Bitumen exhibits viscoelastic properties, showcasing both viscous and elastic behaviors, which are characterized by the phase angle and dynamic modulus. Issues like early fatigue fractures, rutting, and permanent deformations in bituminous asphalt pavements arise due to moisture susceptibility, high-temperature deformation, low-temperature cracking, and overloading. These distresses result in potholes, alligator cracks, and specific deformations that lead to early pavement failure, increasing rehabilitation and maintenance costs. To address these issues, this study examines the dynamic modulus and phase angle behavior of Styrene Butadiene Styrene (SBS) modified and unmodified asphalt mixtures. SBS was incorporated in various proportions, ranging from 2 to 7% by the weight of bitumen. The asphalt mixture performance tester (AMPT) was utilized to measure the dynamic modulus at temperatures of 4.4, 21.1, 37.8, and 54.4 °C, and frequencies of 0.1, 0.5, 1, 5, 10, and 25 Hz. The study found significant correlations between dynamic modulus, temperature, loading frequency, and SBS content. Additionally, Multi Expression Programming (MEPX) and regression modeling were employed to estimate the dynamic modulus of SBS-modified HMA. Results indicated that increasing SBS content up to 7% decreased penetration and ductility values by up to 46% and 56%, respectively, while raising the softening point by 63% due to increased stiffness. The blend with 6% SBS by weight of bitumen exhibited superior performance compared to other mixtures. Phase angle initially increased with rising temperature, peaking at 37.8 °C at lower frequencies, and continued to increase at higher frequencies. Isothermal and isochronal plots showed that the 0% SBS mix had a higher phase angle due to increased bitumen content. Overall, the HMA mix with 6% SBS provided the best outcomes.

Flexible pavements are mostly affected by meteorological factors in addition to traffic loads, which results in premature pavement failures like rutting and moisture-induced damage. This study focuses on the impacts of adding various contents of nano-silica (NS), i.e., 2%, 4%, 6%, and 8% (percentage weight of asphalt), along with a constant value of 4.5% styrene-butadiene-styrene (SBS). To assess the effectiveness of modified and unmodified mixtures, the indirect tensile strength (ITS) test, resilient modulus (MR) test, and wheel tracking test were conducted. The MR test was performed at dual temperature values, i.e., 25 °C and 40 °C, and demonstrated different metrological conditions in this region. The tensile strength ratio was used to estimate the mitigation of water losses in hot mix asphalt (HMA) mixtures (specimens) utilizing ITS test results of the conditioned and unconditioned specimens. Moreover, a model was developed for the rutting potential of the modified specimens using multi expression programming (MEP), a sophisticated technique that employs experimental data and suggests an equation for different input variables. The results indicated that the addition of NS to SBS-modified bitumen enhanced different mechanical properties of the specimens, including the stiffness and moisture and rutting resistances. The temperature had adverse effects on the stiffness of the specimens, while the modifiers had a direct relationship with the stiffness. The two-way factorial method justified the effect of the temperature and modifiers on MR with 95% precision, while the MEP model for rutting showed an R² value of >0.95, which revealed a good relationship between the experimental and predicted data. Furthermore, NS and SBS had a good impact on the mechanical properties of the HMA specimens.

Concrete is used worldwide as a construction material in many projects. It exhibits a brittle nature, and fibers' addition to it improves its mechanical properties. Polypropylene (PP) fibers stand out as widely employed fibers in concrete. However, conventional micro-PP fibers pose challenges due to their smooth texture, affecting bonding within concrete and their propensity to clump during mixing due to their thin and soft nature. Addressing these concerns, a novel type of PP fiber is proposed by gluing thin fibers jointly and incorporating surface indentations to enhance mechanical anchorage. This study investigates the incorporation of macro-PP fibers into high-strength concrete, examining its fresh and mechanical properties. Three different concrete strengths 40 MPa, 45 MPa, and 50 MPa, were studied with fiber content of 0–1.5% v/f. ASTM specifications were utilized to test the fresh and mechanical properties, while the RILEM specifications were adopted to test the bond of bar reinforcements in concrete. Test results indicate a decrease in workability, increased air content, and no substantial shift in fresh concrete density. Hardened concrete tests, adding macro-PP fibers, show a significant increase in splitting tensile strength, bond strength, and flexural strength with a maximum increase of 34.5%, 35%, and 100%, respectively. Concrete exhibits strain-hardening behavior with 1% and 1.5% fiber content, and the flexural toughness increases remarkably from 2.2 to 47.1. Thus, macro PP fibers can effectively improve concrete's mechanical properties and resistance against crack initiation and spread.

Bitumen shows visco-elastic behavior, exhibiting both elastic and viscous properties as predicted by dynamic response and phase angle. Modern asphalt bituminous pavements face issues such as early-stage fatigue cracks, rutting, and permanent deformations due to low-temperature cracking, high-temperature deformation, moisture susceptibility, and overloading. These pavement distresses result in the formation of potholes, alligator cracks, and various deformations, which accelerate the need for rehabilitation and maintenance. To address these concerns, this study focused on utilizing Nitrile Butadiene Rubber derived from surgical gloves as an additive in conventional asphalt pavements to assess its effect on stiffness. Nitrile Butadiene Rubber was added in intervals of 2%, 4%, 6%, and 8% to conventional bituminous pavement. The rheological properties, marshall properties, dynamic modulus, and phase angle were evaluated for varying percentages of Nitrile Butadiene Rubber at different temperature, and frequency. The dynamic response was determined using a simple performance tester at four different temperatures (4.4 °C, 21.1 °C, 37.8 °C, and 54.4 °C) and six different frequencies (0.1, 0.5, 1, 5, 10, and 25 Hz). Response surface methodology was employed to establish a relationship between input and output variables and to optimize the amount of Nitrile Butadiene Rubber in the mix based on dynamic modulus and phase angle. The study concluded that adding up to 6% of Nitrile Butadiene Rubber improved Marshall stability, while higher percentages led to reduced stability. A similar trend was observed in the dynamic modulus, which peaked with the addition of 6% Nitrile Butadiene Rubber, regardless of frequency and temperature. The response surface methodology model indicated that coupling the percentage of Nitrile Butadiene Rubber with frequency increased the dynamic modulus at a constant temperature, with the highest value occurring at 4.4 °C. However, the dynamic modulus decreased as the temperature rose for the same combinations of Nitrile Butadiene Rubber percentages and frequencies. Numerical optimization suggested that a maximum of 5.9% Nitrile Butadiene Rubber should be added to achieve the highest dynamic modulus and lowest phase angle.

Millions of lives are lost in road accidents annually, underscoring the severity of traffic incidents. Despite numerous studies on crash severity forecasting with static ensemble machine learning, the potential of Dynamic Ensemble Selection (DES) for combining classifiers and improving performance remains relatively unexplored in this field. In contrast to previous research focused on static ensemble machine learning models, our study introduces an interpretable Bayesian Optimized Dynamic Ensemble Selection strategy, reinforced with instance hardness and region of competence considerations, for crash risk assessment. Utilizing six algorithms – Random Forest, Extra Tree, Cat Boost, Light GBM, KT Boost, and XGBoost – optimized through cross-validation, we employed bagging as a hybrid ensemble selection criterion to ensure ensemble diversity for DES models. Addressing class imbalance with cost-sensitive learning, we evaluated six DES algorithms, ‘KNORA-E, KNORA-U, DES-KNN, DES-RRC, DES-KL, and META-DES’ using a pool of input classifiers, based on key metrics such as Balanced Accuracy Score, Sensitivity, G-mean, and Matthews Correlation Coefficient. Our results revealed that the KNORA-E ensemble with Cat Boost achieved the highest performance, with a Balanced Accuracy (BCA) of 69.3%, Matthews Correlation Coefficient (MCC) of 0.36, and a G-mean score of 69.10%. DES-RRC demonstrated notable performance across all base classifiers, particularly in addressing imbalanced classes, and DES-RRC with LightGBM emerged as the second-best performer. SHAP analysis for the KNORAE-Cat Boost model identified key factors in crash severity as, road user gender, airbag deployment, vehicle age and road surface conditions. This approach could be a valuable tool for informed decision-making and targeted interventions.