Natural weak cohesive subgrade soils are considered low quality foundations for pavement structures that may lead to significant pavement distresses. Several solutions can be proposed to enhance pavement behavior under cyclic loads such as stabilizing clays with cement or lime, removing and replacing weak material, or increasing the base thickness to reduce the stresses on subgrade. Despite the effectiveness of using additives like lime and cement for subgrade stabilization, cement and lime are considered environmentally unfriendly owing to the generated emission during their production. Recently, several researchers have shown the potential of using biopolymers as alternative for traditional additives. This study investigates the feasibility of using sodium alginate biopolymer for treating weak cohesive subgrades particularly under repeated traffic loads for pavement construction applications. Two different soils (clay and silt), representing a typical range of cohesive soils in the delta region in Egypt, were investigated. The sodium alginate was added to the cohesive soils in the range of 0% to 6% using two different mixing methods (wet and dry). The experimental program included basic engineering tests, resilient modulus, and unconfined compressive strength tests, along with other chemical tests such as Fourier transform infrared spectroscopy (FTIR) and thermal gravimetric analysis (TGA). In addition, scanning electronic microscope (SEM) images were performed for both untreated and treated soils at different alginate contents. Results showed that the resilient modulus of the treated subgrade soils generally increased with the increase in sodium alginate content up to an optimum value of 2% and 4% for clayey and silty soils, respectively. This significant improvement was reached in the first 4 days of curing with continuous gain of strength up to 28 days. This increase in resilient modulus was found to be dependent on the soil type, alginate concentration, curing time, and treatment method. The microlevel tests, TGA, SEM, and FTIR, confirmed the presence of the cementation polymer due to sodium alginate that increased the bond and cross-linking between particles, and consequently an increase in the soil stiffness and strength occurred.

This paper presents experimental results on the effect of matric suction on the resilient modulus of four recycled unbound granular materials. The recycled materials were prepared at moisture contents ranging between 70% and 90% of optimum moisture content (OMC) and tested in a repeated load triaxial test (RLTT) apparatus under various stress regimes. Soil-water characteristic curves (SWCC) were established for each material by preparing samples at various moisture contents and measuring matric suction with filter papers. To obtain the wet end of the SWCC, further samples were conditioned on a tension plate at suctions controlled by the hanging water column method. Some published models for prediction of resilient modulus were applied to the experimental data, but the correlations were unsatisfactory generally, and so an improved model was sought. Subsequently, a model with four terms and six constants was developed, which followed the general power law. A single set of material constants was found for all recycled materials to provide satisfactory predictions of resilient modulus (R² = 0.88), over a wide range of stresses and moisture states.

The mineral filler is an integral constituent of asphalt mixture, which affects its performance. This research investigates the effect of using four local industrial waste/by-product materials (marble, granite, steel slag and hydrated lime powder) as mineral fillers on asphalt mixtures characteristics. The detailed performance of these materials as well as asphalt binder and asphalt mixture characteristics are presented. In Egypt, limestone powder is a typical mineral filler in asphalt mixture composition. A control asphalt mixture with limestone mineral filler is designed using Marshall method. Other four asphalt mixtures are prepared using similar constituents and volumetrics except replacing the limestone filler with other types of industrial waste/by-product materials, while maintaining the same percentage of mineral filler as in the control mix. Marshall stability and moisture-induced damage of the investigated asphalt mixtures are determined and compared to the control asphalt mixture results. In addition, the performance characteristics of the investigated mixtures are evaluated by conducting cyclic performance tests including the dynamic modulus (E*) and flow number (FN). Testing results show that the asphalt mixture containing marble as a filler yields the highest stability; while, the hydrated lime filler noticeably enhances the performance of the asphalt mixture in terms of moisture damage resistance, E*, and FN. Unexpectedly, steel slag filler does not improve the asphalt mixture performance. Finally, the stiffness of the investigated mixtures is evaluated using three well-known E*-predictive models (NCHRP 1-37A, Hirsh, and NCHRP 1-40D); while, the resistance of the investigated mixtures to permanent deformation is evaluated using a calibrated version of Apeagyei-literary model based on the investigated mixtures to predict FN at 54.4°C and 1 Hz frequency.

Electric vehicles (EVs) have emerged as a transformative solution for reducing carbon emissions and promoting environmental sustainability in the automotive industry. However, the widespread adoption of EVs in the United States faces challenges, including high costs and unequal access to charging infrastructure. To overcome these barriers and ensure equitable EV usage, a comprehensive understanding of the intricate interplay among social, economic, and environmental factors influencing the placement of charging stations is crucial. This study investigates the key variables that contribute to demographic disparities in the accessibility of EV charging stations (EVCSs). We analyze the impact of various factors, including EV percentage, geographic area, population density, available electric vehicle supply equipment (EVSE) ports, electricity sources, energy costs, per capita and average family income, traffic patterns, and climate, on the placement of EVCSs in nine selected US states. Furthermore, we employ predictive modeling techniques, such as linear regression and support vector machine, to explore unique nuances in EVCS installation. By leveraging real-world data from these states and the identified variables, we forecast the future distribution of EVCSs using machine learning. The linear regression model demonstrates exceptional effectiveness, achieving 90% accuracy, 94% precision, 89% recall, and a 91% F1 score. Both graphical analysis and machine learning converge on a significant finding: Texas emerges as the most favorable state for optimal EVCS placement among the studied areas. This research enhances our understanding of the multifaceted dynamics that govern the accessibility of EVCSs, thereby informing the development of policies and strategies to accelerate EV adoption, reduce emissions, and promote social inclusivity.

This paper presents the engineering characteristics of reclaimed asphalt pavement (RAP), blended with virgin aggregate for unbound base and subbase layers. The proportions of RAP were 0%, 20%, 60%, 80%, and 100% by total mass of the blend. The experimental laboratory testing included index properties such as gradation, modified Proctor compaction, California Bearing Ratio, and hydraulic conductivity. Repeated load resilient modulus testing was conducted on the blends. The impact of load duration on resilient modulus was also investigated. A strong inverse trend was found between resilient modulus and California Bearing Ratio. An accurate model was proposed for the prediction of the resilient modulus as a function of stress state and reclaimed asphalt pavement percentage with coefficient of determination of 0.94. Finally, multilayer elastic analysis of typical pavement sections with the base layer constructed of virgin aggregate and reclaimed asphalt pavement blends showed good performance.

Deteriorating road infrastructure is a global concern, especially in low-income countries where financial and technological constraints hinder effective monitoring and maintenance. Traditional methods, like inertial profilers, are expensive and complex, making them unsuitable for large-scale use. This paper explores the integration of cost-effective, scalable smartphone technologies for road surface monitoring. Smartphone sensors, such as accelerometers and gyroscopes, combined with data preprocessing techniques like filtering and reorientation, improve the quality of collected data. Machine learning algorithms, particularly CNNs, are utilized to classify road anomalies, enhancing detection accuracy and system efficiency. The results demonstrate that smartphone-based systems, paired with advanced data processing and machine learning, significantly reduce the cost and complexity of traditional road surveys. Future work could focus on improving sensor calibration, data synchronization, and machine learning models to handle diverse real-world conditions. These advancements will increase the accuracy and scalability of smartphone-based monitoring systems, particularly for urban areas requiring real-time data for rapid maintenance.