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.

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Pavement materials that could enhance the mechanical properties of open-graded porous asphalt mixtures in long-term service periods could offer a solution to produce long-life pavements, causing a reduction of interventions' needs, as well as the associated disruptions to road users and user costs. One option to improve the longevity of open-graded porous mixtures is with the use of epoxy asphalt that, despite its high initial cost, offers enhanced longevity that might offset any future user and intervention costs. This study aimed to evaluate the durability of plant-produced epoxy-modified open-graded porous asphalt mixtures. A batch production plant was employed to produce loose mixtures, which were used to pave a test road in the Province of Gelderland, the Netherlands, and compact specimens in the laboratory. Control mixtures with a non-epoxy-modified asphalt binder were also produced in the same plant. The durability of laboratory- and field-compacted mixtures was evaluated by conducting indirect tensile tests before and after oven conditioning. Results illustrated that the epoxy-modified asphalt demonstrated the highest strength and stiffness values, while the strength was reduced after conditioning in a water bath with the retained strength within the allowable specification limits. This attribute was confirmed from drill cores obtained from the test road after one year in service. Also, the materials compacted in the field had slightly higher strength and stiffness values than the laboratory-produced mixtures. Although the results provided have illustrated the improvement of durability of open-graded porous asphalt with implementing epoxy modification, further evidence from the test road over the years is needed for validation.

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Scymol is a Python-based software package specifically designed to facilitate the setup and execution of molecular simulations in LAMMPS. It comes equipped with a user-friendly interface, which simplifies the process of initializing molecular systems and defining simulation parameters. Moreover, the software generates and executes LAMMPS simulation sequences, enabling researchers to establish comprehensive simulation schemes, such as heating or deformation cycles, in a single run. Through its successful application in diverse research projects and its modular design, Scymol demonstrates considerable promise as an indispensable tool for researchers aiming to carry out molecular dynamics simulations without sacrificing complexity or high-throughput capabilities in their methodologies.@en

Developed by Delft University of Technology, the tri-component polyurethane modified cold binder (PMCB) displays impressive durability and strength in asphalt mixtures, showing promise as a reliable binder for cold in-place recycling. However, when applying PMCB for rapid, in-situ recycling, the presence of moisture in reclaimed asphalt pavement (RAP) poses a significant challenge. To address this, an innovative approach involving treatment of the wet RAP with Calcium dioxide (CaO) prior to the integration of PMCB was tested. Evaluation methods used included the Indirect Tensile Test (ITT), followed by the calculation of the Indirect Tensile Strength Ratio (ITSR) to assess moisture susceptibility. Furthermore, Cantabro tests were performed to determine the material loss under abrasion and weathering conditions. These assessments underscored the feasibility of this approach. The treatment of wet RAP with CaO has proven a viable strategy for rapid in-situ recycling with PMCB, contributing to sustainable pavement construction. In addition, the research identified that a 5.5% concentration of the PMCB binder maximizes structural integrity and performance in the considered RAP.

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The relationship between real-world traffic and pavement raveling is unclear and subject to ongoing debates. This research proposes a novel approach that extends beyond traditional correlation analyses to explore causal mechanisms between mixed traffic and raveling. This approach incorporates the causal discovery method, and is applied to five Dutch porous asphalt (PA) highway sites that have substantial data sets. Findings indicate a nonlinear relationship between traffic volume and raveling, with road age emerging as a shared contributor. The results also suggest that the degree to which different vehicle types contribute as a causal factor for raveling varies with carriageway configurations and lane characteristics. This underlines the need for targeted maintenance strategies. Challenges remain due to confounding correlations among traffic variables, necessitating further development of causal discovery models. This study may not conclusively resolve the debate on to what extent traffic contributes to raveling, but we argue we provide sufficient evidence against rejecting this hypothesis.

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This study aims to correlate the chemical and rheological properties of bitumen at different ageing states and understand the chemical mechanisms of bitumen degradation due to ageing. The relationship between Fourier transform infrared (FTIR) spectral data and rheological results is investigated using partial least squares (PLS) regression integrated with two variable selection methods. The spectral region of 1800 – 800 cm1 is identified as the most informative for accurate estimation of the rheological properties of bitumen. Variable selection methods, particularly moving windows (MW), improve the prediction accuracy of the regression models.

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This study employs strain-controlled oscillatory deformations in Molecular Dynamics (MD) simulations to evaluate the dynamic properties of all-atom molecular systems, specifically targeting the SARA fractions of bitumen. Twelve molecular systems representing these fractions were modeled using the PCFF force field. The simulations effectively captured their viscoelastic properties across multiple frequency domains, including Elastic, Glassy, Rubbery, and Viscous responses. Reported storage and loss moduli range from thousands to tens of megapascals, with viscosities from tens to near-zero Pascal-seconds across various frequencies and temperatures, aligning well with experimental observations. Saturates and Aromatics were identified as the softest and most thermally susceptible fractions, while Resins and Asphaltenes were the stiffest and least susceptible. The study reveals that the relaxation time of all-atom molecular systems is significantly shorter than in experimental setups, necessitating careful comparison of stress-related phenomena across equivalent relaxation times. Although this allows for the exploration of response profiles in computationally tractable simulations, the nature of all-atom force fields and simulation algorithms introduces spatiotemporal scale discrepancies that must be addressed in future simulations involving the study of stress-related phenomena using MD.

