Abstract
The experimental design of high‐strength concrete (HSC) requires deep analysis to get the target strength. In this study, machine learning approaches and artificial intelligence python‐based approaches have been utilized to predict the mechanical behaviour of HSC. The data to be used in the modelling consist of several input parameters such as cement, water, fine aggregate, and coarse aggregate in combination with a superplasticizer. Empirical relation with mathematical expression has been proposed using engineering programming. The efficiency of the models is assessed by statistical analysis with the error by using MAE, RRMSE, RSE, and comparisons were made between regression models. Moreover, variable intensity and correlation have shown that deep learning can be used to know the exact amount of materials in civil engineering rather than doing experimental work. The expression tree, as well as normalization of the graph, depicts significant accuracy between target and output values. The results reveal that machine learning proposed adamant accuracy and has elucidated performance in the prediction aspect.
Highlights
High-strength concrete (HSC) production in the construction industry has been adamantly upsurge in recent years for use in modern construction work [1,2,3]
This paper aims to build a genetic engineering programming (GEP)-based model for accurate prediction for high-strength concrete with an empirical equation
This paper aims to develop a generalized equation for the compressive strength of high-strength concrete
Summary
High-strength concrete (HSC) production in the construction industry has been adamantly upsurge in recent years for use in modern construction work [1,2,3]. HSC is a modified form of concrete that requires vibrating media and nonvibrating media for its placement; it is dense and homogenous concrete with adamant high strength and superior durability properties as compared with traditional concrete making it extensively applicable to the concrete industry [5, 6]. It is adamantly used for high-rise buildings, long-span bridges, piers, etc.
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