A New Method for Predicting the Ingredients of Self-Compacting Concrete (SCC) Including Fly Ash (FA) Using Data Envelopment Analysis (DEA)

Abstract

Self-compacting concrete (SCC) is a liquid mixture appropriate for putting in structures with excessive reinforcement without vibration. The application of SCC has found wide use in practice. However, its application is often limited by lack of knowledge on mix material gained from laboratory tests. This paper presents a nonparametric mathematical method for the design of SCC mixes containing fly ash, which called as data envelopment analysis (DEA). DEA have the ability to estimate a set of units (a unit is consisted of multi-input–multi-output), in order to determine their efficiencies. To create DEA models, a database of experimental data was collected from the technical literature and applied. The data applied in the data envelopment analysis approach are organized in a format of six inputs parameters that contain superplasticizer, coarse aggregates, fine aggregates, water–binder ratio, fly ash replacement percentage, and the total binder content. Four outputs parameters are predicted based on the DEA method as the V-funnel time, the slump flow, the L-box ratio, and the cylindrical compressive strength at 28 days of SCC including fly ash. In this paper, we predict the optimal level of input required to produce the level of output required by SCC using DEA. To validate the usefulness of the suggested model and better its proficiency, a comparison of the DEA model with other investigator’s empirical results and other models results such as ANN was performed, and a good assent was gained.