Deep learning techniques in concrete powder mix designing

Abstract

The water-cement ratio is multiple phases procedure in which we aim to determine the most optimal combination for producing high-performing concrete. In modern literature and business practice, there are various methods for designing concrete mixes, although the Three Equation Method-inspired procedures are by far the most widely used. Concrete compressive strength is one of the fundamental properties that determines its class. Foreseeable compressive strength concrete is necessary to promote the use of concrete structures. The primary feature of its durability and safety is. Deep learning has recently received a lot of attention, and the prospects for this technology are even brighter. Machine learning algorithms have advanced to the point that they can recognize patterns, which are difficult for humans to recognize. This has sparked interest in data mining on enormous datasets. In this research, we aim to utilize cutting-edge developments in machine learning techniques for the production of concrete mixes. To provide the ideal structure of a synthetic neural network that has been chosen, we compiled a comprehensive dataset of concrete mixtures, complete with laboratory destructive test results. A mathematical formula that may be used in practical applications has been developed from the creation of an artificial neural network.

Keywords

• deep learning techniques
• concrete powder mix
• water-cement ratio
• concrete compressive strength