Search

During the development of symmetric asphalt concrete material samples, aggregates play a vital role in the performance of its production. Shape characteristics and aggregates geology are two major factors influencing strength parameters of the asphalt concrete mix. In this study, two different geological sources of aggregates with different shape characterizations have been utilized for the development of the asphalt concrete mix. In addition to that, the stability analysis has been performed under different temperature conditions ranging between 25 and 60 °C. By the application of the destructive technique (DT) and non-destructive technique (NDT), the performance of the asphalt concrete mix has been analyzed and compared based on the geology of aggregates under various temperatures. Furthermore, a statistical model has been developed to analyze the stability and performance of the developed asphalt concrete mix with reference to regional climatic conditions. This study will help in the development of symmetric formation of sustainable asphalt materials.

The present investigation aims to propose a numerical model for assessing the complex damaging response of glass fiber-reinforced polymer- (GFRP-) reinforced concrete columns having hybrid fibers and confined with GFRP spirals (GFHF columns) under concentric and eccentric compression. Fiber-reinforced concrete (FRC) consists of polyvinyl alcohol fibers (PVA) and polypropylene fibers (PF). A total of six GFHF circular columns were constructed having a circular cross section of 250 mm and a height of 1200 mm. A commercial package ABAQUS was used for the finite element analysis (FEA) of the GFHF columns by using a modified concrete damage plastic (CDP) model for hybrid fiber-reinforced concrete (HFRC). The damaging response of GFRP bars was defined using a linear elastic model. The results depicted that the failure of GFHF columns occurred either in the upper or in the lower half portion with the rupture of GFRP longitudinal bars and GFRP spirals. The decrease in the pitch of GFRP spirals led to an improvement in the axial strength (AS) of GFHF columns. The eccentric loading caused a significant reduction in the AS of columns. The comparative study solidly substantiates the validity and applicability of the newly developed FEA models for capturing the AS of GFHF columns by considering the axial involvement of longitudinal GFRP bars and the confinement effect of transverse GFRP spirals. So, the suggested numerical model having a complex system of equations for HFRC can be used for the accurate analysis of HFRC members.

Polymer composites have been identified as the most innovative and selective materials known in the 21st century. Presently, polymer concrete composites (PCC) made from industrial or agricultural waste are becoming more popular as the demand for high-strength concrete for various applications is increasing. Polymer concrete composites not only provide high strength properties but also provide specific characteristics, such as high durability, decreased drying shrinkage, reduced permeability, and chemical or heat resistance. This paper provides a detailed review of the utilization of polymer composites in the construction industry based on the circular economy model. This paper provides an updated and detailed report on the effects of polymer composites in concrete as supplementary cementitious materials and a comprehensive analysis of the existing literature on their utilization and the production of polymer composites. A detailed review of a variety of polymers, their qualities, performance, and classification, and various polymer composite production methods is given to select the best polymer composite materials for specific applications. PCCs have become a promising alternative for the reuse of waste materials due to their exceptional performance. Based on the findings of the studies evaluated, it can be concluded that more research is needed to provide a foundation for a regulatory structure for the acceptance of polymer composites.

Numerous environmental issues arise as a result of a linear economy strategy: reserves become scarce and end up in landfills and as greenhouse gases. Utilizing waste as a resource or shifting towards a circular economy are among the effective strategies for addressing these issues. To track this shift, appropriate measures that concentrate on sustainable development while taking practical contexts into consideration are required. In this paper, we utilize plastic wastes as a replacement for bitumen for reuse aiming at a circular economy. The use of plastic waste materials, i.e., plastic bottles (PET) and gas pipes (PE) in asphalt materials as a bitumen modifier was studied through series of experimental lab test methods. Marshall samples were prepared using a conventional Marshall method containing five different percentages (0%, 5%, 10%, 15%, and 20%) of plastic content by total weight of bitumen. Samples were tested after 1 and 30 days and the result shows that the stability of plastic-modified asphalt concrete was increased after 30 days, while still meeting standard criteria with plastic contents up to 20%. Moreover, the addition of waste plastic in road construction is a very effective strategy for reusing plastic waste, which also provides economic and social benefits for a sustainable approach to road pavements.

The behaviour of asphalt concrete mixtures is difficult to understand due to its complex nature under different loading conditions and environmental factors. For prediction, there is a need to find mathematical relations between multiple inputs and outputs using a simple and precise way. Recently, artificial neural networks (ANNs) has been widely used to study the mechanical parameters of asphalt concrete materials and its applications in civil engineering fields. This study presents the application of ANNs method for prediction of Marshall stability of asphalt concrete developed with two different types of aggregates based on mineralogy under four different testing temperatures ranging between 25 °C and 60 °C. The ANNs model established with six input variables including temperature, aggregate type, ultrasonic pulse velocity–time and space volume, unit volume of dry air, and saturated surface dry weight. The proposed model developed using six neurons in hidden layer for the prediction of experimental data. The feasibility of the proposed model checked in terms of root mean square error (RMSE) and coefficient of determination (R2). The R2 values found within range during both training (0.909–0.999) and validation phase (0.886–0.997) depending on estimated parameters. Moreover, the influence of different aggregate type has been investigated under varying temperatures conditions using the proposed ANNs method. The proposed model has shown the potential to understand the mechanical behaviour of sustainable asphalt concretes accurately under various temperature conditions.

Self-Compacting Concrete (SCC) differs from the normal concrete as it has the basic capacity to consolidate under its own weight. The increased awareness regarding environmental disturbances and its hazardous effects caused by blasting and crushing procedures of stone, it becomes a delicate and obvious issue for construction industry to develop an alternative remedy as material which can reduce the environmental hazards and enable high-performance strength to the concrete, which would make it durable and efficient for work. A growing trend is being established all over the world to use industrial byproducts and domestic wastes as a useful raw material in construction, as it provides an eco-friendly edge to the construction process and especially for concrete. This study aims to enlighten the use and comparative analysis for the performance of concrete with added industrial byproducts such as Ground Granulated Blast Furnace Slag (GGBFS), Silica fumes (SF) and Marble Powder (MP) in the preparation of SCC. This paper deals with the prediction of mechanical properties (i.e., compressive, tensile and flexural Strength) of self-compacting concrete by considering four major factors such as type of additive, percentage additive replaced, curing days and temperature using Artificial Neural Networks (ANNs). This project has been supported by PIET.