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Feature engineering for predicting compressive strength of high-strength concrete with machine learning models
High-performance concrete finds extensive application in a diverse range of civil engineering structures, such as towering buildings, swift roadways, sea-spanning bridges, dams, and marine constructions. Anticipating the compressive strength of concrete poses significant challenges due to its inherent complexity. However, this study successfully utilized machine learning approaches to accurately predict the compressive strength of high-strength concrete. Through experimental programs, the researchers gathered 100 data points, enabling a comprehensive evaluation of the model's predictive capabilities across various strength parameters. The constructed models exhibited precise predictions of the compressive strength of geopolymer concrete, as evidenced by the high R2 value and low root-mean-square error value.
Feature engineering for predicting compressive strength of high-strength concrete with machine learning models
High-performance concrete finds extensive application in a diverse range of civil engineering structures, such as towering buildings, swift roadways, sea-spanning bridges, dams, and marine constructions. Anticipating the compressive strength of concrete poses significant challenges due to its inherent complexity. However, this study successfully utilized machine learning approaches to accurately predict the compressive strength of high-strength concrete. Through experimental programs, the researchers gathered 100 data points, enabling a comprehensive evaluation of the model's predictive capabilities across various strength parameters. The constructed models exhibited precise predictions of the compressive strength of geopolymer concrete, as evidenced by the high R2 value and low root-mean-square error value.
Feature engineering for predicting compressive strength of high-strength concrete with machine learning models
Asian J Civ Eng
Kumar, Pramod (Autor:in) / Pratap, Bheem (Autor:in)
Asian Journal of Civil Engineering ; 25 ; 723-736
01.01.2024
14 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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