ABSTRACT
Accumulating vast amounts of pollutants drives modern civilization toward sustainable development. Construction waste is one of the prominent issues impeding progress toward net-zero. Pollutants must be utilized in constructing civil engineering structures for a green ecosystem. On the other hand, large-scale production of industrial steel fibers (ISFs) causes significant damage to the goal of a sustainable environment. Recycled steel fibers (RSFs) from waste tires have been suggested to replace ISFs. This research critically examines RSF's application in the mechanical properties' improvement of concrete and mortar. A statistical analysis of dimensional parameters of RSFs, their properties, and their use in manufacturing various cement-based composites are given. Furthermore, comparative assessments are carried out among the improvements in compressive, split tensile, and flexural strengths of plain and RSF-incorporated concrete and mortar. In addition, the optimum contents of RSF for each strength property are also discussed. The influence of RSFs parameters on various strength properties of concrete and mortars is discussed. The possible applications of RSF for various civil engineering structures are reviewed. The limitations and errors noticed in previous review papers are also outlined. It is found that the maximum enhancement in compressive strength (CS), split tensile strength (STS), and flexure strength (FS) are 78%, 149%, and 157%, respectively, with the addition of RSF into concrete. RSF increased cement mortars' CS, STS, and FS by 46%, 50.6%, and 69%, respectively. The current study encourages the building sector to use RSFs for sustainable concrete.
ABSTRACT
Deterioration of asphalt pavements due to massive load of vehicles and climatic variation has demanded the use of pavements construction material with an excellent resilience characteristic, resistance to permanent deformation, and most importantly, a much longer service lifespan. The main structural distresses in pavement construction are permanent deformation at high temperatures and fatigue cracking under repetitive traffic loadings. To comprehensively investigate the performance of bitumen penetration grade (PG) 70 against rutting, fatigue, and high temperature cracking in hot mix asphalt (HMA) pavements, polycarbonate (PC) and polytetrafluoroethylene (PTFE) were used. The investigation of the internal structure, rheological, and physical properties of base and modified bitumen (MB) mixes with different percentages of modifiers (0%, 2.5%, and 5%) by weight were performed via scanning electron microscope (SEM), Fourier transform infrared spectroscopy (FTIR) analysis, X-ray diffraction (XRD) pattern analysis, rolling thin-film oven test (RTFOT), pressurized aging vessel (PAV), dynamic shear rheometer (DSR), rotational viscosity (RV), and bending beam rheometer (BBR). The results of the RV test indicate that modification of neat bitumen with polycarbonate and polytetrafluoroethylene increased the viscosity for polycarbonate-modified bitumen (PCMB), polytetrafluoroethylene-modified bitumen (PTFEMB), and for a blend of PCMB-PTFEMB by 44%, 50%, and 55.75% at 135 °C and 111.10%, 127.80%, and 138.88% at 165 °C, accordingly. BBR test results revealed that modifiers increased the rigidity of neat bitumen by 74.8%, 75.8%, and 74.5% at -16 °C, -22 °C, and -28 °C, respectively.
ABSTRACT
The consumption of waste materials in the construction sector is a sustainable approach that helps in reducing the environmental pollution and decreases the construction cost. The present research work emphasizes the mechanical properties of bituminous concrete mix prepared with crumb rubber (CR) and waste sugarcane bagasse ash (SCBA). For the preparation of bituminous concrete mix specimens with CR and SCBA, the effective bitumen content was determined using the Marshall Mix design method. A total of 15 bituminous concrete mix specimens with 4%, 4.5%, 5%, 5.5% and 6% of bitumen content were prepared, and the effective bitumen content turned out to be 4.7%. The effect of five different CR samples of 2%, 4%, 6%, 8% and 10% by weight of total mix and SCBA samples of 25%, 50%, 75% and 100% by weight of filler were investigated on the performance of bituminous concrete. A total of 180 samples with different percentages of CR and SCBA were tested for indirect tensile strength (ITS) and Marshall Stability, and the results were compared with conventional bituminous concrete mix. It was observed that the stability values rose with an increase in CR percentage up to 6%, while the flow values rose as the percentage of SCBA increased in the mix. Maximum ITS results were observed at 4% CR and 25% SCBA replacement levels. However, a decrease in stability and ITS result was observed as the percentages of CR and SCBA increased beyond 4% and 25%, respectively. We concluded that the optimum CR and SCBA content of 4% and 25%, respectively, can be effectively used as a sustainable alternative in bituminous concrete mix.
ABSTRACT
Abiotic stress, particularly drought, will remain an alarming challenge for sustainable agriculture. New approaches have been opted, such as nanoparticles (NPs), to reduce the negative impact of drought stress and lessen the use of synthetic fertilizers and pesticides that are an inevitable problem these days. The application of zinc oxide nanoparticles (ZnO NPs) has been recognized as an effective strategy to enhance plant growth and crop production during abiotic stress. The aim of the current study was to investigate the role of ZnO NPs in drought stress management of drought-susceptible Coriandrum sativum L. (C. sativum) in two consecutive seasons. Drought regimes (moderate drought regime-MDR and intensive drought regime-IDR) were developed based on replenishment method with respect to 50% field capacity of fully irrigated (control) plants. The results showed that foliar application of 100 ppm ZnO NPs improved the net photosynthesis (Pn), stomatal conductance (C), and transpiration rate (E) and boosted up the photosynthetic capacity associated with photosynthetic active radiation in MDR. Similarly, 48% to 30% improvement of chlorophyll b content was observed in MDR and onefold to 41% in IDR during both seasons in ZnO NP-supplemented plants. The amount of abscisic acid in leaves showed a decreasing trend in MDR and IDR in the first season (40% and 30%) and the second season (49% and 33%) compared with untreated ZnO NP plants. The ZnO NP-treated plants showed an increment in total soluble sugars, total phenolic content, and total flavonoid content in both drought regimes, whereas the abaxial surface showed high stomatal density and stomatal index than the adaxial surface in foliar-supplied NP plants. Furthermore, ZnO NPs improve the magnitude of stomata ultrastructures like stomatal length, stomatal width, and pore length for better adaptation against drought. Principal component analysis revealed the efficacy of ZnO NPs in inducing drought tolerance in moderate and intensive stress regimes. These results suggest that 100 ppm ZnO NPs can be used to ameliorate drought tolerance in C. sativum plants.
ABSTRACT
BACKGROUND: Obstructive sleep apnea (OSA) is a chronic sleeping disorder. The analysis of the pharynx and its surrounding tissues can play a vital role in understanding the pathogenesis of OSA. Classification of the pharynx is a crucial step in the analysis of OSA. METHODS: A visual analysis-based classifier is developed to classify the pharynx from MRI datasets. The classification pipeline consists of different stages, including pre-processing to select the initial candidates, extraction of categorical and numerical features to form a multidimensional features space, and a supervised classifier trained by using visual analytics and silhouette coefficient to classify the pharynx. RESULTS: The pharynx is classified automatically and gives an approximately 86% Jaccard coefficient by evaluating the classifier on different MRI datasets. The expert's knowledge can be utilized to select the optimal features and their corresponding weights during the training phase of the classifier. CONCLUSION: The proposed classifier is accurate and more efficient in terms of computational cost. It provides additional insight to better understand the influence of different features individually and collectively. It finds its applications in epidemiological studies where large datasets need to be analyzed.
