Prediction of compressive strength of ultra-high-performance concrete using machine learning algorithms—sfs and ann

Abstract

This paper presents machine learning algorithms based on back-propagation neural network (BPNN) that employs sequential feature selection (SFS) for predicting the compressive strength of ultra-high-performance concrete (UHPC). A database, containing 110 points and eight material constituents, was collected from the literature for the development of models using machine learning techniques. The BPNN and SFS were used interchangeably to identify the relevant features that contributed with the response variable. As a result, the BPNN with the selected features was able to interpret more accurate results (r2 = 0.991) than the model with all the features (r2 = 0.816). It is concluded that the usage of ANN with SFS provided an improvement to the prediction model’s accuracy, making it a viable tool for machine learning approaches in civil engineering case studies.