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The superior mechanical qualities of polyurethane have garnered increasing attention for its application in modifying asphalt mixtures. However, polyurethane needs to use polyols to cure, and polyols need to be produced by petroleum refining. As we all know, petroleum is a non-renewable energy source. In order to reduce oil consumption and conform to the trend of a green economy, lignin and chitin were used instead of polyols as curing agents. In this paper, a biological polyurethane-modified asphalt mixture (BPA-16) was designed and compared with a polyurethane-modified asphalt mixture (PA-16) and a matrix asphalt mixture (MA-16). The viscoelastic characteristics of the three asphalt mixtures were evaluated using dynamic modulus, static modulus, and creep tests. The interplay between dynamic and static modulus and frequency is examined, along with the variations in the correlation between dynamic and static modulus. The creep behavior of the mixture was ultimately examined by a uniaxial static load creep test. The findings indicate that the dynamic modulus of BPA-16 exceeds those of PA-16 and MA-16 by 8.7% and 30.4% at 25 Hz and −20 °C, respectively. At 25 Hz and 50 °C, the phase angle of BPA-16 decreases by 26.3% relative to that of MA-16. Lignin and chitin, when utilized as curing agents in place of polyol, can enhance the mechanical stability of asphalt mixtures at low temperatures and diminish their temperature sensitivity. A bio-based polyurethane-modified asphalt mixture can also maintain better elastic properties in a wider temperature range. At −20–20 °C, the dynamic and static moduli of BPA-16, PA-16 and MA-16 are linear, and they can be converted by formula at different frequencies. The failure stages of BPA-16, PA-16, and MA-16 are not observed during the 3600 s creep duration, with BPA-16 exhibiting the least creep strain, indicating that lignin and chitin enhance the resistance to permanent deformation in PU-modified asphalt mixes. [ABSTRACT FROM AUTHOR]

The use of waste, recycled, and modified materials is increasingly popular in roadway construction for sustainability and pavement longevity. This research examines the combination of steel slag (SS) and low-density polyethylene (LDPE), commonly used in plastic bags and steel manufacturing by-products, to mitigate environmental pollution. LDPE was tested as a binder modifier in two bitumen grades, 60–70 and 80–100, at concentrations of 3%, 5%, and 7% by weight. SS was used as a replacement for coarse aggregate. The physical properties of both modified and unmodified bitumen grades and SS were analyzed before creating and testing hot-mix asphalt (HMA) samples. The dynamic modulus of these samples was measured at temperatures of 4.4 °C, 21.1 °C, 37.8 °C, and 54.4 °C with frequencies of 0.1 Hz, 0.5 Hz, 1 Hz, 5 Hz, 10 Hz, and 25 Hz. Master curves were developed, and the dynamic modulus data underwent design of experiment (DOE) and computational intelligence (CI) analyses. Using KENPAVE, a mechanistic–empirical tool, the analysis assessed the design life and enhancements in damage ratio for each modifier and grade. The results showed that adding LDPE increases the softening point and penetration grade but decreases ductility due to increased bitumen stiffness, leading to premature fatigue failure at higher LDPE levels. Both 3% LDPE and 3% SS-modified LDPE improved Marshall Stability and dynamic modulus across all temperature and frequency ranges. Specifically, 3% LDPE enhanced stability by 13–16% and 3% SS-LDPE by 30–32%. The KENPAVE results for 3% LDPE showed a design life improvement of 19–25% and a damage ratio reduction of 15–18%. In comparison, 3% SS-LDPE demonstrated a design life improvement of 50–60% and a damage ratio reduction of 25–35%. Overall, this study concludes that 3% LDPE- and 3% SS-LDPE-modified HMA in both bitumen grades 60–70 and 80–100 provide optimal results for improving pavement performance. [ABSTRACT FROM AUTHOR]

The dynamic modulus of asphalt mixture is an important parameter in the design of asphalt pavement. Extracting material characteristics, dynamic modulus, and phase angle information from a large amount of asphalt concrete datasets using integrated methods is of great significance for optimizing the performance of asphalt pavement. The extreme gradient boost (XGBoost) model aggregated a series of decision tree models through weighted summation to construct a powerful prediction model, while optimizing the loss function to minimize prediction errors. In order to further improve the accuracy of dynamic modulus and phase angle prediction, heuristic algorithms were used to optimize the model. Initially, the basic model was initialized based on samples and the gradient of the loss function of the training data was calculated. Subsequently, XGBoost utilized gradient details to construct a decision tree model, optimized leaf node weights, and updated the model's predictions through weighted summation. During this process, heuristic algorithms are used to optimize the optimal parameters of the entire XGBoost model. The experimental results show that the improved XGBoost model outperforms the original model in all performance evaluation indicators, improving the accuracy of predicting the dynamic modulus and phase angle of asphalt mixtures. [ABSTRACT FROM AUTHOR]

