Fresh, Durability and Mechanical Behavior of Self Consolidating Concrete Incorporating Fly Ash and Admixture under Hot Weather

The self-consolidating concrete (SCC) become the material of choice by concrete industry due to its superior properties. However, these properties need to be verified under hot weather conditions. The paper investigates the behavior of SCC under hot weather. Six SCC mixtures were prepared under high temperatures. The SCC mixtures incorporated polycarboxylate admixture at different dosages and prolonged mixed for up to 2 hours at 30 °C and 40 °C. The cement paste was replaced with 20% of fly ash (FA). The fresh properties were investigated using slump flow, T50, and VSI tests. The compressive strength was measured at 3, 7, and 28 days. The durability of SCC mixtures was evaluated by conducting rapid chloride penetration and water absorption tests.

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

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.