Artificial intelligence-based modeling of compressive strength of slurry infiltrated fiber concrete

Abstract

The purpose of this paper is to develop a reliable model that would predict the compressive strength of slurry infiltrated fiber concrete (SIFCON) modified with various supplementary cementitious materials (SCMs) using artificial intelligence approach.</jats:p> </jats:sec> <jats:sec><jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title> <jats:p>This study engaged the artificial intelligence to predict the compressive strength of SIFCON through deep neural networks (DNN), artificial neural networks, linear regression, regression trees, support vector machine, ensemble trees, Gaussian process regression and neural networks (NN). A thorough data set of 387 samples was gathered from relevant studies. Eleven variables (cement, silica fume, fly ash, metakaolin, steel slag, fine aggregates, steel fiber fraction, steel fiber aspect ratio, superplasticizer, water to binder ratio and curing ages) were taken as input to predict the output (compressive strength). The accuracy and reliability of the developed models were assessed using a variety of performance metrics.</jats:p> </jats:sec> <jats:sec><jats:title content-type="abstract-subheading">Findings</jats:title> <jats:p>The results showed that the DNN (11-20-20-20-1) predicted the compressive strength of SIFCON better than the other algorithms with <jats:italic>R</jats:italic><jats:sup>2</jats:sup> and mean square error yielding 95.89% and 8.07. The sensitivity analysis revealed that steel fiber, cement, silica fume, steel fiber aspect ratio and superplasticizer are the most vital variables in estimating the compressive strength of SIFCON. Steel fiber contributed the highest value to the SIFCON’s compressive strength with 16.90% impact.</jats:p> </jats:sec> <jats:sec><jats:title content-type="abstract-subheading">Originality/value</jats:title> <jats:p>This is a novel technique in predicting the compressive strength of SIFCON optimized with different SCMs using supervised learning algorithms, improving its quality and performance.</jats:p> </jats:sec>