Computational Complexity and Its Influence on Predictive Capabilities of Machine Learning Models for Concrete Mix Design

Patryk Ziółkowski

doi:10.3390/ma16175956

Computational Complexity and Its Influence on Predictive Capabilities of Machine Learning Models for Concrete Mix Design

Abstrakt

The design of concrete mixtures is crucial in concrete technology, aiming to produce concrete that meets specific quality and performance criteria. Modern standards require not only strength but also eco-friendliness and production efficiency. Based on the Three Equation Method, conventional mix design methods involve analytical and laboratory procedures but are insufficient for contemporary concrete technology, leading to overengineering and difficulty predicting concrete properties. Machine learning-based methods offer a solution, as they have proven effective in predicting concrete compressive strength for concrete mix design. This paper scrutinises the association between the computational complexity of machine learning models and their proficiency in predicting the compressive strength of concrete. This study evaluates five deep neural network models of varying computational complexity in three series. Each model is trained and tested in three series with a vast database of concrete mix recipes and associated destructive tests. The findings suggest a positive correlation between increased computational complexity and the model’s predictive ability. This correlation is evidenced by an increment in the coefficient of determination (R2) and a decrease in error metrics (mean squared error, Minkowski error, normalized squared error, root mean squared error, and sum squared error) as the complexity of the model increases. The research findings provide valuable insights for increasing the performance of concrete technical feature prediction models while acknowledging this study’s limitations and suggesting potential future research directions. This research paves the way for further refinement of AI-driven methods in concrete mix design, enhancing the efficiency and precision of the concrete mix design process.

Cytowania

5

CrossRef
0

Web of Science
5

Scopus

Autor (1)

Patryk Ziółkowski dr inż.

Cytuj jako

Pełna treść

pobierz publikację

pobrano 65 razy

Wersja publikacji: Accepted albo Published Version
DOI:: Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/ma16175956
Licencja: otwiera się w nowej karcie

pełna treść artykułu zobacz w serwisie zewnętrznym otwiera się w nowej karcie

Słowa kluczowe

Informacje szczegółowe

Kategoria:

Publikacja w czasopiśmie

Typ:

artykuły w czasopismach

Opublikowano w:

Materials nr 16,
ISSN: 1996-1944

Język:

angielski

Rok wydania:

2023

Opis bibliograficzny:

Ziółkowski P.: Computational Complexity and Its Influence on Predictive Capabilities of Machine Learning Models for Concrete Mix Design// Materials -Vol. 16,iss. 17 (2023), s.5956-

DOI: