Chiara Riccardi

Asphalt pavements suffer from progressive deterioration during service life due to aging, while conventional polymer modifiers raise environmental concerns regarding recyclability and volatile organic compound emissions. Functionalized-biochar derived from renewable biomass offers a sustainable alternative through its tunable surface chemistry and porous structure. This study develops and evaluates a nitrogen-rich, surface-functionalized biochar as a multifunctional asphalt binder modifier and elucidates the synergistic roles of inherent nitrogen and post-synthetic functionalization in governing binder performance. To this end, soybean powder was pyrolyzed to prepare the biochar precursor, which was subsequently characterized to determine its suitable pyrolysis temperature and surface properties. These analyses identified 300 °C as the preferable pyrolysis temperature within the tested range of 200–500 °C, maximizing biochar yield and achieving favorable surface physicochemical properties including the iodine adsorption value, oil absorption value, surface functional groups and pore morphology. To improve surface functionality, the biochar underwent a two-step modification: nitric acid oxidation followed by hydroxymethylation, hereinafter referred to as functionalized-biochar. The influence of functionalized-biochar content on asphalt binder performance was evaluated across dosages of 10–20 wt%, and 15 wt% was identified as the recommended content. Asphalt binder modified with 15 wt% functionalized-biochar exhibited improved high-temperature performance, as evidenced by enhanced rutting resistance factor G*/sin δ values obtained from dynamic shear rheometer testing. Aging resistance also improved, reflected in a lower complex modulus aging index and a higher phase angle aging index. At 15 wt% dosage, the softening point increased by 6 °C, and ductility rose by 10.6 cm relative to the base asphalt (1.9 cm), corresponding to a 557.9% improvement. Penetration declined by 17%, while the complex modulus aging index decreased from 3.44 to 1.68. Notably, the experimental program in this study was limited to binder-scale characterization.

The qualitative evolution of SARA fractions during asphalt binder aging is well established, but further validation is needed to demonstrate whether automated HPLC-SARA analysis can provide repeatable compositional indicators for systematic aging studies. In this work, the applicability of a previously optimized automated high-performance liquid chromatography (HPLC) workflow for saturate, aromatic, resin, and asphaltene (SARA) fractionation was evaluated for the monitoring of thermo-oxidative aging. Four penetration-grade asphalt binders were subjected to short-term aging and multiple long-term aging cycles, and the resulting SARA distributions were used to calculate the colloidal instability index (I_c). Fourier-transform infrared spectroscopy (FTIR) was used as an independent reference technique to evaluate carbonyl and sulfoxide oxidation indices. The automated HPLC-SARA method provided repeatable compositional indicators across all asphalt binders and aging conditions. As expected, aromatics progressively decreased whereas asphaltenes increased, while saturates showed only limited variations and resins behaved as an intermediate operational fraction. More importantly, I_c increased consistently with aging and showed a strong correlation with the FTIR carbonyl index (R² = 0.84 when all asphalt binders and aging conditions were considered), whereas the sulfoxide index showed a more asphalt binder-dependent response. These results demonstrate that automated HPLC-SARA analysis can provide aging-sensitive compositional and colloidal indicators that are chemically consistent with independent FTIR oxidation markers. The proposed workflow represents a complementary tool for asphalt binder aging research and may support future studies aimed at linking compositional evolution with rheological and durability-related properties.

The construction sector remains one of the largest consumers of raw materials and energy and, therefore, one of the most important fields for implementing circular-economy strategies and low-carbon technologies

To achieve synergistic, efficient degradation of volatile, harmful gases in asphalt and to scientifically quantify inhibitor dosage, this study proposes a dosage optimization method that integrates nonlinear regression with a multi-response satisfaction function. Focusing on a proprietary composite volatile gas suppressant, we systematically measured the concentration trends of ammonia, nitrogen oxides, sulfur dioxide, and hydrogen sulfide emitted from three asphalt systems: base asphalt, SBS modified asphalt (Styrene-Butadiene-Styrene modified asphalt), and rubber modified asphalt under different suppressant dosages (0%, 0.02%, 0.04%, 0.06%, 0.08%, and 0.10%). First, high-precision prediction models (R² > 0.95) were established using nonlinear regression to relate different inhibitor dosages to corresponding gas concentrations. Based on a satisfaction function, the multi-objective degradation effects were normalized into a comprehensive satisfaction index, and the optimal dosage was then determined. The results indicate: (1) the constructed models can accurately predict the concentrations of volatile harmful gases at various dosages; (2) the predicted optimal blending ratios vary by asphalt type, specifically 0.082% for base asphalt, 0.079% for SBS modified asphalt, and 0.080% for rubber modified asphalt; and (3) at the optimal blending ratios, all four gases achieve high and balanced degradation levels, resulting in the best overall degradation performance. At the same time, road performance tests confirmed that this blending ratio has no significant negative impact on the high-temperature and low-temperature stability or water stability of the asphalt mixture. Compared with traditional single-factor empirical methods, this approach represents a methodological upgrade from qualitative description to quantitative prediction, and from single-objective comparison to multi-objective synergistic optimization, providing data and theoretical support for the precise, efficient, and engineering-applicable use of asphalt volatile gas inhibitors.

The composition of asphalts and other heavy petroleum-derived materials is usually subdivided into four fractions called saturates, aromatics, resins, and asphaltenes (SARA), which have different polarities and solubilities. The separation and quantification of these fractions using chromatographic methods provide essential information on the colloidal stability, chemical reactivity, and processing behavior. However, the measured distribution of SARA fractions is inherently dependent on the analytical procedure adopted. Based on a liquid chromatographic separation in open columns, ASTM D4124 is still the most widely applied standard, despite being time-consuming, solvent-intensive, and operator-dependent. Although numerous HPLC-based alternatives have been proposed, their broader implementation has been limited by the methodological variability and the lack of a clear framework for interpreting the results across different separation schemes, particularly for asphaltic systems rich in aromatics, resins and asphaltenes. This work presents an automated HPLC workflow for SARA fractionation which is applied to four different penetration-grade asphalts and systematically compared to ASTM D4124. The study highlights the practical aspects of HPLC implementation, including the critical methodological steps that affect the reproducibility and transferability between laboratories. Additional analyses of ASTM-isolated fractions and selected model compounds provide insights into the origins of systematic differences between HPLC- and ASTM-derived SARA compositions. These differences are shown to arise from distinct separation mechanisms and operational cut points rather than from analytical inaccuracies. This reflects the intrinsically method-dependent nature of SARA classification in materials characterized by a continuous distribution of molecular polarity. By clarifying the methodological and conceptual basis of these differences, this work provides practical guidance for the interpretation of HPLC-SARA data and supports more consistent and transferable approaches to asphalt characterization.

This review examines data-driven road traffic safety modeling, aiming to provide a comprehensive overview of the state-of-the-art and persistent research gaps. The study is structured around data sources, influencing factors, reactive and proactive modeling approaches, and key challenges. Data sources, including crashes, trajectories, traffic, roadway geometry, and environmental data, are first reviewed in the context of reactive and proactive safety analysis. To address the substantial heterogeneity across studies, a vote-counting strategy is adopted to aggregate directional evidence reported in the literature. The synthesis indicates that traffic demand variables exhibit consistently positive associations with crash occurrence, while speed-related effects are strongly context-dependent. Road geometry and surface conditions have largely consistent directional impacts on safety outcomes. From a methodological perspective, reactive approaches remain dominant, while proactive approaches exhibit potential for early risk identification but remain insufficiently validated due to data quality constraints. In addition, empirical evidence on conflict–crash relationships is still limited. Notably, model performance varies substantially across safety tasks, with algorithm effectiveness primarily driven by data structure, outcome definition, and aggregation level, rather than by the intrinsic superiority of any single approach. Overall, this review highlights challenges related to data integration, spatio-temporal modeling, interpretability, and transferability, and provides practical guidance for model selection in operational road safety analysis.