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Advancements in Concrete Engineering: A Comprehensive Review of Computational Methods for Strength Prediction and Optimization

Yash Dangi, Harsh Rathore

Abstract


The utilization of computational methods has revolutionized the field of concrete engineering, offering unprecedented insights into concrete behavior and strength prediction. Among these methods, Artificial Neural Networks (ANN), MATLAB, and FUZZY logic have emerged as powerful tools for analyzing complex systems and making accurate predictions based on vast datasets. This review explores the application of computational methods in forecasting concrete condition and strength, focusing on the methodologies employed, comparative analysis with traditional approaches, and practical applications in concrete engineering. Through an extensive examination of existing literature and research findings, the review highlights the effectiveness of ANN models in predicting concrete properties, such as compressive strength, slump, and durability. Additionally, the review identifies areas for future research and innovation in leveraging computational techniques to optimize concrete mix designs, assess the effects of additives, and enhance construction scheduling and quality control processes. By harnessing the potential of computational methods, concrete engineers can unlock new avenues for innovation and efficiency in the construction industry.


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References


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