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Open AccessEditor’s ChoiceArticle

Model-Based Adaptive Machine Learning Approach in Concrete Mix Design

1
Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Gabriela Narutowicza 11/12, 80-233 Gdansk, Poland
2
School of Civil Engineering, Chongqing University, Chongqing 400045, China
3
Key Laboratory of New Technology for Construction of Cities in Mountain Area, Chongqing University, Chongqing 400045, China
*
Author to whom correspondence should be addressed.
Academic Editors: Łukasz Sadowski and Qing-feng Liu
Materials 2021, 14(7), 1661; https://doi.org/10.3390/ma14071661
Received: 30 January 2021 / Revised: 23 March 2021 / Accepted: 24 March 2021 / Published: 28 March 2021
(This article belongs to the Special Issue Artificial Intelligence for Cementitious Materials)
Concrete mix design is one of the most critical issues in concrete technology. This process aims to create a concrete mix which helps deliver concrete with desired features and quality. Contemporary requirements for concrete concern not only its structural properties, but also increasingly its production process and environmental friendliness, forcing concrete producers to use both chemically and technologically complex concrete mixtures. The concrete mix design methods currently used in engineering practice are joint analytical and laboratory procedures derived from the Three Equation Method and do not perform well enough for the needs of modern concrete technology. This often causes difficulties in predicting the final properties of the designed mix and leads to precautionary oversizing of concrete properties for fear of not providing the required parameters. A new approach that would make it possible to predict the newly designed concrete mix properties is highly desirable. The answer to this challenge can be methods based on machine learning, which have been intensively developed in recent years, especially in predicting concrete compressive strength. Machine learning-based methods have been more or less successful in predicting concrete compressive strength, but they do not reflect well the variability that characterises the currently used concrete mixes. A new adaptive solution that allows estimating concrete compressive strength on the basis of the concrete mix main ingredient composition by including two observations for a given batch of concrete is proposed herein. In presented study, a machine learning model was built with a deep neural network architecture, trained on an extensive database of concrete recipes, and translated into a mathematical formula. Testing on four concrete mix recipes was performed, which were calculated according to contemporary design methods (Bolomey and Fuller method), and a comparative analysis was conducted. It was found out that the new algorithm performs significantly better than that without adaptive features trained on the same dataset. The presented algorithm can be used as a concrete strength checking tool for the concrete mix design process. View Full-Text
Keywords: applied machine learning; concrete; concrete mix design; concrete strength prediction; data mining applied machine learning; concrete; concrete mix design; concrete strength prediction; data mining
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MDPI and ACS Style

Ziolkowski, P.; Niedostatkiewicz, M.; Kang, S.-B. Model-Based Adaptive Machine Learning Approach in Concrete Mix Design. Materials 2021, 14, 1661. https://doi.org/10.3390/ma14071661

AMA Style

Ziolkowski P, Niedostatkiewicz M, Kang S-B. Model-Based Adaptive Machine Learning Approach in Concrete Mix Design. Materials. 2021; 14(7):1661. https://doi.org/10.3390/ma14071661

Chicago/Turabian Style

Ziolkowski, Patryk; Niedostatkiewicz, Maciej; Kang, Shao-Bo. 2021. "Model-Based Adaptive Machine Learning Approach in Concrete Mix Design" Materials 14, no. 7: 1661. https://doi.org/10.3390/ma14071661

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