The increasing demand for sustainable construction materials has driven the exploration of alternative fillers in asphalt production. Traditional asphalt mixtures rely heavily on natural aggregates and petroleum-based binders, contributing to environmental degradation. This study proposes an innovative solution by utilizing Crushed Recycled Marble Stone Powder (CRMSP) as a sustainable filler in SBS polymer-modified asphalt containing high volumes of recycled tire rubber, addressing both resource depletion and waste management concerns. A total of 10 asphalt mixes were formulated with varying CRMSP content (0–100% as a replacement for conventional filler) and SBS polymer (3–5%), and their performance was evaluated through Marshall stability, flow, volumetric properties, and dynamic modulus tests. The results demonstrate that incorporating CRMSP up to 75% significantly enhances asphalt's mechanical properties. The 75% CRMSP mix showed superior stability (19.2 kN, 24.1% improvement), flow (4.6 mm, 4.5% improvement), and resistance to rutting (lowest rut depth: 0.18 mm, 16.7% reduction) compared to the control mixture. Dynamic modulus testing further confirmed the improved resistance to deformation, with the 75% CRMSP mix exhibiting the highest modulus (6.9 GPa, 15.0% improvement). This research highlights the potential of CRMSP as an innovative and eco-friendly alternative filler, improving asphalt performance while reducing environmental impact. By offering a sustainable way to recycle marble waste and tire rubber, this study paves the way for greener, cost-effective asphalt formulations. Future studies should focus on real-world applications, durability, and long-term performance to validate the potential of CRMSP-modified asphalt in commercial use. [ABSTRACT FROM AUTHOR]

The dynamic modulus (|E*|) of hot-mix asphalt (HMA) is a crucial mechanistic characteristic essential in defining the strain response of asphalt concrete (AC) mixtures under varying loading rates and temperatures. This paper aims to conduct a comprehensive investigation of classical machine learning (ML) and deep learning (DL) algorithms as applied to the prediction of |E*| and compare their performance with renowned |E*| regression models (Witczak NCHRP 1-37A, Witczak NCHRP 1-40D, and Hirsch). Eight state-of-the-art ML and DL algorithms are attempted with diverse structures, including multiple linear regression (MLR), decision trees (DT), support vector regression (SVR), ensemble trees (ET), Gaussian process regression (GPR), artificial neural networks (ANN), recurrent neural networks (RNN), and convolutional neural networks (CNN). A comprehensive database was assembled, incorporating 50 AC mixtures, of which 25 were from the Kingdom of Saudi Arabia and 25 were from the state of Idaho, USA. This database encompasses an extensive dataset of 3,720 |E*| measurements, associated with thirteen input features representing the proposed AC mixtures' aggregate gradations, binder characteristics, and volumetric properties. This pioneering study surpasses existing research by examining various algorithms to predict |E*| on the same dataset, applying them with different structures and individual optimization to achieve optimal performance. The developed models are evaluated based on multi-stage assessment criteria, including the accuracy and complexity performance measures and rationality based on a sensitivity analysis. The multi-stage comparative analysis results reveal that the bagging ETs, GPR with exponential kernel, and DT record the highest prediction accuracy; however, only the bagging ETs yield the highest accuracy, lowest training and testing complexity, and rational trends throughout the sensitivity analysis. The research outcome has the potential to provide pavement engineers with advanced tools for predicting |E*| and, therefore, optimizing pavement designs and rehabilitations. [ABSTRACT FROM AUTHOR]

Abstract 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.

Lightweight soil is a novel technological material that boasts characteristics such as low density, high strength, thermal insulation, vibration isolation, environmental friendliness, and cost-effectiveness. These features make it highly suitable for a wide range of applications in geotechnical engineering, including roadbed backfill, soft foundation treatment, and tunnel load reduction. The influence of vibration loads resulting from transportation, earthquakes, waves, and other factors on the mechanical properties of lightweight soil has garnered considerable attention in recent research. This paper expounds on the influence of factors on the dynamic deformation characteristics and dynamic strength properties of lightweight soil. These factors include the mix ratio (such as the content of lightweight materials, dosage of curing agent, and moisture content), stress state, vibration frequency, dry–wet alternation effect, and freeze–thaw cycle. Additionally, we summarise the calculation model for the dynamic shear modulus and damping ratio of lightweight soil. The findings reveal that the incorporation of curing agents, such as cement and fly ash, substantially improves the resistance of lightweight soil to dynamic loads. Additionally, the distinctive pore structure of lightweight soil markedly enhances its vibration isolation effect. As dynamic strain increases, there is a nonlinear decrease in the dynamic modulus of lightweight soil while the damping ratio increases nonlinearly. Adjusting the content of lightweight materials and the dosage of curing agents can markedly improve the seismic reduction effect of lightweight soil, thus granting it greater dynamic stability. The coupling effects of dry–wet cycles, freeze–thaw cycles, and dynamic loads may lead to a degradation in the dynamic performance of lightweight soil. To extend its service life in practical engineering applications, the implementation of a waterproof layer is recommended. Model tests and numerical simulations substantiated the commendable dynamic stability and durability of lightweight soil in real-world engineering scenarios. Finally, following a comprehensive literature review, this paper identifies potential research directions. The study of dynamic characteristics in lightweight soil is still in its infancy, with the dynamic properties of novel solid waste lightweight soil remaining largely unexplored. Further exploration is required to fully understand the response mechanisms, mechanical properties, and constitutive models of lightweight soil under the combined effects of complex environmental factors and dynamic loads. Additionally, there is a need for continued research into the design and construction methods of lightweight soil across various engineering settings. In conclusion, this paper serves as a valuable reference for investigating the dynamics of lightweight soil and its extensive application in geotechnical engineering.

Non-destructive testing (NDT) systems are essential tools and are widely used for assessing the condition and structural integrity of pavement structures without causing any damage. They are cost-effective, provide comprehensive data, and are time efficient. The bearing capacity and structural condition of a flexible pavement depends on several interrelated factors, with asphalt layers stiffness being dominant. Since asphalt mix is a viscoelastic material, its performance can be fully captured by the dynamic modulus master curve. However, in terms of evaluating an in-service pavement, although a dynamic load is applied and the time history of deflections is recorded during testing of FWD, only the peak deflection is considered in the analysis. Therefore, the modulus of stiffness estimated by backcalculation is the modulus of elasticity. While several methods have been introduced for the determination of the field dynamic modulus master curve, the MEPDG approach provides significant advantages in terms of transparency and robustness. This study focuses on evaluating the methodology’s accuracy through an experimental study. The data analysis and validation process showed that routine measurements with the FWD and GPR, within the framework of a pavement monitoring system, can provide valuable input parameters for the evaluation of in-service pavements.

Abstract 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.

Abstract The widespread use of low-density polyethylene (LDPE) and high-density polyethylene (HDPE) plastics has resulted in a large amount of waste plastic that requires appropriate disposal or reuse. One potential solution is to use them in the modification of asphalt concrete (AC) mixtures for more sustainable highways. To study this possibility, permanent deformation and dynamic modulus (DM) of the LDPE and HDPE modified AC mixtures was investigated by conducting flow number (FN), flow time (FT) and DM tests on Superpave gyratory compacted specimens. Machine learning models; multi-layer perceptron (MLP), radial basis function neural network (RBFNN), generalized regression neural network (GRNN) and support vector machine (SVM) were used to predict the DM on the basis of frequency and temperature parameters. The model’s performance was gauged by analyzing the root mean square error, mean relative error, and coefficient of determination. The study findings revealed that the LDPE and HDPE modified AC mixtures provide 2.07 times and 1.27 times better resistance to permanent deformation, respectively, than their counterpart. It was also found that the LDPE and HDPE modified AC mixtures have 2.1 times and 1.4 times higher DM values, respectively, than the Control AC mixtures. Among the machine learning models, MLP (R2 = 0.98) showed best accuracy in predicting DM and thus is recommended to be used in similar studies due to its robustness. Additionally, the feature importance analysis revealed that frequency has the highest impact on DM predictions, followed by temperature and the inclusion of the LDPE.

Acrylonitrile Butadiene Rubber (NBR) and Polyvinyl Chloride (PVC) blends are popular in rubber product manufacturing for their flexibility, strength, oil resistance, and durability. Their combination enhances mechanical properties, thermal stability, and chemical resistance, making them ideal for industrial uses like hoses, seals, and insulation. This research work aims to interpret the mechanical properties and interfacial interaction of the NBR/PVC blend with graphene as a reinforcing filler. The morphology and dynamic mechanical properties of 50/50 and 70/30 (w/w) NBR/PVC blend vulcanizates revealed an uniform mixing. In X-ray diffraction studies, an increase in interlayer spacing was evident up to 5 phr graphene. The dynamic viscoelastic properties were used to report the activation energy and α-relaxation of NBR-PVC 70/30, 50/50 neat and graphene filled vulcanizates. The α-relaxation frequency of the filled compositions exhibited a gradual decline as the graphene content increased, while concurrently witnessing a reduction in the breadth of the loss factor peak. The storage modulus increased with increasing the graphene content. The interaction parameter of all compositions was determined by a theoretical expression based on the storage modulus at 10, 50, 100, 500 and 1000 Hz. The 50/50 blend of NBR and PVC was seen to have a greater interaction parameter than the 70/30 blend. However, among the NBR-PVC 70/30 blend compositions, the interaction was highest for 5 phr graphene, and reduced with further incorporation of graphene. Many studies lack information on the interaction of graphene with the rubber matrix. Hence, this research could enhance the understanding of how graphene disperses within the rubber matrix, its influence on mechanical and viscoelastic properties of NBR/PVC rubbers. [ABSTRACT FROM AUTHOR]

This research assesses the dynamic modulus (E*) as a pivotal rheological attribute for characterizing viscoelastic behaviour in various indigenous asphalt mixtures across diverse scenarios. An exhaustive investigation involving 32 laboratory-designed asphalt mixes and two field mixes explores the influence of traffic loading patterns, air voids, binder sources, and climatic conditions on E*. Utilizing Superpave gyratory compaction, 68 E*-specimens are compacted and subjected to E*-testing, leading to master curve development. Evaluation of NCHRP 1-37A and NCHRP 1-40D Witczak models demonstrates their fair to excellent accuracy in predicting E* for Egyptian mixes. The NCHRP 1-40D model stands out with an R² of 0.94, showcasing superior accuracy and minimal bias. This study integrates AASHTOW are Pavement ME Design (PMED) to analyse pavement sections, revealing the impact of design factors, characterized by measured E*, on predicted pavement performance, encompassing asphalt concrete rutting, alligator cracking, longitudinal cracking, and terminal international roughness index. The investigation highlights the marginal impact of substituting predicted E* for measured E* on AASHTOWare pavement performance indicators for Egyptian mixes under default binder characteristic values. This research contributes valuable insights into the interplay of E* and pavement performance, facilitating wellinformed decisions in pavement design and analysis across diverse conditions in the Egyptian context. [ABSTRACT FROM AUTHOR]

Non-destructive testing (NDT) systems are essential tools and are widely used for assessing the condition and structural integrity of pavement structures without causing any damage. They are cost-effective, provide comprehensive data, and are time efficient. The bearing capacity and structural condition of a flexible pavement depends on several interrelated factors, with asphalt layers stiffness being dominant. Since asphalt mix is a viscoelastic material, its performance can be fully captured by the dynamic modulus master curve. However, in terms of evaluating an in-service pavement, although a dynamic load is applied and the time history of deflections is recorded during testing of FWD, only the peak deflection is considered in the analysis. Therefore, the modulus of stiffness estimated by backcalculation is the modulus of elasticity. While several methods have been introduced for the determination of the field dynamic modulus master curve, the MEPDG approach provides significant advantages in terms of transparency and robustness. This study focuses on evaluating the methodology's accuracy through an experimental study. The data analysis and validation process showed that routine measurements with the FWD and GPR, within the framework of a pavement monitoring system, can provide valuable input parameters for the evaluation of in-service pavements. [ABSTRACT FROM AUTHOR]

Sandstone has poor mechanical properties. To facilitate the application of sandstone into asphalt mixtures, sandstone was treated by immersion in sodium silicate solution, and the dynamic modulus after reinforcement was used as a criterion. The results showed that the mechanical properties of the sandstone aggregate treated with sodium silicate were improved, and the dynamic modulus was increased by 18.2%, which will help to reduce rutting. The dynamic modulus and phase angle can be effectively predicted over a wide frequency range using the sigma function and the Kramers–Kronig relationship. Sandstone asphalt mixtures basically conform to linear viscoelasticity, but the phase angle changes are more complicated at high temperatures and do not vary monotonically with frequency. By calculating the rutting coefficient, fatigue coefficient, and DSRFn parameters for performance prediction, it was found that an increase in dynamic modulus resulted in a significant increase in the rutting coefficient but a decrease in the cracking resistance. [ABSTRACT FROM AUTHOR]

The dynamic modulus (∣ E * ∣) is a fundamental mechanical parameter for studying the performance of hot mix asphalt and simulating its viscoelastic behaviour under different loading and thermal conditions. It is a primary tool to replicate road surface behaviour under vehicle traffic loading and temperature variations. Though, laboratory testing to determine this parameter is time-consuming and costly. Several predictive models have been developed to estimate the dynamic modulus, ranging from rheological to empirical regression models. This research was dedicated to studying two predictive models for determining the master curve of the dynamic modulus of hot mix asphalt used in a regular pavement binder course containing different reclaimed asphalt contents (0%, 30%, 40%, and 50%). Laboratory experiments were conducted to assess their accuracy. The results show that Witczak's sigmoid function provided the best accuracy for the master curves, while the Generalized Huet-Sayegh (2S2P1D) model showed less accurate predictions, particularly at the range of low and high frequencies. [ABSTRACT FROM AUTHOR]

This study investigates the effect of using steel slag in different layers of a flexible pavement structure on the permanent deformation performance and life cycle cost (LCC). Materials were obtained from a recent expressway pavement construction project. Physical and chemical properties of steel slag produced in Oman indicated that it can be used in the pavement layers. Results indicated that 100% of slag can be used in the base or subbase layers resulting in a CBR value of 212%. For wearing course (WC) and bituminous base course (BBC) layers, a 30% and 25% replacement of coarse aggregate was recommended based on Marshall mix design method. A finite element program was used to model the pavement sections. Rutting performance was determined based on the dynamic modulus |E*| results and mechanistic empirical pavement design guide (MEPDG) rut models. Rutting was not significantly different from the control section when steel slag was used in the base and/or subbase pavement structure. Rutting increased from 12.5 to 17.9 mm at the end of design life when slag was used in the WC layer. Further increase in rutting resulted when the steel slag was introduced in the BBC layer. LCC analysis based on 75 mm milling and overlay and zero slag cost indicated a reduction of 29.4% in cost when slag is used in both base and subbase layers. Steel slag in WC was recommended for lower volume roads with a higher target rut depth (16.5 mm), in which case LCC increased by 12% compared to the control section. [ABSTRACT FROM AUTHOR]

For asphalt mixtures, it is widely known that Poisson's Ratio (PR) varies according to several parameters, and it is a temperature and loading frequency-dependent property. However, measuring PR at different temperatures and frequencies during mechanical tests is not commonly done. For that, a complex arrangement of fixtures and instruments would be necessary, and still likely not compatible with typical cylindrical specimens. This work aimed to combine the simple and practical Ultrasonic Pulse Velocity (UPV) test with the conventional Dynamic Modulus ( | E ∗ | ) test used for determining pavement design inputs related to asphalt mixtures. To do this, four typical lower binder asphalt concrete mixes were selected, having different levels of Asphalt Binder Replacement (ABR), given the presence of RAP/RAS, and covering seven different asphalt binders in terms of Performance Grade (PG). Additionally, a baseline mixture with no recycled material was used for comparison. Results show that estimating | E ∗ | via UPV assuming a typical PR value may be inaccurate. Once reference experimental modulus values were available, PR was backcalculated using the UPV theory, showing that this property increases as temperature increases, while it decreases as ABR and air voids increases. This indicates that PR can be a source of significant variability when it comes to the use of recycled materials. Therefore, UPV emerges as a low-cost, practical, and reasonably accurate piece of equipment (compared to state-of-practice assumptions) with potential to be integrated with traditional | E ∗ | testing for an assessment of PR sensitivity according to different mixes' properties. [ABSTRACT FROM AUTHOR]

To investigate the effect of hot and humid environmental factors on the mechanical properties of asphalt mixtures research, in this paper, the dynamic modulus of asphalt mixtures under the effects of aging, dry–wet cycling, and coupled effects of aging and dry–wet cycling were measured by the simple performance tester (SPT) system, and the dynamic modulus principal curves were fitted based on the sigmoidal function. The results show that under the aging effect, the dynamic modulus of asphalt mixture increases with the aging degree; the dynamic modulus of short-term aged, medium-term aged, long-term aged, and ultra-long-term aged asphalt mixtures increased by 9.3%, 26.4%, 44.8%, and 57%, respectively, compared to unaged asphalt mixtures at 20 °C and 10 Hz; the high-temperature stability performance is enhanced, and the low temperature cracking resistance performance is enhanced; under the dry–wet cycle, the aging effect of asphalt water is more obvious in the early stage, and dynamic modulus of resilience of the mixture is slightly increased. In the long-term wet–dry cycle process, water on the asphalt and aggregate erosion increased, the structural bearing capacity attenuation, and the dynamic modulus of rebound greatly reduced at 20 °C and 10 Hz. For example, the dynamic modulus of asphalt mixtures with seven wet and dry cycles increased by 3% compared to asphalt mixtures without wet and dry cycles, and the dynamic modulus of asphalt mixtures with 14 cycles of wet and dry cycles and 21 cycles of wet and dry cycles decreased by 10.8% and 16.5%, respectively, compared to asphalt mixtures without wet and dry cycles. The main curve as a whole shifted downward; the high-temperature performance decreased significantly; in the aging wet–dry cycle coupling, the aging asphalt mixture is more susceptible to water erosion, and the first wet–dry cycle after the mix by the degree of water erosion is relatively small, along with the dynamic modulus of rebound. The dynamic modulus of resilience is relatively larger, and the high-temperature performance is relatively better, while the low-temperature performance is worse. [ABSTRACT FROM AUTHOR]

