Utilizing Artificial Intelligence to Predict the Superplasticizer Demand of Self-Consolidating Concrete Incorporating Pumice, Slag, and Fly Ash Powders.

Jing Liu,Maria Rashidi,Masoud Mohammadi,Peyman Mehrabi,Pengqiang Zheng,Yubao Zhan

doi:10.3390/ma14226792

Abstract

Self-consolidating concrete (SCC) is a well-known type of concrete, which has been employed in different structural applications due to providing desirable properties. Different studies have been performed to obtain a sustainable mix design and enhance the fresh properties of SCC. In this study, an adaptive neuro-fuzzy inference system (ANFIS) algorithm is developed to predict the superplasticizer (SP) demand and select the most significant parameter of the fresh properties of optimum mix design. For this purpose, a comprehensive database consisting of verified test results of SCC incorporating cement replacement powders including pumice, slag, and fly ash (FA) has been employed. In this regard, at first, fresh properties tests including the J-ring, V-funnel, U-box, and different time interval slump values were considered to collect the datasets. At the second stage, five models of ANFIS were adjusted and the most precise method for predicting the SP demand was identified. The correlation coefficient (R2), Pearson’s correlation coefficient (r), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), mean absolute error (MAE), and Wilmot’s index of agreement (WI) were used as the measures of precision. Later, the most effective parameters on the prediction of SP demand were evaluated by the developed ANFIS. Based on the analytical results, the employed algorithm was successfully able to predict the SP demand of SCC with high accuracy. Finally, it was deduced that the V-funnel test is the most reliable method for estimating the SP demand value and a significant parameter for SCC mix design as it led to the lowest training root mean square error (RMSE) compared to other non-destructive testing methods.

Highlights

Self-consolidating concrete (SCC) is a type of concrete that requires a higher dosage of cement and fine aggregate as well as lower coarse aggregate content in comparison with normal concrete [1]
High cement content is one of the harmful issues of SCC for the environment. Segregation is another typical problem of employing SCC that is highly sensitive to the water to cement ratio
(SP)demand demandisiscomplicated complicated due many facinvolved in the estimation problem

Summary

Introduction

SCC is a type of concrete that requires a higher dosage of cement and fine aggregate as well as lower coarse aggregate content in comparison with normal concrete [1]. This material provides a high level of workability for structural applications and is a good option for enormous cast volume. High cement content is one of the harmful issues of SCC for the environment. Segregation is another typical problem of employing SCC that is highly sensitive to the water to cement ratio. In order to deal with high cement content, various cement replacement powders including either natural or synthetic powders have been proposed and verified by reliable investigations [3,4,5]

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