The transition to sustainable pavement materials requires innovative alternatives to traditional mineral fillers that can simultaneously deliver mechanical performance, durability, and reduced environmental impact. This study systematically investigates four geopolymer-based powders derived from fly ash (FAG), metakaolin (MKG), red mud (RM), and slag (S) as substitutes for conventional limestone fillers (WG and WG60K) in asphalt mastic. Rheological testing, chemical characterization, and microstructural analysis are conducted to evaluate their effects on the chemo-structural-mechanical behaviour of bitumen. Results show that FAG markedly enhances high-temperature performance, improving rutting resistance, thermal stability, and shear strength by up to 58%, 45%, and 62%, but exhibits poor fatigue resistance and limited stress-relaxation capacity. In contrast, slag and metakaolin powders offer a more balanced performance profile, with superior fatigue resistance, finer dispersion, and smoother surface morphology (Ra < 1.5 μm), making them promising candidates for durable pavements in warm and moderate climates. RM shows intermediate behaviour, providing good thermal stability but a rougher texture and stronger elastic stiffening. Although no chemical reaction is observed between powders and bitumen, physical interactions such as surface adsorption and alignment of aliphatic chains are found to stiffen the mastic and alter its temperature-dependent response. Surface roughness and dispersion quality are directly correlated with rheological performance, with coarser fillers (FAG, WG60K) enhanced rigidity but shortened fatigue life. Overall, Slag and metakaolin emerge as the most promising geopolymer fillers for durable asphalt pavements.

Developing a rapid and realistic binder aging protocol remains a challenge in pavement engineering. This study introduces the UV-Peroxide Aging (UPA) protocol, which employs hydrogen peroxide and UV light to accelerate oxidative reactions. The effects of hydrogen peroxide concentration, temperature, and aging duration were evaluated and compared with existing laboratory protocols and long-term field aging. Results show that UPA aging at 85°C for 3 h or 60°C for 6–9 h produces aging effects comparable to 9 years of field exposure. Additionally, new methods for lab-field comparisons of chemical and rheological properties were applied, offering a systematic framework for analysing optimized aging protocols.

This study investigates the effects of warm-mix asphalt (WMA) additives and bio-oil on the rheological and chemical properties of virgin bitumen (VB) and polymer-modified bitumen (PMB) under varying aging conditions. The workability, viscoelasticity, and chemical characteristic of warm-mix bio-rejuvenated bitumen are assessed using a rotational viscometer, dynamic shear rheometer, Fourier Transform Infrared Spectroscopy. Results show that PMB has superior aging resistance than VB. The wax-based additive exponentially reduces viscosity of VB, while the chemical-based additive decreases viscosity linearly and performs better in PMB due to improved polymer-bitumen interfacial lubrication. The wax-based additive enhances high-temperature elasticity and rutting resistance, whereas adding 0.9 wt% chemical-based additive declines the rutting failure temperature (RFT) of VB by 3.3°C and PMB by 2.3°C. However, the wax-based additive lowers the fatigue life of VB, while the chemical-based additive extends the fatigue life. The fatigue failure temperature (FFT) value increases by 2.3°C for VB and 3.4°C for PMB after adding 4 % wax-based additive. The optimal dosage of the chemical-based additive for PMB is determined to be 0.6 %. The bio-rejuvenator significantly enhances the fatigue performance of aged VB, but has limited impact on aged PMB. Both WMA additives reduce aromaticity and alter aliphatic content, with the chemical-based one showing a stronger dilutive effect, particularly in PMB. Additionally, a warm-mix bio-rejuvenated bitumen with higher aliphatic index (AII) and carbonyl index (CI) shows better deformation resistance and longer fatigue life.

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.

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.

The increasing demand for cleaner and more efficient refining processes has driven the development of advanced upgrading technologies for heavy crude residues. This study investigates a novel Visbreaking-Supercritical Fluid Extraction (SFE) approach to upgrade the Merey vacuum residue (VR), integrating experimental analysis with molecular dynamics (MD) simulations for atomic-level mechanism exploration. The Visbreaking process is optimized at 400 °C for 40 min, achieving a viscosity reduction of 89.0 % while minimizing coke formation. The SFE process fractionates the visbroken VR, with total extraction yields ranging from 70.1 wt% to 70.7 wt%, demonstrating remarkable efficiency. Higher extraction pressures enhance deasphalted oil (DAO) yield but compromise its quality with higher metal and sulfur contents, while lower temperatures improve extraction selectivity. The integrated process effectively removes Fe, Ni, V, and Na, with demetalization efficiencies exceeding 62 %, 75 %, and 95 %, respectively. Molecular dynamics simulations provide atomic-scale insights into solubility mechanisms, revealing that higher pressures and lower temperatures enhance solvent compatibility with lighter visbroken VR fractions. The extracted DAO meets marine fuel oil blending specifications, while raffinates show potential for bitumen production and modification. These findings highlight the Visbreaking-SFE combination as a promising and sustainable upgrading strategy for heavy crude residues.

Bitumen, a crucial constituent in the composition of asphalt pavements, plays a vital role in the performance and durability of pavements. Bitumen undergoes aging over time due to complex interactions between its chemical components and various environmental factors. In this investigation, the study focuses on examining the aging process of bitumen under the combined influence of moisture and reactive oxygen species (ROS). The findings highlight the importance of considering ROS and moisture as critical factors contributing to accelerated aging. Results obtained from Fourier-transform infrared (FTIR) spectroscopy, SARA fractionation, and optical inverse microscope (OIM), as well as dynamic shear rheometer (DSR) examination, indicate that the concurrent influence of these factors induces significant aging in bitumen, with the extent of this impact being modulated by the bitumen's inherent aging susceptibility. The insights obtained from this study enhance strategies to justify the destructive impacts of aging, extending the operational lifespan of asphalt pavements.

Understanding aging across material scales is critical for predicting the long-term performance of bituminous materials. This study investigates the aging of binder, mastic, and asphalt mixture samples under various temperature, pressure, reactive oxygen species (ROS), and humidity. Chemical aging processes were analysed using attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR), principal component analysis (PCA), and Euclidean distance. Normalisation, baseline correction, and advanced ATR correction were used to enhance the accuracy of FTIR results. Hydrated lime in mastics enhanced the resistance to oxidative aging, particularly under hygrothermal conditions. PCA identified key spectral regions for understanding aging processes of bituminous materials. Porous asphalt (PA) mixtures aged more than stone mastic asphalt under field-like conditions. PCA identified distinct aging clusters at low and high pressure. Euclidean distance analysis indicated that binder-level aging can approximate mastic and mixture aging under certain conditions. The findings confirm that FTIR indices are effective for multi-scale aging studies.

This recommendation is an output of a small-scale round-robin test involving three different laboratories from TG2 of the RILEM TC 295-FBB: “Fingerprinting bituminous binders using physico-chemical analysis” concerning the use of ¹H-NMR for fingerprinting of bituminous binders. It demonstrates the full capabilities of ¹H-NMR as a robust characterisation tool for complex organic materials, like bituminous binders, to examine their molecular composition in a reproducible way and with the best possible detail. This recommendation documents the key factors in sample preparation and the sensitivity of data post-processing steps. It concludes with best practices and a case study examining the effect of laboratory ageing on two bituminous binders. Overall, it highlights the potential, to the broader scientific community, of another efficient chemometric tool for bituminous binders.

Attenuated Total Reflection Fourier Transform Infrared spectroscopy has become a popular spectroscopic technique in bituminous binder analysis. However, comparable results are not obtainable yet due to differences in devices, measurement routines, sample preparation procedures, and spectral evaluation. Thus, the Task Group 1 of the RILEM TC 295-FBB: “Fingerprinting bituminous binders using physicochemical analysis” focuses on bringing this method towards pre-standardization. This study evaluates the reproducibility and consistency from round robin test, where 21 participating laboratories performed six different preparation techniques on three different binders in an unaged, short-term, and long-term aged state. A total of 6461 spectra were recorded and evaluated for their mean, standard deviation and coefficient of variation (CV) in the spectral region between 1800 and 600 cm⁻¹. The results show that the solid sample preparation methods provide excellent reproducibility, with a coefficient of variation below 2%. Only the solvent method showed a higher coefficient of variation at 7.18%. Outliers with a high CV were detected and categorized into two groups: one where only one of the four samples differed and the other where all 16 spectra showed slight scattering in the overall absorption. The consistency of the method is significantly influenced by the accuracy of sample preparation, which is crucial for minimizing differences in slope, baseline, and noise in the spectra. These findings show the excellent reproducibility of these sample preparation methods and will be further examined to establish universal indices for evaluating effects such as ageing, bringing the method closer towards standardization.

