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

Abstract

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.

Citations

5

CrossRef
0

Web of Science
6

Scopus

Author (1)

Patryk Ziółkowski dr inż.

Cite as

Full text

download paper

downloaded 73 times

Publication version: Accepted or Published Version
DOI:: Digital Object Identifier (open in new tab) 10.3390/ma16175956
License: open in new tab

full content of the article see on external site open in new tab

Keywords

Details

Category:

Articles

Type:

artykuły w czasopismach

Published in:

Materials no. 16,
ISSN: 1996-1944

Language:

English

Publication year:

2023

Bibliographic description:

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: