Estimating Slump Flow and Compressive Strength of Self-Compacting Concrete Using Emotional Neural Networks

Mosbeh R Kaloop,Mohamed Shafeek,Jong Wan Hu,Pijush Samui

doi:10.3390/app10238543

Abstract

The characteristics of fresh and hardened self-compacting concrete (SCC) are an essential requirement for construction projects. Moreover, the sensitivity of admixture contents of SCC in these properties is highly impacted by that cost. The current study investigates to estimate the slump-flow (S) and compressive strength (CS), as fresh and hardened properties of SCC, respectively. Four developed soft-computing approaches were proposed and compared, including the group method of data handling (GMDH), Minimax Probability Machine Regression (MPMR), emotional neural network (ENN), and hybrid artificial neural network-particle swarm optimization (ANN-PSO), to estimate the S and 28-day CS of SCC, which comprises fly ash (FA), silica fume (SF), and limestone powder (LP) as part of cement by mass in total powder content. In addition, the impact of eight admixture components is investigated and evaluated to assess the sensitivity of admixture contents for the modelling of S and CS of SCC. The results demonstrate that the performance prediction of ENN model is more significant than other models in estimating S and CS characteristics of SCC. The overall of Pearson correlation coefficient, r, and root mean square error (RMSE) of ENN model are 97.80% and 20.16 mm, respectively, for the S. These are 96.07% and 2.59 MPa, respectively, for the CS. Furthermore, the sensitivity of the powder content of fly ash is shown to have a high impact on the estimated S and CS values of SCC.

Highlights

Concrete workability and strength are essential characteristics that should be determined and used widely in construction projects [1,2,3]
Dutta et al [11] applied Minimax Probability Machine Regression (MPMR) to predict the Compressive strength (CS) of concrete, and the results showed that the performance of it is acceptable, coefficient of correlation (R) = 93.5%, to use for estimating the CS values
Deep learning based on artificial neural network (ANN) was applied to estimate the S value of self-compacting concrete (SCC), and the results showed the performance of the proposed model could be utilized routinely estimate SCC workability [13]

Summary

Introduction

Concrete workability and strength are essential characteristics that should be determined and used widely in construction projects [1,2,3]. The workability of concrete is required for the handling and producing of concrete during the construction. While it affects the cost of construction finalization [1]. With increasing the engineering applications of concrete, the self-compacting concrete (SCC) is developing to use with easy replacement in a narrow spacing of steel of reinforcement concrete [2,4,5]. Both SCC characteristics, CS and S, should be accurately estimated

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Methods

Conclusion