Search
Abstract
Concrete mix design is a complex and multistage process in which we try to find the best composition of ingredients to create good performing concrete. In contemporary literature, as well as in state-of-the-art corporate practice, there are some methods of concrete mix design, from which the most popular are methods derived from The Three Equation Method. One of the most important features of concrete is compressive strength, which determines the concrete class. Predictable compressive strength of concrete is essential for concrete structure utilisation and is the main feature of its safety and durability. Recently, machine learning is gaining significant attention and future predictions for this technology are even more promising. Data mining on large sets of data attracts attention since machine learning algorithms have achieved a level in which they can recognise patterns which are difficult to recognise by human cognitive skills. In our paper, we would like to utilise state-of-the-art achievements in machine learning techniques for concrete mix design. In our research, we prepared an extensive database of concrete recipes with the according destructive laboratory tests, which we used to feed the selected optimal architecture of an artificial neural network. We have translated the architecture of the artificial neural network into a mathematical equation that can be used in practical applications.
Citations
-
8 6
CrossRef
-
0
Web of Science
-
9 5
Scopus
Authors (2)
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/ma12081256
- License
-
open in new tab
Keywords
Details
- Category:
- Articles
- Type:
- artykuł w czasopiśmie wyróżnionym w JCR
- Published in:
-
Materials
no. 12,
pages 1256 - 1252,
ISSN: 1996-1944 - Language:
- English
- Publication year:
- 2019
- Bibliographic description:
- Ziółkowski P., Niedostatkiewicz M.: Machine Learning Techniques in Concrete Mix Design// Materials. -Vol. 12, iss. 8 (2019), s.1256-1252
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/ma12081256
- Verified by:
- Gdańsk University of Technology
seen 210 times