Self-Healing Bio-Concrete Using Encapsulated in Iron Oxide Nanoparticles.

For the creation of healable cement concrete matrix, microbial self-healing solutions are significantly more creative and potentially successful. The current study investigates whether gram-positive "" () microorganisms can effectively repair structural and non-structural cracks caused at the nano- and microscale. By creating an effective immobilization strategy in a coherent manner, the primary challenge regarding the viability of such microbes in a concrete mixture atmosphere has been successfully fulfilled.

Review on Carbonation Study of Reinforcement Concrete Incorporating with Bacteria as Self-Healing Approach.

This study carried out a comprehensive review to determine the carbonation process that causes the most deterioration and destruction of concrete. The carbonation mechanism involved using carbon dioxide (CO) to penetrate the concrete pore system into the atmosphere and reduce the alkalinity by decreasing the pH level around the reinforcement and initiation of the corrosion process. The use of bacteria in the concrete was to increase the pH of the concrete by producing urease enzyme.

Application of Machine Learning Techniques for Predicting Compressive, Splitting Tensile, and Flexural Strengths of Concrete with Metakaolin.

The mechanical properties of concrete are the important parameters in a design code. The amount of laboratory trial batches and experiments required to produce useful design data can be decreased by using robust prediction models for the mechanical properties of concrete, which can save time and money. Portland cement is frequently substituted with metakaolin (MK) because of its technical and environmental advantages.

Using Machine Learning Algorithms to Estimate the Compressive Property of High Strength Fiber Reinforced Concrete.

The low tensile strain capacity and brittle nature of high-strength concrete (HSC) can be improved by incorporating steel fibers into it. Steel fibers' addition in HSC results in bridging behavior which improves its post-cracking behavior, provides cracks arresting and stresses transfer in concrete. Using machine learning (ML) techniques, concrete properties prediction is an effective solution to conserve construction time and cost.

Investigating the Ultrasonic Pulse Velocity of Concrete Containing Waste Marble Dust and Its Estimation Using Artificial Intelligence.

Researchers and engineers are presently focusing on efficient waste material utilization in the construction sector to reduce waste. Waste marble dust has been added to concrete to minimize pollution and landfills problems. Therefore, marble dust was utilized in concrete, and its prediction was made via an artificial intelligence approach to give an easier way to scholars for sustainable construction.