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.

Estimation of concrete materials uniaxial compressive strength using soft computing techniques

“…Moreover, the important analysis of input variables on the output is a critical step in predictive modeling, as it provides valuable insights into which factors exert the most significant influence on the target variable [ 58 ]. This understanding is instrumental in various applications, including optimizing processes, enhancing model interpretability, and guiding decision-making.…”

Evaluation of Durability Performance for Chloride Ingress Considering Long-Term Aged GGBFS and FA Concrete and Analysis of the Relationship between Concrete Mixture Characteristic and Passed Charge Using Machine Learning Algorithm

“…Currently, machine learning (ML) and deep learning (DL) algorithms are actively used in a variety of fields, and they can be used to devise new techniques for the performance prediction and evaluation of concrete quality, considering several variables [22][23][24][25][26][27][28]. In related research, DL algorithms offer the advantage of extracting and automating complex features, especially from high-level abstract concepts.…”

“…ML is used to analyze the relationship between independent and dependent variables using linear and nonlinear regression analyses [28,31]. Dealing with regression problems involves supervised learning, which is one of the most common ML methods.…”

“…In the field of concrete performance, most of the existing studies conducted using ML and DL algorithms are limited to predicting mechanical and durable performances based on specific aged days and mix design [22][23][24]28]. However, concrete is a timedependent building material whose performance improves with aged days [34].…”