Simulation and Prediction of Strength of Geopolymer Concrete by using Artificial Neural Networks
Anogna Bayya
Structural Engineering,Department of Civil Engineering&Vignan deemed to be University , IN
C Mahalingam
Structural Engineering,Department of Civil Engineering&Vignan deemed to be University , IN
Sravani Vudumula
Structural Engineering,Department of Civil Engineering&Vignan deemed to be University , IN
Keywords: Geopolymer Concrete (GPC), Artificial Neural Networks (ANNs), Compressive Strength, Split Tensile Strength, Levenberg –Marquardt
Abstract
Concrete strength tests are generally calculated at 3 days to one year after discharging the concrete. Waiting period needed to conduct that test can delay the decision modelling construction progression and ignoring that type of test would limit the quality control survey in construction projects. Therefore such kind of determination of strength of the concrete becomes require that the quick and reliable for pre design of construction or quality check of construction. In this paper a try built to develop geopolymer concrete (GPC) of compressive and split tensile strength prediction models with help of Artificial Neural Networks (ANNs). The data analysis and model development were collected at 7-days and 28-days curing period via experiments in the laboratory. The developed
models have been also conducted on concrete results taken from the journals. A comparison for the calculated results acquired using the models and it can be accurate that the neural network model with their training function Levenberg-Marquardt (LM) for the prediction of compressive and tensile strength of GPC is the best prediction tool.
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