Design of Experiment on Concrete Mechanical Properties Prediction: A Critical Review

Concrete mix design and the determination of concrete performance are not merely engineering studies, but also mathematical and statistical endeavors. The study of concrete mechanical properties involves a myriad of factors, including, but not limited to, the amount of each constituent material and its proportion, the type and dosage of chemical additives, and the inclusion of different waste materials. The number of factors and combinations make it difficult, or outright impossible, to formulate an expression of concrete performance through sheer experimentation. Hence, design of experiment has become a part of studies, involving concrete with material addition or replacement. This paper reviewed common design of experimental methods, implemented by past studies, which looked into the analysis of concrete performance. Several analysis methods were employed to optimize data collection and data analysis, such as analysis of variance (ANOVA), regression, Taguchi method, Response Surface Methodology, and Artificial Neural Network. It can be concluded that the use of statistical analysis is helpful for concrete material research, and all the reviewed designs of experimental methods are helpful in simplifying the work and saving time, while providing accurate prediction of concrete mechanical performance.

Automated summary

Concrete mix design and performance determination involve not only engineering, but also mathematical and statistical approaches. The study of concrete mechanical properties considers various factors, and design of experiments has become crucial in simplifying the work and providing accurate predictions. Common methods such as ANOVA, regression, Taguchi method, Response Surface Methodology, and Artificial Neural Network are used for optimizing data collection and analysis.