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The present paper investigates the viscoelastic stress-strain responses of laboratory and plant produced warm mix asphalt mixtures containing basalt fiber dispersed reinforcement. The investigated processes and mixture components were evaluated for their efficacy in producing highly performing asphalt mixtures with decreased mixing and compaction temperatures. Surface course asphalt concrete (AC-S 11 mm) and high modulus asphalt concrete (HMAC 22 mm) conventionally and using a warm mix asphalt technique with foamed bitumen and a bio-derived fluxing additive. The warm mixtures included lowered production temperature (by 10 °C) and lowered compaction temperatures (by 15 °C and 30 °C). The complex stiffness moduli of the mixtures were assessed under cyclic loading tests at combinations of four temperatures and five loading frequencies. It was found that the warm produced mixtures were characterized by lower dynamic moduli than the reference mixtures in the whole spectrum of loading conditions, however, the mixtures compacted at the 30 °C lower temperature performed better than the mixtures compacted at 15 °C lower temperature, specifically when highest testing temperatures are considered. The differences in the performance of plant and laboratory produced mixtures were ascertained to be nonsignificant. It was concluded that the differences in stiffness of hot mix and warm mixtures can be attributed to the inherent properties of foamed bitumen mixtures and that these differences should shrink in time.

This experimental study examines the impact of installation damage and chemical degradation on the mechanical and thermal properties of a polypropylene geogrid. Two protocols were employed: a chemical degradation test involving immersion in a sulfuric acid solution at 80 ± 2°C, and an installation damage test conducted on site. The damage incurred by the geogrid following the degradation tests was evaluated by monitoring the evolution of its mechanical behavior, specifically the secant stiffness modulus at various strains, using a tensile test. Additionally, a differential scanning calorimetry analysis was performed to assess changes in the melting rate of the polypropylene. The results demonstrate that prolonged exposure to an acidic environment leads to a marked reduction in the material’s stiffness, especially at higher strain levels, which reflects a significant degradation of its long-term mechanical performance. Although the effects of installation damage are comparatively less pronounced, they still contribute to a decrease in stiffness modulus. This suggests that the material exhibits greater sensitivity to chemical degradation than to mechanical damage. From a thermal standpoint, an increase in the melting rate during the first three months of immersion, reaching up to 35.61%, followed by a significant decrease after extended exposure, dropping to 27.32%.

Coconut products such as oil, milk powder, activated carbon, and desiccated coconut are increasingly in demand, leading to higher coconut production and a surplus of coconut fibers. Despite their excellent physical and mechanical properties, these fibers are often discarded or burned due to limited research into alternative uses, contributing to environmental pollution. This study evaluates the potential of coconut fibers in hot mix asphalt (HMA) to reduce waste and enhance their mechanical performance. Central composite design (CCD) was adopted to optimize fiber-modified HMA mixes using response surface methodology (RSM) based on Marshall testing. Sixty Marshall samples with varying fiber content, bitumen content, and fiber length were prepared to develop the RSM model based on 20 runs. Fiber content (%), fiber length (mm), and bitumen content (%) were considered as factors, while marshall stability (KN) and flow (mm) were taken as responses. The optimized mix, containing 0.28% coconut fibers (approximately 13 mm in length) and 4. 16% bitumen, achieved a marshall stability of 18. 02 kN and a flow of 3.12 mm. Validation of the optimized solution with the experimental trials showed an error of 7.05% for marshall stability and 6. 11% for marshall flow. Indirect tensile strength testing showed a 5% higher tensile strength for the optimized dry mix compared to the 1.29 KN observed for control samples. Furthermore, the tensile strength ratio between dry and wet samples was recorded to be higher than the threshold of 80% for both control and optimized HMA mixes. Moreover, the indirect tensile stiffness modulus (ITSM) for control samples recorded at 5 °C was higher than the optimized mix. However, the optimized HMA mixes resulted in around 13%, 6%, and 2.16% higher ITSM at 15 °C, 20 °C, and 25 °C, respectively, in reference to the control mix. Furthermore, the indirect tensile fatigue testing revealed that the control mix performed better than the optimized mix. Nonetheless, the optimized mix showed steady behavior against stress variation as compared to the control mix. Overall, this study demonstrates the effective use of RSM to optimize the Marshall mix design, reducing laboratory testing. Additionally, it was observed that optimized fiber-modified HMA mixes exhibit superior mechanical properties compared to control samples, paving the roads for sustainable and efficient asphalt technologies.

The main purpose of this paper is to present the development of a new predictive model intended for the calculation of stiffness modulus |E*| determined by a four-point bending beam test (4PBB or 4PB-PR). The model developed, called model A, was based on the Witczak model, which was developed for the dynamic-modulus (DM) method. Most of the asphalt mixtures used to develop the model were high-modulus asphalt concrete (HMAC). The most commonly used methods for determining the stiffness modulus |E*| of asphalt mixtures were also discussed. The paper presents the results of the study for 10 asphalt mixtures but 8 of them were used to develop the predictive model. In addition, the results of complex shear modulus G* tests on neat and modified bituminous binders carried out in a dynamic shear rheometer (DSR), necessary for the development of a predictive model, are presented. The tests carried out in the dynamic shear rheometer had significant measurement uncertainties. The results of the volumetric parameters of the asphalt mixtures are also reported. The developed model A has maximum absolute errors e = 1930 MPa (p = 95%) and maximum relative errors re = 50% (p = 95%). The distribution of the absolute errors of the model, after discarding outliers, has a normal distribution as in the development of other models of this type, which was confirmed by appropriate statistical tests. On the basis of the tests and calculations carried out, it was concluded that, in order to increase the precision of the predictive models, it is advisable to reduce the measurement uncertainty of the bitumen complex shear modulus G*. For the developed model A, the limiting values of the stiffness modulus |E*| are also shown, within which the determined stiffness modulus should fall.

Soft soils pose significant challenges to the environment and construction of infrastructure on them owing to their distinct characteristics such as low bearing strength, high water content, low permeability, and high void ratio. The stiffness modulus of soft ground soils (Gs) is one of the major considerations while designing geo-structures. The determination of the stiffness modulus of soft ground materials such as soils requires expensive machinery, more skilled labor, and consumption of time which is contrary to the current trends of sustainable development. Therefore, this paper presents the artificial intelligence (AI)-based sustainable solutions for the estimation of Gs using artificial neural network (ANN), gene expression programming (GEP), and multiple linear regression (MLR) techniques. In this regard, 199 samples of soft soil from different locations were retrieved and tested to determine basic soil attributes such as sand content (S), fine content (FC), liquid limit (LL), plastic limit (PL), water content (w), and bulk density (d) which were used as potential indicators for computing soft ground stiffness modulus. Many statistical tests, including R-square (R2), root means square error (RMSE), and mean absolute error (MAE), were used to further substantiate the performance efficiency of computed prediction models. The findings show that the proposed models meet all accuracy-related acceptance requirements. However, ANN outperforms GEP and MLR. Further, to evaluate the specific impact of input factors, sensitivity and parametric tests were also executed.

A previous study conducted by the authors proved that the inferiority of the mechanical properties of Cold Bituminous Emulsion Mixtures (CBEMs) could be overcome by incorporating a waste or by-product material, namely Paper Sludge Ash (PSA). The new CBEMs have demonstrated comparative mechanical and durability properties compared to conventional Hot Mix Asphalt (HMA). Furthermore, the new CBEMs have less impact on the economy, environment, and safety. However, the air void content of the new CBEMs is still high – to a stage unacceptable by pavement engineers. Thus, this study introduces a treatment method to reduce the air void without affecting the improvement achieved in mechanical properties and other environmental and economic issues. This study presents microwave energy treatment as a unique post-mix treatment method to overcome high air void content in CBEMs. Test results showed that microwave energy improves the stiffness modulus and air void content. However, new post-mix microwave treatment CBEMs still have comparative mechanical, volumetric, economic, and environmental characteristics to HMA.

