Prediction of the Compressive Strength of Fly Ash Geopolymer Concrete by an Optimised Neural Network Model

Abstract

This article presents a regression tool for predicting the compressive strength of fly ash (FA) geopolymer concrete based on a process of optimising the Matlab code of a feedforward layered neural network (FLNN). From the literature, 189 samples of different FA geopolymer concrete mix-designs were collected and analysed according to ten input variables (all relevant mix-design parameters) and the output variable (cylindrical compressive strength). The developed optimal FLNN model proved to be a powerful tool for predicting the compressive strength of FA geopolymer concrete with a small range of mean squared error (MSE = 10.4 and 15.0), a high correlation coefficient with the actual values (R = 96.0 and 97.5) and a relatively small root mean squared error (RMSE = 3.22 and 3.87 MPa) for the training and testing data, respectively. Based on the optimised model, a powerful design chart for determining the mix-design parameters of FA geopolymer concretes was generated. It is applicable for both one- and two-part geopolymer concretes, as it takes a wide range of mix-design parameters into account. The design chart (with its relatively small error) will ensure cost- and time-efficient geopolymer production in future applications.

Artificial Intelligence Models for Predicting the Compressive Strength of Geopolymer Cements

“…The ANN model accurately predicts absorption rate as a function of binder content, basal fiber content, compressive strength, and changes in concrete mass [40]. Furthermore, other studies reveal that fly ash-based geopolymers utilizing a developed Neural Network model algorithm are potent tools for predicting geopolymer compressive strength [41]. Machine learning techniques applied to predict concrete compressive strength based on cellulose nanofibers yield R 2 >0.72, MAPE ≤ 0.1, and MAE ≤ 5, aligning with the standard of R 2 values exceeding 0.60 [42].…”

Applicability of waste foundry sand stabilization by fly ash geopolymer under ambient curing conditions

“…Based on the pioneering literature [ 15 , 19 , 22 , 26 ] and previous experience [ 27 ], the alkaline activator solution (AAS) was fixed at probable optimum controlling factors: molarity of SH = 16 M, SS/SH = 2.0, and w/GPS = 0.2, where w/GPS is the water (w) to geopolymer-solid (GPS) ratio. The WFS was wet by the amount of water (w extra ) to reach the optimum moisture content (OMC).…”

Review of Recent Developments Regarding the Durability Performance of Eco-Friendly Geopolymer Concrete

“…The annual CO 2 emissions from OPC production are estimated to be around 4 billion tons [18,24]. In the context of energy consumption and the decarbonization process applied in the conversion of limestone, about 1 ton of CO 2 is released per ton of cement production [25][26][27][28][29][30]. The International Energy Agency (IEA) reported that these emissions account for 5-8% of total CO 2 emissions globally [31][32][33][34][35][36][37][38].…”