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This paper presents a United Atom (UA) force field for simulating hydrocarbon molecules in bituminous materials, integrating explicit hydrogens into beads with their parent atom. This method simplifies all-atom molecular models, significantly accelerating Molecular Dynamics (MD) simulations of bitumen by 10 to 100 times. Key advantages include halving the particle count, eliminating complex hydrogen interactions, and decreasing the degrees of freedom of the molecules. Developed by mapping forces from an all-atom model to the centers of mass of UA model beads, the force field ensures accurate replication of energies, forces, and molecular conformations, mirroring properties like pressure and density. It features 17 bead types and 287 interaction types, encompassing various hydrocarbon molecules. The UA force field's stability, surpassing all-atom models, is a notable achievement. This stability, stemming from smoother potential energy surfaces, leads to consistent property measurements and improved stress tensor accuracy. It enables the extension of MD simulations to larger spatiotemporal scales, crucial for understanding complex phenomena such as phase separation in bituminous materials. This foundational work sets the stage for future developments, including refining parameters and introducing new bead types, to enhance the modeling capabilities of the force field, thereby advancing the application and understanding of bituminous materials.

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This study explores the use of chemical descriptors derived from force field atom types to predict Fickian diffusion coefficients of rejuvenators in bitumen, utilizing machine learning models trained on data from 240 non-equilibrium molecular dynamics simulations. The simulations cover three bitumen types (NO, TO, FO), five aging degrees, and four temperatures (60 °C, 120 °C, 160 °C, 200 °C), capturing diffusion coefficients ranging from 0.0068e-10 m2/s in highly aged bitumens at 60 °C to 4.35e-10 m2/s in fresher samples at 200 °C. The MLM, built with 18 chemical descriptors for bitumen and rejuvenator sides, achieves an R2 of 0.97, accurately predicting diffusion across varied conditions. This approach abstracts away from the need for repeated MD simulations, enabling diffusion predictions even for systems outside the original dataset. The manuscript presents three case studies to illustrate how the model can be used for the iterative design of rejuvenators by optimizing molecular structures based on critical chemical features, such as rejuvenator oxygen content, bitumen sulfur content, and molecular weights. It also demonstrates how the model offers a practical framework for understanding the diffusion and performance of rejuvenators by linking time-dependent factors—such as concentration, depth, and rejuvenation time—with the bulk properties of bitumen-rejuvenator systems, facilitating industrial applications.@en

Bituminous binders naturally age, affecting the properties and performance of asphalt pavements. The physical and chemical characteristics of binders are influenced by environmental factors, leading to a decline in their performance and durability. Therefore, it is essential to understand the mechanisms of binder environmental aging to design more resilient and long-lasting asphalt pavements. This study examines the effects of temperature, liquid, and vapor water on binder aging to develop more durable pavements. Aging was induced at three temperatures (60°C, 70°C, 85°C) under dry air, 90% relative humidity, and water immersion conditions. Field-aged samples were also analyzed to compare with laboratory-aged samples. Fourier-transform infrared spectroscopy (FTIR) and dynamic shear rheometer (DSR) were used to assess chemical and rheological changes. To assess the similarity between samples and identify the lab aging protocol closest to field aging, Hierarchical Cluster Analysis (HCA) was employed for data analysis.

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The advent of computational techniques, particularly atomistic simulations, has lessened the dependency on physical experiments in various scientific fields. Yet, the preparation complexity for simulations using platforms like LAMMPS and GROMACS persists. We introduce SMI2PDB, a Python tool that automates molecular systems assembly from SMILES to PDB format, easing molecular dynamics simulation setups. SMI2PDB manages molecule configuration and quantification effortlessly, establishes stable conformers, applies random rotations, and positions them in a simulation box with a Sobol sequence to reduce overlaps. This script facilitates the rapid preparation of complex organic mixtures for use in simulations, enhancing the exploration of novel materials.@en

This study implements molecular dynamics (MD) simulations to explore the atomic-level energy properties of rejuvenated bitumen, considering the influence of different recycling agent (RA) types, dosages and aging levels of bitumen. Moreover, the potential correlations between energy indices and high-temperature performance of rejuvenated bitumen are explored. Our findings show that recycling agents can effectively reinstate the cohesive energy density (CED) values of aged bitumen, correlating well with their high-temperature rheological properties. The results reveal that the energy parameters of potential energy (UVEP), kinetic energy (UWEK), non-bond energy (EN), total energy (UVET), diagonal energy (UNED), and cross-terms energy (ECT) can reflect the restoration level of recycling agents (RAs) on atomic-level energy characteristics of aged bitumen. Compared to rutting failure temperature (RFT), elastic recovery (R3.2), and creep compliance (Jnr3.2), the zero-shear viscosity (ZSV) greatly correlates with CED. Meanwhile, the UWEK index from MD simulations demonstrates a strong correlation with high-temperature rheological indicators of rejuvenated bitumen. With the rise in UWEK, there is a linear decrease in the RFT, Log(ZSV), and R3.2 values of rejuvenated bitumen. Conversely, the Log(Jnr3.2) exhibits a linear increasing trend. However, the correlation patterns between rheological indicators and either EN or ECT are contingent on the aging degree of bitumen. Based on the correlation coefficient, the UWEK stands out as the primary choice among all energy indices for predicting high-temperature rheological performance of rejuvenated bitumen.@en

