finite element simulations and discrete element simulations. Burger's model is one of the widely adopted mechanical models and remains highly favored in contemporary research due to its multiple advantages. Specifically, it excels in representing long-term creep and stress relaxation behavior in a relatively simplified manner. Accurate identification of the long-term behavior for the viscoelastic material, particularly asphalt concrete, is crucial, as it serves as a key indicator of asphalt pavement performance over its service life. However, past research studies show that the parameters of Burger's model should be back-calculated from experimental data only within a limited range of frequency, otherwise, the parameters fail to represent the true material behavior. To the best of the authors’ knowledge, there is no approach for researchers to obtain the critical frequency range in which the experiments should be performed. Therefore, this study proposes a novel framework to find the critical frequency range to obtain appropriate model parameters of Burger's model, to better characterize the viscoelastic behavior of the materials. To examine the framework, asphalt concrete mixtures are used as examples in this study. Necessary laboratory tests including complex modulus tests and stress relaxation tests, are performed on two distinctive types of asphalt concrete mixtures. The generalized Maxwell model with different number of Maxwell chains are used to evaluate the performance of Burger's model. Furthermore, since commercially available finite element packages generally do not have a direct built-in Burger's model, the article shows a way of implementing Burger's model in finite element simulation. The simulations corresponding to the laboratory tests are carried out in both frequency domain and time domain to thoroughly evaluate the performance of Burger's model. The optimal frequency range of 0.1–20 Hz for the examined mixtures is found to significantly improve the accuracy of the descriptive master curve. The results also suggest that the generalized Maxwell model requires a minimum of four Maxwell chains to maintain good performance in accurately characterizing the behavior of asphalt mixtures. However, adding more Maxwell chains beyond a critical limit may not provide significant benefits. Finite element simulations demonstrate that the stress relaxation behavior predicted by the obtained Burger's model parameters aligns more closely with experimental data over longer time intervals. This makes Burger's model a strong choice for aiding in the design of simulations for studies focused on the long-term behavior of materials.

The ever-growing need to build roads to meet the necessary transportation demands is challenging, especially for developing countries. Low-volume roads (LVRs) are usually the backbone of catalyzing economic growth in these countries. With impediments surrounding Petroleum bitumen (price fluctuations) and environmental concerns, scientists are putting their effort into finding an alternative. The presented research is an attempt to check if Natural asphalt can be used as a full or partial replacement of the Petroleum bitumen. To the best of the authors' knowledge, only limited studies have focused on characterizing and understanding the engineering properties of Natural asphalt. The available techniques do not provide reliable information to the road authorities and hence they are discouraged from using it in practice. Particularly for countries, where the Natural asphalt source is available, the overall dependence on importing the Bitumen could be substantially reduced. Empirical and experience-based design criteria may not be sufficient as the standards were never developed for such materials, hence, a scientific approach is required before bringing it into practice. In this research, Natural asphalt sourced from different locations in Nigeria was assessed. Before performing the mixture level tests using Marshall and Cantabro design methods, the rheological and fatigue properties of the extracted Natural bitumen were examined in the laboratory. In the design of the experiment, various percentages of Natural asphalt were added between 0 % and 20 % by total mix weight; implying that the remaining required fraction of binder was fulfilled by the addition of petroleum bitumen. By using a ranking system (supported by statistics), an optimal design of mixture was obtained which was used in the field (exposed to normal traffic) at 30 different sections.

The complex structure of bituminous mixtures ranging from nanoscale binder components to macroscale pavement performance requires a comprehensive approach to material characterization and performance prediction. This paper provides a critical analysis of advanced techniques in paving materials modeling. It focuses on four main approaches: finite element method (FEM), discrete element method (DEM), phase field method (PFM), and artificial neural networks (ANNs). The review highlights how these computational methods enable more accurate predictions of material behavior, from asphalt binder rheology to mixture performance, while reducing reliance on extensive empirical testing. Key advances, such as the smooth integration of information across multiple scales and the emergence of physics-informed neural networks (PINNs), are discussed as promising avenues for enhancing model accuracy and computational efficiency. This review not only provides a comprehensive overview of current methodologies but also outlines future research directions aimed at developing more sustainable, cost-effective, and durable paving solutions through advanced multiscale modeling techniques.

The demand for alternative bitumen which could fully/partially replace Petroleum sourced bitumen for pavement construction is globally increasing. The increase in demand can be associated with several factors: depletion in crude oil resources, advances in crude oil refining processes, increased demand for highway infrastructure, and regional transportation-environmental policies. Since the production of Petroleum bitumen consumes energy and generates emissions, there is an effort to decrease harmful emissions which has inspired researchers to look for so-called "green alternatives". Natural bitumen could be considered a green alternative as it is a mixture of bitumen and mineral matter present naturally on earth, mainly if the Natural bitumen can be transported easily to the construction site. This paper reviews the state-of-the-art information on pavement construction using Natural bitumen from laboratory and field perspectives. The Physico-chemical properties, rheological properties and field behaviour of asphalts pavements containing Natural bitumen were assessed. Many road authorities would hesitate to utilise Natural bitumen for pavement applications due to a lack of available data, knowledge and a systematic research study. To the best of the authors’ knowledge, there is no comprehensive literature review article on Natural bitumen. Thus, the presented article aims to comprehensively review Natural bitumen resources and their types, Physico-chemical properties, application in pavement constructions, and reported field performances. At the end of the paper, future research challenges, future recommendations and a methodological framework is proposed.

In the context of climate change and global warming, the attention on the environmental cost of pavements is increasing. To scientifically quantify the environmental cost of pavements, accurate prediction of rolling resistance and fuel consumption is important. In this paper, a comprehensive review on rolling resistance of asphalt pavements and its environmental impact was presented. At first, the commonly used definitions of rolling resistance and texture characterisation methods of pavement surface were introduced. Then, the influence of different factors on rolling resistance was discussed. Next, the measuring and modelling approaches of rolling resistance were reviewed. At last, methods which can be used to predict fuel consumption and environmental impact were presented. It was found that an ideal approach for texture characterisation of pavement surface is to make use of the entire wavelength spectrum of road profiles and consider the enveloping curve of tire treads. Furthermore, the fact that rolling resistance can be influenced by different factors introduces difficulties in accurate measurement and modelling of rolling resistance. Moreover, testing methods and conditions have a significant effect on the empirical modelling of rolling resistance, while it is difficult and time-consuming to consider all the energy loss in the computational modelling of rolling resistance. In addition, the prediction of fuel consumption and environmental impact highly depends on the formulating methods and measuring conditions. The work presented in this paper will help to gain more insight into rolling resistance and its environmental impact, which ultimately promotes the construction of environmentally friendly pavements.

Moisture in bitumen and at the bitumen-aggregate interface affects the cohesive and adhesive properties of asphalt mixtures, which are critical for the service performance and durability of pavements. This paper aims to investigate the kinetics and thermodynamics of moisture transport in bitumen at various temperatures and relative humidity for different bitumen types. Transport models are introduced to study the moisture transport mechanisms. A parameter optimization approach combined with the finite element method is applied to simulate moisture transport behavior. Results show salient sorption increase at higher relative humidity levels (more than 70%), indicating the occurrence of clustering of water molecules in bitumen, which can lead to a significant decrease of the diffusion coefficient. Transport models show great quality in simulating experimental results, in which the S-Cluster model provides a detailed explanation of the moisture transport mechanisms and describes better the performance at high sorption levels. The diffusion coefficient, cluster size and activation energy were determined and were found to be linked to the bitumen chemical and structural properties. The transport kinetics and thermodynamics are expected to contribute to a comprehensive understanding of moisture transport behavior in bitumen and further of pavement moisture damage at complex and interacting environmental conditions.

The structural evaluation of existing pavements forms the basis for formulating cost-effective maintenance and rehabilitation strategies. A promising tool for pavement structural evaluation at network level is the Traffic Speed Deflectometer (TSD) test. However, the application of the TSD test is hindered by the lack of a robust and efficient parameter identification technique. To solve this problem, a theoretical model for the TSD test is first formulated. Then, a minimisation algorithm which works best with the theoretical TSD model for parameter identification is selected. Finally, the performance of this combination in processing field TSD measurements is studied. The results show that the modified Levenberg-Marquardt algorithm using all the 9 detection points is most suitable to be combined with the theoretical TSD model for parameter identification, which gives a promising parameter identification technique for TSD tests of pavements. The presented work contributes to the development of technologies for pavement structural evaluation.

Asphalt mixtures with high porosities (known as porous asphalt (PA) mixes) are becoming a popular choice among road authorities as it provides better skid resistance while also reducing tire-pavement noises. Towards the design and manufacture of PA mix pavement, the evaluation of the mechanical properties of PA mixes is of great importance. To predict the mechanical properties of PA mixes, micromechanical models have been considered as an effective tool. In most research studies, continuum-based micromechanical models, i.e. the Self-consistent model, the Mori-Tanaka model, etc. are widely used to predict the stiffness of asphalt mixtures. However, the limitation of these models is that they cannot describe the characteristics of individual particles and thus they cannot provide accurate predictions. On the other hand, the discrete-based micromechanical model (DBMM) which simulates a granular material as an assembly of bonded particles seems to be a promising alternative. Limited research studies have focused on studying the utilization and the applicability of this model for asphalt mixes. Therefore, this paper aims to propose a framework to use DBMM and to evaluate its performance in estimating a PA mix's stiffness. Based on the obtained results, both the merits and limitations of this model were highlighted.

Particle image velocimetry (PIV), a method based on image cross-correlation, is widely used for obtaining velocity fields from time-series of images of deforming objects. Rather than instantaneous velocities, we are interested in reconstructing cumulative deformation, and use PIV-derived incremental displacements for this purpose. Our focus is on analogue models of tectonic processes, which can accumulate large deformation. Importantly, PIV provides incremental displacements during analogue model evolution in a spatial reference (Eulerian) frame, without the need for explicit markers in a model. We integrate the displacements in a material reference (Lagrangian) frame, such that displacements can be integrated to track the spatial accumulative deformation field as a function of time. To describe cumulative, finite deformation, various strain tensors have been developed, and we discuss what strain measure best describes large shape changes, as standard infinitesimal strain tensors no longer apply for large deformation. PIV or comparable techniques have become a common method to determine strain in analogue models. However, the qualitative interpretation of observed strain has remained problematic for complex settings. Hence, PIV-derived displacements have not been fully exploited before, as methods to qualitatively characterize cumulative, large strain have been lacking. Notably, in tectonic settings, different types of deformation-extension, shortening, strike-slip-can be superimposed. We demonstrate that when shape changes are described in terms of Hencky strains, a logarithmic strain measure, finite deformation can be qualitatively described based on the relative magnitude of the two principal Hencky strains. Thereby, our method introduces a physically meaningful classification of large 2-D strains. We show that our strain type classification method allows for accurate mapping of tectonic structures in analogue models of lithospheric deformation, and complements visual inspection of fault geometries. Our method can easily discern complex strike-slip shear zones, thrust faults and extensional structures and its evolution in time. Our newly developed software to compute deformation is freely available and can be used to post-process incremental displacements from PIV or similar autocorrelation methods.

Micromechanics, which can be used to relate the properties of a composite to the properties of individual constituents, is considered a good approach to understanding the fundamental mechanisms behind the behavior of asphalt materials. Compared with the semi-empirical and numerical micromechanical models, analytical micromechanical models do not need calibration factors. In addition, they can provide analytical solutions on the basis of a series of assumptions. Using these models, researchers have separated the effects of different stiffening mechanisms (i.e., the volume-filling reinforcement, the physicochemical reinforcement, and the particle-contact reinforcement) for mastic. However, similar research work has not been conducted for asphalt mixtures and, moreover, the characteristics of the particle-contact reinforcement have not been deeply analyzed by researchers. Therefore, this paper aims to understand the stiffness of asphalt mixture through micromechanics. The focus of this study was on porous asphalt mixture where particle-contact reinforcement plays an important role in its behavior. The stiffening effects of different mechanisms were separated using analytical micromechanical models. The effects of temperature/frequency and the properties of the matrix phase on the stiffening effect of the particle-contact reinforcement were analyzed.