Modifiers have been applied to enhance the performance of asphalt binder and asphalt mixes in recent years. In this study, metakaolin and dolomite powder have been used as fillers for the potential to enhance the characteristics of hot mix asphalt. An experimental program on asphalt mixes was undertaken to evaluate the impact of both metakaolin and dolomite powder materials on pavement performance. Both materials were used instead of part of the traditional filler (cement) with different percentages (0%, 50%, 100%) by weight of mixes. Several fundamental assessments were performed on the asphalt mixture, including Marshall Stability (MS), Marshall Flow (MF), Unit weight (γ), Marshall Quotient (MQ), and Marshall Stiffness Modulus (Ms). The outcomes showed that the properties of the modified asphalt mixture improved by using binary blended filler (50% metakaolin and 50% dolomite) in comparison to the traditional hot mix asphalt. Furthermore, the replacement of cement with 100% metakaolin increases Marshall stability by around 27% compared to the traditional hot mix asphalt.

It is commonly known that mineral fillers significantly affect the asphalt mixture's performance. Superior flexible pavement performance can be ensured by gaining a deeper understanding of the function of filler. This research investigates the influence of three different fillers: granite dust, cement, and hydrated lime, at Australian asphalt mixtures. The testing program includes Marshall testing, moisture damage resistance, indirect tensile strength (ITS), and indirect tensile stiffness modulus (ITSM) tests of asphalt mixtures. Analysis of variance (ANOVA) was used to statistically assess the results obtained, besides damage analysis. The results indicate that using natural granite dust yields the highest resistance to moisture, while cement produces the highest stability, ITS, and ITSM. Unexpectedly, using hydrated lime filler decreases the stability/stiffness and moisture resistance of asphalt mixtures. ANOVA tests indicate that the type of filler affects ITS, TSR, and ITSM results (i.e., the p-value <0.05). The damage analysis shows that the design life of the asphalt mixture made with cement filler is higher than that of mixtures made with natural granite dust and hydrated lime fillers respectively. The findings indicate the important role of nontraditional fillers at the performance of Australian asphalt mixtures.

The study of the behavior of bituminous mixtures is a very old subject in the specialized literature, many specialists in the field trying to explain the causes of the degradations, considering different factors. The bituminous mixtures were initially used in the construction of road structures for roads, so that a few years ago they also started to be used in the road structures of airport surfaces. The subject of the current research study is a priority worldwide and contributes, through the obtained results, to the enrichment of knowledge regarding the behavior of airport bituminous mixtures. The complexity of this study was based on the multitude of laboratory tests carried out on the designed airport bituminous mixture, BBA 16, using complex modern and high-performance equipment and equipment that lead to the determination of the stiffness modulus at different temperatures and frequencies both on trapezoidal samples and on prismatic samples.

In recent years, nanotechnology has sparked an interest in nanomodification of bituminous materials to increase the viscosity of asphalt binders and improves the rutting and fatigue resistance of asphalt mixtures. This paper presents the experimental results of laboratory tests on bituminous mixtures laid on a 1052 m-long test section built in Rome, Italy. Four asphalt mixtures for wearing and binder layer were considered: two polymer modified asphalt concretes (the former modified with the additive Superplast and the latter modified with styrene–butadiene–styrene), a “hard” graphene nanoplatelets (GNPs) modified asphalt concrete and a not-modified mixture. The indirect tensile strength, water sensitivity, stiffness modulus, and fatigue resistance of the mixtures were tested and compared. A statistical analysis based on the results has shown that the mixtures with GNPs have higher mechanical performances than the others: GNP could significantly improve the tested mechanical performances; further studies will be carried out to investigate its effect on rutting and skid resistance.