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 objective of this research was to evaluate the lifecycle costs associated with emerging pavement maintenance technologies, namely, in-situ rejuvenation and very open emulsion asphalt concrete (ZOEAB+), and scrutinise their suitability over corrective resurfacing maintenance using a stochastic approach. A rational lifecycle inventory was developed by conducting interviews and questionnaire surveys with experts and referring to standard guidelines and international databases. The net present value (NPV) was found sensitive to 12 different inputs with traffic growth rate and discount rate causing the highest uncertainty followed by gasoline and diesel prices. Monte Carlo simulations suggested that the median uncertainty in NPV by using in-situ rejuvenation and ZOEAB+ was 13% and 4% lower than resurfacing. It is envisioned that the research outcomes will assist decision-makers in understanding the uncertainties and costs associated with different maintenance alternatives in the early stages of the project to foster procurement of sustainable and circular pavement maintenance strategies.

Refurbishing of milled asphalt into new bound layers of pavement is recognized as a sustainable maintenance practice. However, limited attention has been paid towards investigating their circularity aspects. Furthermore, evolving legislative requirements call for integration of circularity and sustainability facets. Therefore, this study presents a flexible framework that aims at assessing and integrating the environmental, economic, and circularity aspects associated with two maintenance alternatives, i.e., reconstruction of pavement with a bound base layer using bituminous materials (BSM) versus routine patch repair and resurfacing. The integration of different assessment categories was performed using a metric termed as net risk reduction gain (NRRG). The results revealed that the NRRG for BSM option was 2.5 times higher than routine maintenance over a 50 year analysis period, thereby emphasizing its higher circularity and sustainability benefits. The proposed framework is envisioned to pave way for integration of circular thinking along with sustainability considerations for pavement design and construction.

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.

Oxidative aging induces significant stiffening of asphalt binders that leads to a pronounced reduction in the overall durability of asphalt pavements. The strategic implementation of antioxidant additives provides a potential solution to alleviate this issue. This work presents results from the second phase of the global consortium for antioxidants research aimed at investigating the effectiveness of potential antioxidants in increasing the durability of asphalt binders. Sixteen laboratories around the world participated in this effort and a total of 28 binders from diverse geographical regions were tested. Two promising antioxidants, namely zinc diethyldithiocarbamate (ZDC) and kraft lignin were evaluated in this phase and blended with the binders at specific proportions. Subsequently, a comprehensive investigation was conducted to assess rheological characteristics and chemical properties of the various blends, utilising Dynamic Shear Rheometer (DSR) measurements and Fourier Transform Infrared (FTIR) Spectroscopy. The findings indicate that additives such as ZDC hold considerable promise as an effective antioxidant, particularly when considering a wide diversity of binders. In general, its incorporation does not compromise the rutting performance of the binders and significantly improves fatigue performance. Therefore, research efforts should be focused on exploring additional facets to assess its practical applicability in field.

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.

Attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR) is an essential tool for the analysis of bituminous binders due to its cost-effectiveness, user-friendliness, and non-destructive nature. However, its effectiveness is often hampered by challenges such as non-informative regions, lack of standardized analysis methods, and inconsistent baselines in spectral data. Addressing these challenges, this study aims to comprehensively evaluate the impact of various data pre-processing (DP) methods on ATR-FTIR spectra from diverse bituminous binder types, sources, and aging conditions. Using partial least squares discriminant analysis (PLSDA) classification, the study assesses the effectiveness of baseline correction, normalization, and their combinations. The methodology involves analyzing peak areas, indices, entire spectra, and first derivative spectra to determine the most effective pre-processing strategies. Key findings reveal that the effectiveness of DP methods is influenced by the classification goals, characteristics of the spectral dataset, and the specific methods employed for input data preparation. The study demonstrates that using entire spectra or their first derivatives leads to higher classification accuracy compared to indices or specific spectral peak areas. The choice between peak area and indices calculation methods should align with the study’s objectives. For efficient and rapid selection of DP methods, tools like PLSDA are recommended. Among the normalization methods, normalization to constant vector length (NCV), normalization to change the maximum to 1 (NMO), robust scaling (RS), and normalization to sum (NTS) are suitable for peak area or indices-based classification. For entire spectra and their first derivatives, NTS, NCV, autoscaling (AS), pareto scaling (PS), and standard normal variate (SNV) methods are recommended. Regarding baseline correction, Adaptive Smoothness Penalized Least Squares (aspls) is suitable for studies focusing on gradual material changes, such as multi-level aging studies, but not for additive
detection studies. The findings of this study provide valuable insights and practical recommendations for selecting appropriate DP methods, thereby enhancing the classification accuracy and reliability of ATR-FTIR spectral analysis of bituminous binders. This contributes significantly to the design of experiments, reduces operational risks, and optimizes resource utilization in the field.

This study aims to improve laboratory aging procedures for bituminous materials to better replicate field conditions. Two binders and mixtures were subjected to various levels of humidity, temperatures, pressures, film thicknesses, and aging durations. By comparing these lab-aged samples to field-aged samples, the study aims to simulate real-world aging more accurately. Fourier-transform infrared (FTIR) spectroscopy and frequency sweep tests were employed to analyse these samples. Multivariate techniques—Principal Component Analysis (PCA), Multiple Linear Regression (MLR), and Support Vector Regression (SVR)—were used to explore chemical and rheological relationships, evaluate the interchangeability of aging factors, and quantify the equivalency between laboratory and field aging. The findings revealed that increased temperature, pressure, and duration lead to more oxidative products. The PCA distinguished between two binders and aging trends, highlighting the importance of both FTIR and rheological measurements. The SVR model demonstrated strong predictive performance for rheological properties, identifying critical FTIR region, 710–912 -1cm. By MLR model, optimal aging conditions to simulate nine years of field aging for porous asphalt and stone mastic asphalt were back-calculated. The Euclidean distance found laboratory conditions that closely match field-aged samples. SVR models provided predictions of simulated field aging time for various laboratory aging conditions.

Asphalt pavements are subjected to various environmental factors such as rainfall, sunlight, humidity and wind that causes oxidative aging of bitumen, leading to reduced structural and functional performances in the longer run. Antioxidants are often added to asphalt binders to enhance their resistance to oxidative ageing. In the current study, two different antioxidants, Zinc Diethyldithiocarbamate and Lignin were evaluated for their effectiveness in improving the performance of asphalt binders. The laboratory mixing procedures were conducted at two different percentages, and laboratory aging were performed. Rheological and chemical tests were then conducted to evaluate the performance of the binders at different temperatures. The current study provides valuable insights into the use of antioxidants for improving the performance and service life of asphalt pavements, which will help in the development of perpetual asphalt pavements in the future.

The rejuvenation efficiency of aged bitumen is the main concern when developing rejuvenating agents. It is necessary to develop a method to assess the efficiency of rejuvenators using rheological parameters in the whole frequency region. To this end, the 2S2P1D micromechanics model is adopted to fit the entire G∗ master curves of various rejuvenated bitumen, and the influence of rejuvenator type/dosage and aging grade of bitumen on the whole G∗ master curve and chemical indices are investigated. Critical parameters for evaluating rejuvenation efficiency derived from viscoelastic models and chemical characterizations are proposed. Furthermore, the potential relationships between the rheological model-based parameters (E _∞, δ, β, and τ) and chemical indices (carbonyl index CI and sulfoxide index SI) are explored. The results indicate that rejuvenators restore the δ, τ, CI, and SI values of aged bitumen towards the virgin bitumen level. The E _∞ parameter is not applicable to evaluate the rejuvenation efficiency of engine-oil and naphthenic-oil rejuvenators, but the positive E _∞R values of bio-oil and aromatic-oil rejuvenated bitumen are detected. All rejuvenators fail to restore the β parameter of aged bitumen. The τ and CI parameters are selected as critical evaluation indicators from the perspective of viscoelastic models and chemical characteristics. Linear correlations between all rheological parameters and chemical indices are observed and established.