A New Method for Predicting the Ingredients of Self-Compacting Concrete (SCC) Including Fly Ash (FA) Using Data Envelopm
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RESEARCH ARTICLE-CIVIL ENGINEERING
A New Method for Predicting the Ingredients of Self-Compacting Concrete (SCC) Including Fly Ash (FA) Using Data Envelopment Analysis (DEA) Farzad Rezai Balf1 · Hamidreza Mahmoodi Kordkheili2
· Alireza Mahmoodi Kordkheili3
Received: 8 May 2020 / Accepted: 29 August 2020 © King Fahd University of Petroleum & Minerals 2020
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. Keywords Self-compacting concrete (SCC) · Data envelopment analysis (DEA) · Optimum mixed design · Compressive strength · Rheological properties · Fly ash (FA)
1 Introduction For the past two decades, various methods of modeling and forecasting of concrete properties have been popularized based on artificial intelligence (AI) methods, such as
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Hamidreza Mahmoodi Kordkheili [email protected]; [email protected] Farzad Rezai Balf [email protected] Alireza Mahmoodi Kordkheili [email protected]
1
Department of Mathematics, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran
2
Department of Civil Engineering, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran
3
Department of Civil Engineering, Aryan Institute of Science and Technology, Babol, Iran
fuzzy logic (FL) systems, artificial neural networks (ANNs), genetic algorithm (GA), expert systems (ES), and other famous methods in the technical literature like least square support vector machine (LSSVM), design of experiments (DOE), support vector machines (SVM), relevance vector machine (RVM), random kitchen sink algorithm (RKS), least squares sup
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