The widespread use of low-density polyethylene (LDPE) and high-density polyethylene (HDPE) plastics has resulted in a large amount of waste plastic that requires appropriate disposal or reuse. One potential solution is to use them in the modification of asphalt concrete (AC) mixtures for more sustainable highways. To study this possibility, permanent deformation and dynamic modulus (DM) of the LDPE and HDPE modified AC mixtures was investigated by conducting flow number (FN), flow time (FT) and DM tests on Superpave gyratory compacted specimens. Machine learning models; multi-layer perceptron (MLP), radial basis function neural network (RBFNN), generalized regression neural network (GRNN) and support vector machine (SVM) were used to predict the DM on the basis of frequency and temperature parameters. The model's performance was gauged by analyzing the root mean square error, mean relative error, and coefficient of determination. The study findings revealed that the LDPE and HDPE modified AC mixtures provide 2.07 times and 1.27 times better resistance to permanent deformation, respectively, than their counterpart. It was also found that the LDPE and HDPE modified AC mixtures have 2.1 times and 1.4 times higher DM values, respectively, than the Control AC mixtures. Among the machine learning models, MLP (R2 = 0.98) showed best accuracy in predicting DM and thus is recommended to be used in similar studies due to its robustness. Additionally, the feature importance analysis revealed that frequency has the highest impact on DM predictions, followed by temperature and the inclusion of the LDPE. [ABSTRACT FROM AUTHOR]

Given the dominant failure mode of steel bridge deck pavement layers, which is flexural–tensile damage, the dynamic modulus parameters conventionally determined through uniaxial compression testing are found to be inadequate for the design or performance analysis of these layers. In order to simulate the actual stress of a pavement structure under wheel load, the four-point bending fatigue test method and uniaxial compression test method are used to measure the dynamic modulus of an epoxy asphalt mixture, and the differences between the two test methods are analyzed. Furthermore, the four-point bending fatigue test is employed to investigate the dynamic modulus and phase angle properties across varying temperatures and frequencies, facilitating the creation of master curves for these properties and utilizing Sigmoidal models to correlate dynamic modulus data at diverse temperature conditions. This study delves into the influence of epoxy resin content, mixture composition, and aging on the dynamic modulus. The experimental results show that the dynamic modulus measured by uniaxial compression exceeds that obtained from bending fatigue tests, with the difference initially increasing and then decreasing as temperature rises. This discrepancy significantly impacts the mechanical calculations of pavement layers, underscoring the importance of selecting the appropriate testing method. Temperature, frequency, and epoxy resin content have pronounced effects on the viscoelastic properties of the mixtures. Specifically, as temperature increases, the dynamic modulus undergoes a decrease, whereas the phase angle exhibits an increase. Additionally, the dynamic modulus augments with an increase in loading frequency, while the phase angle exhibits varied trends with frequency shifts across different temperatures. Both the WLF and Sigmoidal models are effective in constructing master curve representations for the dynamic flexural modulus and phase angle. The incorporation of epoxy resin transforms asphalt from a primarily viscous to a more elastic material, significantly enhancing the viscoelastic properties of the mixture. Notably, mixtures with 50% and 60% epoxy resin content exhibit comparable dynamic moduli and phase angles, while displaying notably superior performance compared to those with 40% epoxy resin content. For large-scale steel bridge deck pavement, 50% epoxy resin content is recommended. Moreover, epoxy asphalt mixtures demonstrate robust aging resistance, with minimal variations in the dynamic modulus and phase angle before and after aging. The research results can enable the acquisition of dynamic modulus and phase angle data in the whole temperature domain and the whole frequency domain, and provide reliable mixed performance parameters for the study of different application environmental performance of steel bridge deck pavement. [ABSTRACT FROM AUTHOR]

In recent years, smart aggregates have emerged as a promising tool for monitoring the movement of and changes in particles inside asphalt mixtures. However, there remain significant differences between smart aggregates and real rock aggregates, particularly the lack of an asphalt coating on the surface of smart aggregates. Currently, the research on the impact of smart aggregates themselves on the structure and properties of asphalt mixtures is lacking. Therefore, this study focuses on the influence of smart aggregate size on the mesostructure and mechanical properties of asphalt mixtures. Firstly, based on laboratory tests and the discrete element method (DEM), discrete element models of asphalt mixture specimens containing smart aggregates of various sizes were constructed, followed by simulated compaction tests. The effects of smart aggregate size on the mesostructure of asphalt mixture voids were then analyzed. Lastly, in this study, the changes in the dynamic modulus of asphalt mixtures were explored with increasing smart aggregate size and the underlying mechanisms. The results indicate that as the size of smart aggregates increases, the average void ratio of the asphalt mixture specimens decreases, but the heterogeneity of the void distribution increases. Additionally, with the increase in smart aggregate size, the dynamic modulus of the mixture specimens decreases. Further strain analysis of the specimens suggests that the increase in cross-sectional deformation is the primary cause of the reduction in modulus. [ABSTRACT FROM AUTHOR]

