Artificial Neural Network with a Cross-Validation Technique to Predict the Material Design of Eco-Friendly Engineered Geopolymer Composites

Abstract

A material-tailored special concrete composite that uses a synthetic fiber to make the concrete ductile and imposes strain-hardening characteristics with eco-friendly ingredients is known as an “engineered geopolymer composite (EGC)”. Mix design of special concrete is always tedious, particularly without standards. Researchers used several artificial intelligence tools to analyze and design the special concrete. This paper attempts to design the material EGC through an artificial neural network with a cross-validation technique to achieve the desired compressive and tensile strength. A database was formulated with seven mix-design influencing factors collected from the literature. The five best artificial neural network (ANN) models were trained and analyzed. A gradient descent momentum and adaptive learning rate backpropagation (GDX)–based ANN was developed to cross-validate those five best models. Upon regression analysis, ANN [2:16:16:7] model performed best, with 74% accuracy, whereas ANN [2:16:25:7] performed best in cross-validation, with 80% accuracy. The best individual outputs were “tacked-together” from the best five ANN models and were also analyzed, achieving accuracy up to 88%. It is suggested that when these seven mix-design influencing factors are involved, then ANN [2:16: 25:7] can be used to predict the mix which can be cross-verified with GDX-ANN [7:14:2] to ensure accuracy and, due to the few mix trials required, help design the SHGC with lower costs, less time, and fewer materials.

COVID-19 Diagnosis by Wavelet Entropy and Extreme Learning Machine

“…The original dataset is divided into k similarly sized subsets (folds) [45]. Each time the data model is trained, one of the folds is used as the validation set, and the other folds are used as the training set [46,47], and the performance value si of the trained data model is obtained after verification. In this way, the training iteration of the data model is performed k times [48], and each fold will be used as a validation set once and k-1 times as a training set [49].…”

Effect of Steel Fibers on the Interfacial Shear Strength of Flyash and GGBS based Geopolymer concrete activated with water glass

“…The worldwide cement industry releases more than 1.65 billion tonnes of greenhouse gases each year, signi cantly exacerbating global warming [2]. In order to mitigate CO2 emissions from the cement industry, Geopolymer binders have emerged as eco-friendly construction materials with the potential to entirely replace OPC [3,4]. Researchers have investigated the process by which y ash rich in aluminosilicate reacts with alkali to generate geopolymer, an inorganic polymer binder.…”

Machine Learning Approaches for Fault Detection in Renewable Microgrids

“…Machine learning algorithms, which have been taught on a wide range of datasets that include both normal and faulty situations, are capable of adjusting to changing circumstances and offering sophisticated fault detection skills. [6][7][8][9][10] Study Goals: The primary goal of this study is to accomplish a set of interrelated goals. Firstly, the objective is to create and use machine learning models for identifying faults in renewable microgrids.…”