A Review on Green Concrete Using Fly Ash, Silica Fume and Recycled Coarse Aggregate for Reduction of Carbon Footprint

Jaya Shrivastava — 2026-05-13

Concrete is one of the most widely used construction materials due to its strength, durability, and versatility in infrastructure development. However, the production of conventional concrete has a significant environmental impact because cement manufacturing releases a large amount of carbon dioxide, and the continuous extraction of natural aggregates leads to the depletion of natural resources. These environmental concerns have encouraged researchers to explore sustainable alternatives in the construction industry. Green concrete has emerged as an effective solution for reducing the environmental impact associated with conventional concrete production. This review paper focuses on the use of industrial by-products and recycled materials such as fly ash, silica fume, and recycled coarse aggregates in concrete. Fly ash and silica fume act as supplementary cementitious materials that partially replace cement and contribute to improved strength and durability of concrete. Recycled coarse aggregates obtained from construction and demolition waste help reduce the demand for natural aggregates while supporting waste management practices. Various research studies indicate that the combined use of these materials can reduce the carbon footprint of concrete production while maintaining acceptable mechanical properties and long-term durability. Therefore, the adoption of green concrete can promote sustainable construction practices and contribute to environmentally responsible infrastructure development.

Detection and Assessment of Cracks in Concrete Structures Using Machine Learning Techniques: A Review

Isha — 2026-04-29

Cracks represent a common manifestation of concrete deterioration. The concrete construction exhibits fissures at the microscopic level. A consistent change in the structure’s size results in its failure. Crack screening methodologies encompass conventional, optical, and asymmetrical screening approaches. The conventional method evaluates the divisions through a rudimentary graphic that depicts the different states of the variances. The visual method depends on human beings to identify fractures. It is an amalgamation of human perceptual abilities and proficiency. Moreover, manual inspection is primarily employed in developing countries for the detection of fractures. It utilises scanning and tactile devices to identify and delineate fractures. Nonetheless, these methodologies possess specific constraints, like the necessity for a trained practitioner, the degree of expertise, the machinist’s understanding, and the resolution of the images. Researchers conducted multiple investigations to accurately detect fissures in the material’s framework. They have advanced the methodologies by employing image processing techniques, including edge recognition, segmentation, and categorisation. Crack detection procedures are classified into geographical, computational learning (ML), and deep learning (DL) image processing techniques (IPAs). The literature indicates that the most often employed image processing algorithms (IPAs) are the Sobel filter, Canny edge detector, Roberts’ operator, Prewitt operator, and Otsu’s threshold-based method. The efficacy of these methods is contingent upon the texture, noise, and quality of the photo. Furthermore, these techniques depend on the choice of cortical masks and sensitivity parameters. The effectiveness of machine learning-based crack detection methods relies on deciding on a set of handmade features and a precise division of the region of interest. Furthermore, the machine learning-based methodologies necessitate substantial human involvement. Moreover, DL-based approaches require accurate annotation for efficient crack detection. The research indicates that immediate crack evaluation requires the creation of a GUI for analysing the condition of ceramic structures.

Journal of Ceramics and Concrete Sciences (e-ISSN: 2582-1938) (p-ISSN: 3049-0626)

A Review on Green Concrete Using Fly Ash, Silica Fume and Recycled Coarse Aggregate for Reduction of Carbon Footprint

Detection and Assessment of Cracks in Concrete Structures Using Machine Learning Techniques: A Review