In the Netherlands, more than 80% of the highways are surfaced by porous asphalt (PA) mixes. The benefits of using PA mixes include, among others, the reduction of noise and the improvement of skid resistance. However, pavements with PA mixes are known to have a shorter lifetime and higher maintenance costs as compared with traditional dense asphalt mixes. Raveling is one of the most prominent distresses that occur on PA mix pavements. To analyze the raveling distress of a PA mix pavement, the stress and strain fields at the component level are required. Computational models based on finite element methods (FEM), discrete element methods (DEM), or both, can be used to compute local stress and strain fields. However, they require the development of large FEM meshes and large-scale computational facilities. As an alternative, the homogenization technique provides a way to calculate the stress and strain fields at the component level without the need for much computation power. This study aims to propose a new approach to analyze the raveling distress of a PA mix pavement by using the homogenization technique. To demonstrate the application of the proposed approach, a real field-like example was presented. In the real field-like example, the Mori?Tanaka model was used as a homogenization technique. The commonly available pavement analysis tool 3D-MOVE was used to compute the response of the analyzed pavement. In general, it was concluded that the homogenization technique could be a reliable and effective way to analyze the raveling distress of a PA mix pavement.

A non-destructive testing method suitable for network-level pavement structural evaluation is the traffic speed deflectometer (TSD) test. However, the analysis of TSD measurements still needs a proper parameter back-calculation procedure, which requires an accurate and efficient forward calculation model. As a first step to solving this issue, a nonlinear spectral element model which can simulate TSD tests of asphalt pavements is developed. The model is used to investigate the characteristics and parameter sensitivity of the response of asphalt pavements caused by the TSD loading. The results indicate that the vertical deflection curve along the direction of movement observed on the asphalt pavement surface is slightly asymmetric, and the maximum deflection appears behind the centre of the loading area. In addition, the slope curve of vertical deflection is highly sensitive to the magnitude of the applied force, the moduli of the base layer and subgrade, and the thicknesses of the asphalt layer and base layer. Furthermore, the slope curve is relatively sensitive to the glassy modulus of the asphalt layer. Because of its good predictive capability and high computational efficiency, the proposed model has the desired characteristics to be used as the computational kernel for parameter back-calculation procedures of TSD measurements.

The adhesive bonding strength of the membrane layers between the asphalt concrete surface layers and the decks of steel bridges has a strong influence on the fatigue life of orthotropic steel deck bridges (OSDBs). The most important requirement for the application of membrane materials to orthotropic steel deck bridges is that the membrane adhesive layer is able to sufficiently bond to its surrounding material layers. The interfacial properties between the membrane and the layers bonded to it have not been extensively studied in the current orthotropic steel deck bridge system. In this paper, details of the contact interface element utilised to model the interfacial bonding properties will be discussed. Furthermore, the traction-separation material law will be chosen to describe the bonding response of the interfacial properties of the membrane to its surrounding surfacing layers on OSDBs. Some numerical examples, in which various aspects of the finite elements response of the contact interface model will be presented. Utilisation of the model in finite element analyses has enabled the investigation of the response of a 3D orthotropic steel deck bridge subjected to the different traffic loading conditions.

The mechanical properties of asphalt mixture are always required for the evaluation of the durability of pavements. In order to obtain these properties without conducting expensive laboratory tests and using calibrated empirical models, research studies have been carried out to develop micromechanics-based models. Continuum-based micromechanical models (CBMM), which are developed based on continuum mechanics, have increasingly been utilized to estimate the mechanical properties of asphalt materials based on the fundamental properties of individual constituents. These analytical models are expected to provide reliable predictions without the need for extensive computational facilities. Although the utilization of CBMM has been presented by several past studies, most of the studies do not provide a concise and critical review of these models. Therefore, in this paper, a complete review of CBMM was presented. Commonly used CBMM were introduced in detail and their advantages and disadvantages were discussed and compared. Comprehensive summaries and critical discussions about their current utilization and limitations for predicting the mechanical properties of asphalt materials were given. Further modifications and new development for addressing the limitations were extensively described and discussed. In the end, research challenges were highlighted and future recommendations from different perspectives were proposed.

Maintenance and rehabilitation strategies of pavements are usually made based on the results of performance evaluation. An efficient tool for pavement structural evaluation at network level is the traffic speed deflectometer (TSD) test. In order to deal with TSD measurements, this paper proposes a parameter back-calculation technique. Firstly, the sensitivity of the surface response for an elastic pavement structure with hysteretic damping to different structural parameters is investigated. Then, the ability of the parameter backcalculation technique is verified by conducting a case study. The results show that the proposed technique is able to back-calculate the structural parameters of pavements by analysing TSD measurements. The presented work contributes to the development of parameter back-calculation techniques for the TSD test.