Evaluating the strength of industrial wastesbased concrete reinforced with steel fiber using advanced machine learning.

The traditional evaluation of compressive strength through repeated experimental works can be resource-intensive, time-consuming, and environmentally taxing. Leveraging advanced machine learning (ML) offers a faster, cheaper, and more sustainable alternative for evaluating and optimizing concrete properties, particularly for materials incorporating industrial wastes and steel fibers. In this research work, a total of 166 records were collected and partitioned into training set (130 records = 80%) and validation set (36 records = 20%) in line with the requirements of data partitioning and sorting for optimal model performance.

Physics-informed modeling of splitting tensile strength of recycled aggregate concrete using advanced machine learning.

Physics-informed modeling (PIM) using advanced machine learning (ML) represents a paradigm shift in the field of concrete technology, offering a potent blend of scientific rigor and computational efficiency. By harnessing the synergies between physics-based principles and data-driven algorithms, PIM-ML not only streamlines the design process but also enhances the reliability and sustainability of concrete structures. As research continues to refine these models and validate their performance, their adoption promises to revolutionize how concrete materials are engineered, tested, and utilized in construction projects worldwide.

Prediction and validation of mechanical properties of self-compacting geopolymer concrete using combined machine learning methods a comparative and suitability assessment of the best analysis.

Recent sustainable engineering trends show the re-use of wastes in the production of concrete materials. This was important in two ways. First, there is a great environmental necessity to eliminate these industrial wastes and their usage in a solid waste upcycling system to ensure structural sustainability creates an avenue for this process.

Structural retrofitting of RC slabs using bamboo fibre laminate: Flexural performance and crack patterns.

Enhancing the durability of structural elements is a viable approach to promote sustainability in civil engineering. Research has shown that well-maintained slabs outperform degraded ones, which deteriorate rapidly due to insufficient upkeep. The occurrence of cracking and deformation in slabs subjected to sustained loads significantly impacts their functionality.

Development of Fibre-Reinforced Cementitious Mortar with Mineral Wool and Coconut Fibre.

Globally, as human population and industries grow, so does the creation of agricultural, industrial, and demolition waste. When these wastes are not properly recycled, reused, or disposed of, they pose a threat to the environment. The importance of this study lies in the beneficial use of coconut fibre and mineral wool in the form of fibres in cement mortar production.

Experimental Findings and Validation on Torsional Behaviour of Fibre-Reinforced Concrete Beams: A Review.

Fibres have long been utilized in the construction sector to improve the mechanical qualities of structural elements such as beams, columns, and slabs. This study aims to review the torsional behaviour of various forms of fibre reinforced concrete to identify possible enhancements and the practicability of concrete structural beams. Concrete reinforced steel fibre, synthetic fibre, and hybrid fibre are examples of fibre reinforced concrete.

Structural Retrofitting of Corroded Reinforced Concrete Beams Using Bamboo Fiber Laminate.

Corrosion creates a significant degradation mechanism in reinforced concrete (RC) structures, which would require a high cost of maintenance and repair in affected buildings. However, as the cost of repairing corrosion-damaged structures is high, it is therefore pertinent to develop alternative eco-friendly and sustainable methods. In this study, structural retrofitting of corroded reinforced concrete beams was performed using bamboo fiber laminate.