Inconsistencies between performance data from laboratory-prepared and field samples have been widely reported. These inconsistencies often result in inaccurate condition prediction, which leads to inefficient maintenance planning. Traditional pavement management systems (PMS) do not have the appropriate means (e.g., mechanistic solutions, extensive data handling facilities, etc.) to consider these data inconsistencies. With the growing demand for sustainable materials, there is a need for more self-learning systems that could quickly transfer laboratory-based information to field-based information inside the PMS. The article aims to present a future-ready machine learning-based framework for analyzing the differences between laboratory and field-prepared samples. Developed on the basis of data obtained from field and laboratory data, the gradient-boosting decision trees-based framework was able to establish a good relationship between laboratory performance and field performance (R2test > 80 for all models). At the same time, the framework could also show more complex relationships that are often not considered in practice.@en

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.@en

Atomistic simulations are crucial for understanding material properties at the molecular level but are limited by high computational costs, especially for large, complex systems like bituminous materials. Our team developed a Force-matched United Atom (UA) Coarse Graining (CG) force field to enhance computational efficiency while retaining atomic detail. However, converting all-atom models to CG models is complex, requiring detailed atom-to-bead mapping and compatibility with molecular dynamics (MD) engines like LAMMPS. To address this, we introduce AA2UA, an open-source software that simplifies the conversion of PDB files into LAMMPS-readable structure topology files, facilitating broader use of the developed UA force field.@en

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.

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The low-temperature relaxation and fatigue cracking performance are two essential aspects in estimating the rejuvenation efficiency of recycling agents (RAs). This study aims to fundamentally investigate the effects of recycling agent type/dosage and aging degree of bitumen on thermodynamic and rheological properties of rejuvenated bitumen at low and intermediate temperatures. Molecular dynamics (MD) simulations are utilized to predict thermodynamic indices of rejuvenated bitumen, further linked to critical low-temperature and fatigue indicators from experiments. The results reveal that all RAs show a regeneration effect on fractional free volume (FFV), self-diffusion coefficient (DS), glass transition temperature (Tg), and surface free energy (γ). Bio-oil and engine-oil exhibit higher rejuvenation efficiency on these thermodynamic properties than naphthenic-oil and aromatic-oil. The aging degree of bitumen and temperature show significant effects on rejuvenation efficiency. It is recommended to use the FFV parameter to predict the relaxation properties of rejuvenated bitumen. However, these thermodynamic indicators inadequately differentiate between rejuvenators and softeners. Based on crossover parameter results, most recycling agents (bio-oil, engine oil, and naphthenic oil) in this study display softening characteristics. Only aromatic oil effectively rejuvenates the crossover modulus (Gc) of aged binder. Notably, engine oil demonstrates the least rejuvenation in crossover parameters for the recovery of aged bitumen. Further, γ demonstrates a strong association with both Glover-Rowe (G-R) and fatigue crack width C500 indices across all cases involving rejuvenated bitumen. This work will build a multi-scale evaluation framework on the rejuvenation effectiveness of recycling agents on low-temperature and fatigue performance of aged bitumen.@en

Pdb2dat, developed in Python, is an open-source, self-contained utility that facilitates the conversion of PDB files into LAMMPS data files, catering to the need of initializing atomistic simulation from initial atomic configurations. It extracts molecular details from PDB files, uses Rdkit and Xyz2mol for bonding analysis and 3D conformer generation, and uses Pysimm for assigning force field types and charges. Designed to be lightweight and fully Pythonic, pdb2dat is suitable for use in privilege-limited high-throughput environments. The output details system topologies for use in MD simulations, significantly simplifying the preparatory steps needed by researchers to explore materials phenomena through LAMMPS.@en

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.

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Elastomer/plastic compound-modified bitumen was created by adding reactive elastomeric terpolymer (RET) to plastic-modified bitumen, made of either high-density polyethylene (HDPE) or recycled polyethylene (RPE). The rheological properties of the modified bitumen were analyzed. The results indicated that RET elastomer improved high-temperature modulus, temperature insensitivity, anti-rutting properties, elastic recovery, and shear-resistance of both HDPE and RPE-modified bitumen. A high dosage of RET had a negative impact on the cracking resistance of plastic-modified bitumen, thus it is recommended to use 1wt% for optimal results. The increased elasticity in the bitumen was attributed to the creation of a polymer network by RET.

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