Sandstone has poor mechanical properties. To facilitate the application of sandstone into asphalt mixtures, sandstone was treated by immersion in sodium silicate solution, and the dynamic modulus after reinforcement was used as a criterion. The results showed that the mechanical properties of the sandstone aggregate treated with sodium silicate were improved, and the dynamic modulus was increased by 18.2%, which will help to reduce rutting. The dynamic modulus and phase angle can be effectively predicted over a wide frequency range using the sigma function and the Kramers–Kronig relationship. Sandstone asphalt mixtures basically conform to linear viscoelasticity, but the phase angle changes are more complicated at high temperatures and do not vary monotonically with frequency. By calculating the rutting coefficient, fatigue coefficient, and DSRFn parameters for performance prediction, it was found that an increase in dynamic modulus resulted in a significant increase in the rutting coefficient but a decrease in the cracking resistance.

This study evaluates the laboratory performance of Warm-Mix Asphalt (WMA) containing locally sourced Steel Slag (SS) and limestone (LS), focusing on stiffness and moisture sensitivity. Four asphalt mixes were tested: Hot mix with limestone (HL), hot mix with steel slag (HS), warm mix with limestone (WL), and warm mix with steel slag (WS). The binder used for all mixes was AC 60/70 penetration grade, and Aspha-min served as the warm mix additive.Testing included Marshall Stability, boiling water, indirect tensile strength, moisture-induced damage (TSR), and dynamic modulus. Image Analysis quantified the aggregate-retained coated area after the boiling test. Despite performance differences, all mixes met Egyptian code requirements for asphalt concrete mixes.Visual inspection and image analysis demonstrated the effectiveness of using steel slag to mitigate asphalt mix stripping. The retained coated area for limestone mixes ranged from 60 % to 75 %, while SS mixes exhibited over 95 %. Dynamic modulus tests indicated no significant stiffness difference with SS, and stability increased by 8 % and 14 % with and without the warm additive. Economic study confirmed the feasibility of utilizing SS. In conclusion, combining Aspha-Min and steel slag improves AC mix production and construction processes, offering a sustainable and advantageous option in Egypt.

Three kinds of multiblock polyethylene oxide-polypropylene oxide (PEOm-PPOn for short) were synthesized using ethylene glycol as the initiated core via the living anionic polymerization. Respectively, the multiblock polyethers were named E340, E540, and E740 based on the block number (3, 5, 7) and the content of ethylene oxide (EO, 40 wt%), which were confirmed by the Fourier transform infrared (FT-IR), hydrogen nuclear magnetic resonance (1H NMR), and gel permeation chromatography (GPC). Moreover, their bulk and solution properties, including dynamic modulus, rheological characteristic, viscosity, aggregation behavior, surface tension, steady-state fluorescence, solubility, and microemulsion performance were determined. The test results of rheological properties showed that these multiblock copolyethers behaved as the pseudo-plastic non-Newtonian fluids. Furthermore, it was found that their solubility and surface tension were gradually decreased with an increase of block numbers. Nevertheless, the dynamic modulus tended contrary trends. Research on the aggregation and micro-emulsion properties in aqueous solutions indicated that the value of critical micelle concentration (CMC) increased, and meanwhile the ability of solubilization and micro-emulsion formation deteriorated with increasing block numbers. In conclusion, their aggregation mechanism in aqueous solution was also given. [ABSTRACT FROM AUTHOR]

This study evaluates the laboratory performance of Warm-Mix Asphalt (WMA) containing locally sourced Steel Slag (SS) and limestone (LS), focusing on stiffness and moisture sensitivity. Four asphalt mixes were tested: Hot mix with limestone (HL), hot mix with steel slag (HS), warm mix with limestone (WL), and warm mix with steel slag (WS). The binder used for all mixes was AC 60/70 penetration grade, and Aspha-min served as the warm mix additive. Testing included Marshall Stability, boiling water, indirect tensile strength, moisture-induced damage (TSR), and dynamic modulus. Image Analysis quantified the aggregate-retained coated area after the boiling test. Despite performance differences, all mixes met Egyptian code requirements for asphalt concrete mixes. Visual inspection and image analysis demonstrated the effectiveness of using steel slag to mitigate asphalt mix stripping. The retained coated area for limestone mixes ranged from 60 % to 75 %, while SS mixes exhibited over 95 %. Dynamic modulus tests indicated no significant stiffness difference with SS, and stability increased by 8 % and 14 % with and without the warm additive. Economic study confirmed the feasibility of utilizing SS. In conclusion, combining Aspha-Min and steel slag improves AC mix production and construction processes, offering a sustainable and advantageous option in Egypt. [ABSTRACT FROM AUTHOR]

