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Title |
Development of a Concrete Rheology Parameter Prediction Model Based on Concrete Slump Flow Using a Machine Learning Algorithm
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Authors |
이유정(Yu-Jeong Lee) ; 김인태(In-Tae Kim) ; 한동엽(Dong-Yeop Han) |
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DOI |
https://doi.org/10.4334/JKCI.2024.36.1.061 |
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Keywords |
레올로지; 콘크리트; 머신러닝; 인공신경망 rheology; concrete; machine learning; artificial neural network |
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Abstract |
This study involved developing a predictive model for concrete rheological parameters using a machine learning algorithm, based on conventional test results of concrete slump flow. To achieve this, the prediction model’s performance was assessed according to data preprocessing, data quality, and the quantity of training data. Analysis revealed that data preprocessing involving both data cleaning and normalization proved effective. Furthermore, the predictive model’s performance improved with higher quality and a larger volume of training data. This study can contribute to the development of a prediction model for rheology parameters based on fresh concrete slump flow data.
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