Comprehensive Review of State-of-the-Art Applications of Artificial Neural Networks in Predicting Concrete Compressive Strength

Abstract

Concrete compressive strength prediction is a crucial aspect in civil engineering, with applications ranging from structural design to quality control in construction projects. Traditional methods for predicting concrete compressive strength often rely on empirical formulas or physical testing, which may be limited in accuracy or efficiency. In recent years, Artificial Neural Networks (ANNs) have emerged as powerful tools for predicting concrete compressive strength due to their ability to capture complex nonlinear relationships in data. This paper provides a comprehensive review of the state-of-the-art applications of ANNs in predicting concrete compressive strength. It discusses various architectures, training techniques, input parameters, and datasets used in ANN models, as well as their performance compared to traditional methods. Additionally, challenges and future directions in the field are identified to guide further research efforts.