Search Results (344)

Accurately modelling and simulating the stiffness modulus of asphalt mixtures is essential for reliable pavement design and performance prediction under varying environmental and loading conditions. The preceding is commonly achieved through master curves, which relate stiffness to loading frequency at a reference temperature. However, conventional master curves face two primary limitations. Firstly, temperature is not treated as a state variable; instead, its effect is indirectly considered through shift factors, which can introduce inaccuracies due to their lack of thermodynamic consistency across the entire range of possible temperatures. Secondly, conventional master curves often encounter convergence difficulties when calibrated with experimental data constrained to a narrow frequency spectrum. In order to address these shortcomings, this investigation proposes a novel formulation known as the Thermo-Stiffness Integration (TSI) model, which explicitly incorporates both temperature and frequency as state variables to predict the stiffness modulus directly, without relying on supplementary expressions such as shift factors. The TSI model is built on thermodynamics-based principles (such as Eyring’s rate theory and activation free energy) and leverages the time–temperature superposition principle to create a physically consistent representation of the mechanical behaviour of asphalt mixtures. This manuscript presents the development of the TSI model along with its application in a case study involving eight asphalt mixtures, including four hot-mix asphalts and four warm-mix asphalts. Each type of mixture contains recycled concrete aggregates at replacement levels of 0%, 15%, 30%, and 45% as partial substitutes for coarse natural aggregates. This diverse set of materials enables a robust evaluation of the model’s performance, even under non-traditional mixture designs. For this case study, the TSI model enhances computational stability by approximately 4 to 45 times compared to conventional master curves. Thus, the main contribution of this research lies in establishing a valuable mathematical tool for both scientists and practitioners aiming to improve the design and performance assessment of asphalt mixtures in a more physically realistic and computationally stable approach. Full article

The United States produces approximately 500 million tons of asphalt mixtures annually, while generating vast amounts of waste materials that could be repurposed for sustainable infrastructure. Each year, 1.4 billion gallons of lubricating oils are available for reuse and recycling. Additionally, 280 million tires are discarded, contributing to significant environmental challenges. Given the critical role of the roadway network in economic growth, mobility, and infrastructure sustainability, there is a pressing need for innovative material solutions that integrate recycled materials without compromising performance. This study introduces a Rubberized-Aerogel Composite (RaC), a novel asphalt binder modifier combining crumb rubber, recycled oil, and a silica-based aerogel to enhance the sustainability and durability of asphalt pavements. The research methodology involved blending the RaC with the PG70-10 asphalt binder at a 5:1 ratio and conducting comprehensive laboratory tests on binders and mixtures, including rheology, thermal conductivity (TC), specific heat capacity (Cp), the Hamburg Wheel-Tracking Test (HWTT), and indirect tensile strength (IDT). Pavement performance was simulated using AASHTOWare Pavement ME under hot and cold climates with thin and thick pavement structures. Results showed that RaC-modified binders reduced thermal conductivity by up to 30% and increased specific heat capacity by 15%, improving thermal stability. RaC mixtures exhibited a 50% reduction in rut depth in the HWTT and lower thermal expansion/contraction coefficients. Pavement ME simulations predicted up to 40% less permanent deformation and 60% reduced thermal cracking for RaC mixtures compared to the controls. RaC enhances pavement lifespan, reduces maintenance costs, and promotes environmental sustainability by repurposing waste materials, offering a scalable solution for resilient infrastructure. Full article

To reduce reflective cracking in asphalt pavements, gravel base layers are commonly employed to disperse stress and delay structural damage. However, the loose nature of gravel bases results in complex interlayer contact conditions, typically involving interlocking between gravel particles in the base and aggregates in the asphalt surface course. In order to accurately simulate this interaction and to improve the interlayer shear performance, a mesoscale finite element model was developed and combined with macroscopic tests. Effects due to the type and amount of binder material, type of asphalt surface layer, and external loading on shear strength were systematically analyzed. The results indicate that SBS (Styrene–Butadiene–Styrene)-modified asphalt provides the highest interlayer strength, followed by SBR (Styrene–Butadiene Rubber)-modified emulsified asphalt and unmodified base bitumen. SBS (Styrene–Butadiene–Styrene)-modified asphalt achieves optimal interlaminar shear strength at a coating rate of 0.9 L/m². Additionally, shear strength increases with applied load but decreases with increasing void ratio and the nominal maximum aggregate size of the surface course in the analyzed spectra. Based on simulation and experimental data, an equivalent macro–meso predictive model relating shear strength to key influencing factors was established. This model effectively bridges mesoscale mechanisms and practical engineering applications, providing theoretical support for the design and performance optimization of asphalt pavements with gravel bases. Full article

The accurate prediction of the pavement structural modulus is crucial for maintenance planning and life-cycle assessment. While recent deep learning models have improved predictive accuracy using Falling Weight Deflectometer data, challenges remain in effectively structuring time-series inputs and ensuring robustness against noise measurement. This paper presents an integrated framework that combines systematic time-step modeling with perturbation-based robustness evaluation. Five distinct input sequencing strategies (Plan A through Plan E) were developed to investigate the impact of temporal structure on model performance. A hybrid Wide & Deep ResRNN architecture incorporating SimpleRNN, GRU, and LSTM components was designed to jointly predict four-layer moduli. To simulate real-world sensor uncertainty, Gaussian noise with ±3% variance was injected into inputs, allowing the Monte-Carlo-style estimation of confidence intervals. Experimental results revealed that time-step design plays a critical role in both prediction accuracy and robustness, with Plan D consistently achieving the best balance between accuracy and stability. These findings offer a practical and generalizable approach for deploying deep sequence models in pavement modulus prediction tasks, particularly under uncertain field conditions. Full article

This research addresses the challenge of predicting operating vehicles’ speeds (V85) on single-carriageway road tangents. While most previous models rely on preceding segment speeds or focus on curves, this research develops an independent prediction model specifically for road tangents, based on empirical data collected in Croatia. A total of 46 locations across 23 road cross-sections were analysed, with operating speeds measured using field radar surveys and fixed traffic counters. Following a comprehensive correlation and multicollinearity analysis of 24 geometric, environmental, and traffic-related variables, a multiple linear regression model was developed using a training dataset (36 locations) and validated on a separate test set (10 locations). The model includes nine statistically significant predictors: shoulder type (gravel), edge line quality (excellent and satisfactory), pavement quality (excellent), average summer daily traffic (ASDT), crash ratio, edge lane presence, overtaking allowed, and heavy goods vehicle share. The model demonstrated strong predictive performance (R² = 0.89, RMSE = 5.24), with validation results showing an average absolute deviation of 2.43%. These results confirm the model’s reliability and practical applicability in road design and traffic safety assessments. Full article

Geopolymers are an environmentally sustainable class of low-calcium alkali-activated materials (AAMs), distinct from high-calcium C–A–S–H gel systems. Synthesized from aluminosilicate-rich precursors such as fly ash, metakaolin, slag, waste glass, and coal gasification fly ash (CGFA), geopolymers offer a significantly lower carbon footprint, valorize industrial by-products, and demonstrate superior durability in aggressive environments compared to Ordinary Portland Cement (OPC). Recent advances in thermodynamic modeling and phase chemistry, particularly in CaO–SiO₂–Al₂O₃ systems, are improving precursor selection and mix design optimization, while Artificial Neural Network (ANN) and hybrid ML-thermodynamic approaches show promise for predictive performance assessment. This review critically evaluates geopolymer chemistry and composition, emphasizing precursor reactivity, Si/Al and other molar ratios, activator chemistry, curing regimes, and reaction mechanisms in relation to microstructure and performance. Comparative insights into alkali aluminosilicate (AAS) and aluminosilicate phosphate (ASP) systems, supported by SEM and XRD evidence, are discussed alongside durability challenges, including alkali–silica reaction (ASR) and shrinkage. Emerging applications ranging from advanced pavements and offshore scour protection to slow-release fertilizers and biomedical implants are reviewed within the framework of the United Nations Sustainable Development Goals (SDGs). Identified knowledge gaps include standardization of mix design, LCA-based evaluation of novel precursors, and variability management. Aligning geopolymer technology with circular economy principles, this review consolidates recent progress to guide sustainable construction, waste valorization, and infrastructure resilience. Full article

This study investigates the deterioration of skid resistance and surface macrotexture following preventive maintenance using micro-milling techniques. Field data were collected from 31 asphalt pavement sections located across four climatic zones in Texas. The data encompasses a variety of surface types, milling depths, operational speeds, and drum configurations. A standardized data collection protocol was followed, with measurements taken before milling, immediately after treatment, and at 3, 6, 12, and 18 months post-treatment. Skid number and Mean Profile Depth (MPD) were used to evaluate surface friction and texture characteristics. The dataset was reformatted into a time-series structure with 930 observations, including contextual variables such as climatic zone, treatment parameters, and baseline surface condition. A comparative modeling framework was applied to predict the deterioration trends of both skid resistance and macrotexture over time. Eight regression models, including linear, tree-based, and ensemble methods, were evaluated alongside a time series Transformer model. The results show that the Transformer model achieved the highest prediction accuracy for skid resistance (R² = 0.981), while Random Forest performed best for macrotexture prediction (R² = 0.838). The findings indicate that the degradation of surface characteristics after preventive maintenance is non-linear and influenced by a combination of environmental and operational factors. This study demonstrates the effectiveness of data-driven modeling in supporting transportation agencies with pavement performance forecasting and maintenance planning. Full article

Accurate and reliable modeling of pavement deterioration is critical for effective infrastructure management. This study proposes a probabilistic machine learning framework using Bayesian-optimized Natural Gradient Boosting (BO-NGBoost) to predict the International Roughness Index (IRI) of asphalt pavements in cold climates. A dataset only for cold regions was constructed from the Long-Term Pavement Performance (LTPP) database, integrating multiple variables related to climate, structure, materials, traffic, and constructions. The BO-NGBoost model was evaluated against conventional deterministic models, including artificial neural networks, random forest, and XGBoost. Results show that BO-NGBoost achieved the highest predictive accuracy (R² = 0.897, RMSE = 0.184, MAE = 0.107) while also providing uncertainty quantification for risk-based maintenance planning. BO-NGBoost effectively captures long-term deterioration trends and reflects increasing uncertainty with pavement age. SHAP analysis reveals that initial IRI, pavement age, layer thicknesses, and precipitation are key factors, with freeze–thaw cycles and moisture infiltration driving faster degradation in cold climates. This research contributes a scalable and interpretable framework that advances pavement deterioration modeling from deterministic to probabilistic paradigms and provides practical value for more uncertainty-aware infrastructure decision-making. Full article

This study investigates the evolution laws and prediction models of anti-skid and wear-resistant performance for asphalt pavements during the operation period. Using a combination of indoor accelerated wear tests and field detection, mixed specimens are prepared with SBS modified asphalt, limestone, and basalt aggregates. Through accelerated wear tests of different durations, the structural depth and friction coefficient are measured. Combined with the field data from the G56 K2319 section of the Hangrui Expressway, the decay laws of anti-skid performance are analyzed, and prediction models are established. The results show that the anti-skid performance of basalt mixtures is superior to that of limestone. The deepened structure technology significantly enhances the performance of basalt but has a negative impact on the pendulum value of limestone. The influence degrees of wear duration, aggregate type, and deepened structure state on structural depth and pendulum value vary. The initial structural depth of basalt mixtures (0.85 mm) is 11.8% higher than that of limestone (0.76 mm). The longitudinal pendulum value of basalt (44) is 10% higher than that of limestone (40), while the transverse pendulum value of limestone (50) is 4.2% higher than that of basalt (48). After 21 h of wear, the structural depth of basalt (0.68 mm) is 4.6% higher than that of limestone (0.65 mm), with a decay rate 23.6% lower. The pendulum value of basalt remains above 50, while limestone’s longitudinal pendulum value drops to 36 (10% lower than its initial value), even below the unmodified state. The influence order for structural depth is deepened structure state > wear duration > aggregate type, and for lateral pendulum value, it is wear duration > deepened structure state > aggregate type. There is a significant linear relationship between structural depth/pendulum value and wear duration, and the prediction models are reliable. The indoor accelerated wear of 44.5 h is equivalent to the field operation wear of 3 years. The research findings provide a theoretical basis for the evaluation of anti-skid performance, maintenance decision-making, and material optimization of asphalt pavements. Full article

Fatigue failure of bitumen significantly influences the durability and service life of asphalt pavement. Current fatigue tests have drawbacks such as long durations, unrealistic traffic loading simulations, and difficulties of identifying failure mechanisms. Similarly, existing prediction models are often overly complex and inaccurate. To solve these drawbacks, in this study, a novel accelerated cyclic shear test in stress-controlled mode using a dynamic shear rheometer was introduced to evaluate the fatigue performance and reveal the fatigue failure mechanism of bitumen. The sigmoidal function was applied to develop a simplified fatigue failure prediction model for bitumen through stress and temperature shifts. The results demonstrate that bitumen’s response under the newly proposed loading method aligns consistently with behaviour characteristic of a plasticity-controlled failure mechanism. The variable parameter load ratio significantly influenced the bitumen’s time-to-failure, which increased as the load ratio decreased. Bitumen exhibited the longest time-to-failure when the load ratio (minimum stress/maximum stress) was 0.1. The developed model effectively predicted the time-to-failure of bitumen across different load ratios and under various temperature and stress conditions. Full article

This research aimed to develop a comprehensive decision-making model for road rehabilitation, with the goals of extending pavement service life, minimizing major repairs, and improving the efficiency of investment and resource planning. The proposed methodology integrates structural condition, functional performance, and total economic value across the pavement lifecycle. It enables engineers and road managers to make informed decisions based on structural capacity, functional performance, asset value, and optimized rehabilitation strategies. The model was validated through case studies using data from Central European roads and accelerated pavement testing. It compared conventional and high-modulus asphalt overlays of equal thickness, demonstrating that a 3000 MPa increase in modulus extended residual life by over 30% and raised structural value by EUR 5.8/m². This approach enhances planning and prioritization of rehabilitation activities, supports the use of higher-quality materials, reduces lifecycle costs and CO₂ emissions, and facilitates integration with asset management systems. By linking pavement design, performance prediction, and asset management, the model supports strategic decision-making under performance and budget constraints. Full article

This study evaluates the rheological behavior and mechanical performance of Ultra-High-Performance Fiber-Reinforced Concrete (UHPFRC) mixes with varying superplasticizer dosages, aiming to optimize their use in pavement rehabilitation overlays on sloped surfaces. A reference self-compacting UHPFRC mix was modified by reducing the superplasticizer-to-binder ratio in incremental steps, and the resulting mixes were assessed through rheometry, mini-Slump, and Abrams cone tests. Key rheological parameters—static and dynamic yield stress, plastic viscosity, and thixotropy—were determined using the modified Bingham model. The results showed that reducing superplasticizer content increased yield stress and viscosity, enhancing thixotropic behavior while maintaining ultra-high compressive (≥130 MPa) and flexural strength (≥20 MPa) at 28 days. A predictive model was validated to estimate the critical yield stress needed for overlays on slopes. Among the evaluated formulations, the SP-2 mix met the stability and performance criteria and was successfully tested in a prototype overlay, demonstrating its viability for field application. This research confirms the potential of rheology-tailored UHPFRC as a high-performance solution for durable and stable pavement overlays in demanding geometric conditions. Full article

