COMPARATIVE ASSESSMENT OF THE STRENGTH OF CONCRETE MADE FROM VARIOUS AGGREGATE USING ARTIFICIAL INTELLIGENCE

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COMPARATIVE ASSESSMENT OF THE STRENGTH OF CONCRETE MADE FROM VARIOUS AGGREGATE USING ARTIFICIAL INTELLIGENCE

Abstract

This study presents a comparative assessment of the strength of concrete made from various aggregates using artificial intelligence (AI) techniques. Concrete, a fundamental material in construction, derives its mechanical properties significantly from the type and quality of aggregates used. Traditional methods for predicting concrete strength often rely on empirical relationships and experimental testing, which can be time-consuming and resource-intensive.

In contrast, AI offers a promising approach to enhance the accuracy and efficiency of predicting concrete strength based on aggregate properties. This research leverages AI algorithms such as machine learning models and neural networks to analyze diverse datasets encompassing different types of aggregates. The study explores how AI can learn and generalize from data to predict concrete strength more effectively than conventional methods.

Key objectives include evaluating the predictive capabilities of AI models in estimating concrete compressive strength based on aggregate characteristics such as size, shape, texture, and mineral composition. Case studies and experimental data from laboratory tests are utilized to validate the AI models’ accuracy and reliability in diverse environmental conditions and construction applications.

The findings of this study aim to contribute to advancements in concrete technology by providing insights into optimizing aggregate selection for achieving desired concrete strength parameters. By harnessing the potential of AI, this research seeks to enhance construction practices, improve structural durability, and support sustainable development in the built environment.

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