Application of Gene Expression Programming (GEP) for the Prediction of Compressive Strength of Geopolymer Concrete

Abstract

For the production of geopolymer concrete (GPC), fly-ash (FA) like waste material has been effectively utilized by various researchers. In this paper, the soft computing techniques known as gene expression programming (GEP) are executed to deliver an empirical equation to estimate the compressive strength of GPC made by employing FA. To build a model, a consistent, extensive and reliable data base is compiled through a detailed review of the published research. The compiled data set is comprised of 298 experimental results. The utmost dominant parameters are counted as explanatory variables, in other words, the extra water added as percent FA (), the percentage of plasticizer (), the initial curing temperature (), the age of the specimen (), the curing duration (), the fine aggregate to total aggregate ratio (), the percentage of total aggregate by volume (), the percent SiO2 solids to water ratio () in sodium silicate (Na2SiO3) solution, the NaOH solution molarity (), the activator or alkali to FA ratio (), the sodium oxide (Na2O) to water ratio () for preparing Na2SiO3 solution, and the Na2SiO3 to NaOH ratio (). A GEP empirical equation is proposed to estimate the of GPC made with FA. The accuracy, generalization, and prediction capability of the proposed model was evaluated by performing parametric analysis, applying statistical checks, and then compared with non-linear and linear regression equations.

A New Dual-Mode GEP Prediction Algorithm Based on Irregularity and Similar Period

“…where h refers to the number of hidden layer nodes, m refers to the number of input layer nodes, n refers to the number of output layer nodes, a is a constant and its range is [1,10]. In Table 2, logis and purelin refer activation functions.…”

“…Oulapour et al [9] used GEP to find the best equation for the relationship between the width and depth of the possible crack area and the geometric parameters of the valley cross section. Khan et al [10] used GEP to predict the compressive strength of geopolymer concrete. Yang et al [11] proposed a new spectral model for leaf area index estimation based on GEP.…”

Optimisation of GBFS, Fly Ash, and Nano-Silica Contents in Alkali-Activated Mortars

“…Accordingly, in recent years, numerous mathematical predictions and optimisation models have been developed within the research community, including artificial neural networks (ANNs) [ 25 , 26 , 27 ], metaheuristic algorithms [ 28 , 29 ], genetic expression programming (GEP) [ 30 , 31 , 32 , 33 ], adaptive neuro-fuzzy inference systems (ANFIS) [ 34 , 35 , 36 ], and response surface methodology (RSM) [ 37 , 38 , 39 ]. Among them, RSM is one of the best statistical techniques used for data optimization [ 40 ].…”

Random Forest Modeling for Fly Ash-Calcined Clay Geopolymer Composite Strength Detection

“…For split tensile strength the training dataset, MSE, RMSE, R 2 , and MAE values were 0.88, 0.25, 0.88, and 0.0256, respectively. Using the RFR technique, calculate the R 2 , MAE, and RMSE of the anticipated values [24]. This research could help engineers choose optimal supervised learning models and parameters for geopolymer concrete manufacturing.…”