A Neural Network-Based Prediction of Superplasticizers Effect on the Workability and Compressive Characteristics of Portland Pozzolana Cement-Based Mortars

Abstract

Portland Pozzolana Cement (PPC) mortars are predominantly employed in plastering works to achieve better workability, superior surface finish, and higher fineness to offer better cohesion with fine aggregates than the ordinary Portland cement (OPC) mortars. To achieve high performance in the cement mortar similar to cement concrete, the addition of a superplasticizer is recommended. The present study investigates the impact of addition of sulphonated naphthalene formaldehyde- (SNF)-based (0.5%, 0.6%, 0.7%, and 0.8%) and lignosulphate- (LS)-based (0.2%, 0.3%, 0.4%, and 0.5%) superplasticizers on the workability and compressive strength characteristics of PPC mortars. Plastering mortars of ratio 1 : 4 were prepared with natural sand and manufacturing sand (M sand) as fine aggregates. A flow table test was conducted on all the mortar mix proportions, and the effects of the inclusion of superplasticizers on flow properties were recorded at different time intervals (0, 30, 60, 90, and 120 minutes). PPC mortar cubes were prepared, cured, and examined to assess the inclusion of chemical admixtures on compressive strength at different ages (1, 3, 7, 14, and 28 days). The experimental findings from the workability and compressive strength of PPC mortars were analyzed, and the corresponding results were predicted using artificial intelligence. Experimental investigations demonstrated that the desired flow characteristics and higher compressive strength results were achieved from a 0.7% dosage of ligno-based superplasticizer. The predicted workability and compressive strength results at various ages acquired by implementing an Artificial Neural Network (ANN) were found to be in close agreement with the experimental results.