The dynamic modulus of asphalt mixture is an important factor in the design of asphalt pavement, and many scholars have proposed different models for estimating the dynamic modulus of asphalt mixture, but there are almost no studies on the prediction of the dynamic modulus of semi-flexible materials. In order to analyze and estimate the dynamic modulus of semi-flexible materials, we set up a high-performance cementitious paste (HPCP) semi-flexible material and a reference group Stone Mastic Asphalt (SMA-16) under multiple conditions, first measured its dynamic modulus in the laboratory, and analyzed the dynamic modulus characteristics of the material, and then used the equation the estimation equation proposed by Witczak et al. (Witczak1-37A) as a benchmark to introduce a new parameter, grouting mass ratio (Pb) to develop a Witczak- G prediction model to compare and validate the predicted dynamic modulus with the measured values. The results show that compared with SMA-16, HPCP semi-flexible material exhibits higher dynamic modulus and lower phase angle, and its temperature sensitivity and deformation resistance are significantly better than those of SMA-16. Under the influence of porosity and Pb factor, the dynamic modulus is positively correlated with both factors, and the phase angle increases first and then decreases, showing strong elastic properties. In this paper, we propose a dynamic modulus prediction model based on viscosity and Pb, Witczak-G, which predicts the highest coefficient of determination (R2) of the predicted dynamic modulus as high as 0.99 after initial fitting and validation, which indicates that the Witczak-G model is suitable for predicting the dynamic modulus of semi-flexible materials injected with HPCP. [ABSTRACT FROM AUTHOR]

The dynamic modulus of in-service asphalt pavements serves as a critical parameter for the computation of residual life and the design of overlays. However, its acquisition is currently limited to laboratory dynamic modulus testing using a limited number of core samples, necessitating a reassessment of its representativeness. To facilitate the prediction of dynamic modulus design parameters through Falling Weight Deflectometer (FWD) back-calculated modulus data, an integrated approach encompassing FWD testing, modulus back-calculation, core sample dynamic modulus testing, and asphalt DSR testing was employed to concurrently acquire dynamic modulus at identical locations under varying temperatures and frequencies. Dynamic modulus prediction models for in-service asphalt pavements were developed utilizing fundamental model deduction and gene expression programming (GEP) techniques. The findings indicate that GEP exhibits superior efficacy in the development of dynamic modulus prediction models. The dynamic modulus prediction model developed can enhance both the precision and representativeness of asphalt pavement's dynamic modulus design parameters, as well as refine the accuracy of residual life estimations for in-service asphalt pavements. Concurrently, the modulus derived from FWD back-calculation can be transmuted into the dynamic modulus adhering to a uniform standard criterion, facilitating the identification of problematic segments within the asphalt structural layer. This is of paramount importance for the maintenance or reconstruction of in-service asphalt pavements. [ABSTRACT FROM AUTHOR]

In order to solve the dusting and rutting problems of hot mix asphalt mixture during construction and operation, rutting test, bending test, Marshall stability test, freeze-thaw splitting test, uniaxial compression dynamic modulus test, overlay test, and four-point bending fatigue life test are applied on WHMM-13 to study the high-temperature stability, low-temperature cracking resistance, water stability, anti-reflection crack performance and fatigue durability in comparison with HMM-13. The results show that, compared to HMM-13, the dynamic stability and dynamic modulus (45 ℃, 10 Hz) of WHMM-13 are improved by 10.0% and 47% respectively, and the rutting depth is reduced by 27.3%, indicating that the high temperature stability of WHMM-13 has been greatly improved. As for low temperature cracking resistance, the bending failure strain, stiffness modulus, flexural tensile strength and rupture energy of WHMM-13 are slightly lower than those of HMM-13. As for water stability, the residual stability of WHMM-13 in immersion Marshall test is 87.5%, and the splitting strength is 82.3%, both higher than that of HMM-13. As for anti-reflection crack performance, the total tensile rupture energy of WHMM-13 is 2.32 times that of HMM-13, with cracking resistance index (CRI) increased by 25%, which shows that WHMM-13 has better strength, can effectively prevent the crack from spreading and effectively improve the cracking resistance capacity of asphalt pavement. As for fatigue durability, the fatigue life of WHMM-13 is 5.4% lower than that of HMM-13, with bending stiffness modulus and cumulative dissipation energy reduced by 11.3% and 2.8% respectively, indicating that the fatigue durability of WHMM-13 is slightly reduced. [ABSTRACT FROM AUTHOR]