Prediction of the Compressive Strength of Waste-Based Concretes Using Artificial Neural Network.

ABSTRACT: In the 21st century, numerous numerical calculation techniques have been discovered and used in several fields of science and technology. The purpose of this study was to use an artificial neural network (ANN) to forecast the compressive strength of waste-based concretes. The specimens studied include different kinds of mineral additions: metakaolin, silica fume, fly ash, limestone filler, marble waste, recycled aggregates, and ground granulated blast furnace slag. This method is based on the experimental results available for 1303 different mixtures gathered from 22 bibliographic sources for the ANN learning process. Based on a multilayer feedforward neural network model, the data were arranged and prepared to train and test the model. The model consists of 18 inputs following the type of

SUBMITTER: Amar M 

PROVIDER: S-EPMC9604846 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature