Using an Artificial Neural Network to Validate and Predict the Physical Properties of Self-Compacting Concrete

Abstract

SCC (self-compacting concrete) is a high-flowing concrete that blasts into structures. Many academics have been interested in using an artificial neural network (ANN) to forecast concrete strength in recent years. As a result, the goal of this study is to confirm the various possibilities of using an artificial neural network (ANN) to detect the features of SCC when Portland Pozzolana Cement (PPC) is partially substituted with biowaste such as Bagasse Ash (BA) and Rice Husk Ash (RHA) (RHA). Specialist systems based on the fully connected cascade (FCC) architecture in artificial neural networks (ANN) are used to estimate the compressive toughness of SCC. The research results are confirmed with the forecasting results of ANN utilizing 73 trial datasets of differentiation focus proposals of cement, BA, and RHA containing parameters such as initial setting time (IST), final setting time (FST), and standard consistency. Experiments to determine compressive strength for a wider range of mixed prepositions will result in higher project expenses and delays. So, an expert system ANN is used to find the standard consistency, setting time, and compressive strength for the intermediate mix propositions according to IS 10262:2009. The experimental results of compressive strength for 28 days are considered, in which 70% was used to train the ANN and 30% was utilized for testing the accuracy of the predicted compressive strength for the intermediate mix proposition. Using all of the datasets, the number of hidden layers used for compressive strength prediction for intermediate mix proposal is determined in the first step. The compressive strength for the intermediate mix preposition was identified in the second phase of the research, using the number of hidden layers determined in the first phase. The results were validated using the correlation coefficient (R) and root mean square error (RMSE) obtained from ANN, resulting in an acceptance range of 97 percent to 99 percent.

Investigation of Mechanical and Thermal Stabilities of Tamarind Seed- and Peanut Shell Powder-Reinforced Vinyl Ester Composite

“…Tis simultaneous enhancement in both fexural strength and modulus underscores the efcacy of the TMS/PNS hybrid fller in reinforcing the composite for structural applications. Tis is because the hybrid fller has a larger elasticity modulus than pure resin [29][30][31][32][33][34]. Te fexural resistance of the material improves when there is excellent adhesion between the fller and the resin at the interface.…”

Applications of Neural Network and Neuro-Fuzzy Network to Estimate the Parameters of Self-Compacting Concrete

“…For example, R28 was predicted in [8] based on 4 inputs: the water/binder (W/B) ratio, the control compressive strength, the percentage of plastic replacement and the plastic type. But in [9], R28 is predicted from on the W/B ratio and 6 other inputs, and in [10] the number of inputs is increased to 15. The work of [7] used 7 inputs such as cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate to predict R28.…”

Prediction of the Dynamic Properties of Concrete Using Artificial Neural Networks

“…Evaluate the dynamic properties of pre-treated rubberized concrete under incremental loading [30]. Evaluate the mechanical and dynamic properties of rubberized concrete [31], study the effect of loading rate on the dynamic properties of plastic concrete under triaxial loading [28], predict the residual flexural strength of fiber-reinforced concrete [32,33], calculate the shear strength of corrosion-reinforced concrete beams [28], and validate and predict the physical properties of selfcompacting concrete [34]. Predictive models based on ANNs are used to predict the ultimate conditions and stress-strain behavior of steel-confined ultra-high-performance concrete [35] and for the prediction of concrete properties [36].…”