With rising global temperatures and increasing sustainability demands, the need for advanced pavement solutions has never been greater. This study breaks new ground by integrating phase change materials (PCMs), including paraffin-based wax (Rubitherm RT55), hydrated salt (Climator Salt S10), and fatty acid (lauric acid), as binder modifiers within warm mix asphalt (WMA) mixtures. Moving beyond the traditional focus on binder-only modifications, this research utilizes recycled cigarette filters (CFs) as a dual-purpose fiber additive, directly reinforcing the asphalt mixture while simultaneously transforming a major urban waste stream into valuable infrastructure. The performance of the developed WMA mixture has been evaluated in terms of stiffness behavior using an Indirect Tensile Strength Modulus (ITSM) test, permanent deformation using a static creep strain test, and rutting resistance using the Hamburg wheel-track test. Laboratory tests demonstrated that the incorporation of PCMs and recycled CFs into WMA mixtures led to remarkable improvements in stiffness, deformation resistance, and rutting performance. Modified mixes consistently outperformed the control, achieving up to 15% higher stiffness after 7 days of curing, 36% lower creep strain after 4000 s, and 64% reduction in rut depth at 20,000 passes. Cost–benefit analysis and service life prediction show that, despite costing USD 0.71 more per square meter with 5 cm thickness, the modified WMA mixture delivers much greater durability and rutting resistance, extending service life to 19–29 years compared to 10–15 years for the control. This highlights the value of these modifications for durable, sustainable pavements. Full article

This research aimed to evaluate the impact of using brick waste powder (BWP) and varying lengths of polyester fibers (PFs) on the performance properties of asphalt concrete (AC) mixtures. BWP was utilized as a replacement for traditional limestone powder (LS) filler, while PFs of three lengths (3 mm, 8 mm, and 15 mm) were introduced. The study employed the response surface methodology (RSM) for experimental design and analysis of variance (ANOVA) to identify the influence of BWP and PF on the selected performance indicators. These indicators included bulk density, air voids, voids in the mineral aggregate, voids filled with asphalt, Marshall stability, Marshall flow, Marshall quotient, indirect tensile strength, wet tensile strength, and the tensile strength ratio. The findings demonstrated that BWP improved moisture resistance and the mechanical performance of AC mixes. Moreover, incorporating PF alongside BWP further enhanced these properties, resulting in superior overall performance. Using multi-objective optimization through RSM-based empirical models, the study identified the optimal PF length of 5 mm in combination with BWP for achieving the best AC properties. Validation experiments confirmed the accuracy of the predicted results, with an error margin of less than 8%. The study emphasizes the intriguing prospect of BWP and PF as sustainable alternatives for improving the durability, mechanical characteristics, and cost-efficiency of asphalt pavements. Full article

Traditional Pavement Condition Index (PCI) assessments are highly resource-intensive, demanding substantial time and labor while generating significant carbon emissions through extensive field operations. To address these sustainability challenges, this research presents an innovative methodology utilizing Gene Expression Programming (GEP) to determine PCI values based on International Roughness Index (IRI) measurements from Iraqi road networks, offering an environmentally conscious and resource-efficient approach to pavement management. The study incorporated 401 samples of IRI and PCI data through comprehensive visual inspection procedures. The developed GEP model exhibited exceptional predictive performance, with coefficient of determination (R²) values achieving 0.821 for training, 0.858 for validation, and 0.8233 overall, successfully accounting for approximately 82–85% of PCI variance. Prediction accuracy remained robust with Mean Absolute Error (MAE) values of 12–13 units and Root Mean Square Error (RMSE) values of 11.209 and 11.00 for training and validation sets, respectively. The lower validation RMSE suggests effective generalization without overfitting. Strong correlations between predicted and measured values exceeded 0.90, with acceptable relative absolute error values ranging from 0.403 to 0.387, confirming model effectiveness. Comparative analysis reveals GEP outperforms alternative regression methods in generalization capacity, particularly in real-world applications. This sustainable methodology represents a cost-effective alternative to conventional PCI evaluation, significantly reducing environmental impact through decreased field operations, lower fuel consumption, and minimized traffic disruption. By streamlining pavement management while maintaining assessment reliability and accuracy, this approach supports environmentally responsible transportation systems and aligns contemporary sustainability goals in infrastructure management. Full article