Soft Computing for Comprehensive Concrete Strength Prediction – A Comparative Study

Abstract

The evolution of concrete strength prediction methodologies has transitioned from empirical formulas based on experimental data to contemporary soft computing approaches. Initially, the concrete mix design was reliant on simple relationships between concrete mix proportions and compressive strength; later, the early techniques evolved to include statistical models incorporating material properties, curing conditions, and environmental variables. The advent of computational tools and artificial intelligence marked a paradigm shift, with accurate concrete strength prediction crucial for influencing structural integrity, safety, and cost-effectiveness in construction. The article explores empirical and analytical concrete strength prediction models before reviewing the application of soft computing approaches such as fuzzy logic, genetic algorithms, and neural networks. The integration of these models and hybrid approaches is discussed in this research study by highlighting their effectiveness in handling complex relationships within concrete mix parameters. A comparative analysis of various soft computing methods applied to structural and non-structural elements is carried out in this study to demonstrate their diverse applications and advantages in optimizing concrete mix designs, enhancing structural performance, and contributing to cost and time efficiency in construction processes.