Flow direction algorithm-based machine learning approaches for the prediction of high-performance concrete strength property

Abstract

The main significance of utilizing high-performance concrete as an effective item in the construction industry is the compressive strength assessment which requires a vast investigation of the design mix with calculated relevant compressive strength. Through the intelligence approaches, planning an accurate relationship between high-performance concrete different mix designs and their compressive strength is obtainable with the lowest cost of time and finance. Two models based on support vector regression methods are developed in this regard. The optimal output is calculated by tuning support vector regression key constraints by flow direction and biography-based optimization algorithm. The data set collected from the literature is divided into the training, and the testing phase, where the training data is used to develop the models, and the testing data is utilized to validate the accuracy of the models. The results showed a higher accuracy of the FDA_SVR method than the BBO_SVR method, with R2 values of 0.9939 and 0.9755, respectively. moreover, the confidence level obtained about 4.0273 and 7.367 for the FDA-SVR and BBO-SVR, respectively, demonstrating the accurate prediction capacity of the FDA-SVR model for high-performance concrete compressive strength.