Compressive Strength Estimation of Fly Ash/Slag Based Green Concrete by Deploying Artificial Intelligence Models

Abstract

Cement production is one of the major sources of decomposition of carbonates leading to the emission of carbon dioxide. Researchers have proven that incorporating industrial wastes is of paramount significance for producing green concrete due to the benefits of reducing cement production. The compressive strength of concrete is an imperative parameter to consider when designing concrete structures. Considering high prediction capabilities, artificial intelligence models are widely used to estimate the compressive strength of concrete mixtures. A variety of artificial intelligence models have been developed in the literature; however, evaluation of the modeling procedure and accuracy of the existing models suggests developing such models that manifest the detailed evaluation of setting parameters on the performance of models and enhance the accuracy compared to the existing models. In this study, the computational capabilities of the adaptive neurofuzzy inference system (ANFIS), gene expression programming (GEP), and gradient boosting tree (GBT) were employed to investigate the optimum ratio of ground-granulated blast furnace slag (GGBFS) and fly ash (FA) to the binder content. The training process of GEP modeling revealed 200 chromosomes, 5 genes, and 12 head sizes as the best hyperparameters. Similarly, ANFIS hybrid subclustering modeling with aspect ratios of 0.5, 0.1, 7, and 150; learning rate; maximal depth; and number of trees yielded the best performance in the GBT model. The accuracy of the developed models suggests that the GBT model is superior to the GEP, ANFIS, and other models that exist in the literature. The trained models were validated using 40% of the experimental data along with parametric and sensitivity analysis as second level validation. The GBT model yielded correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE), equaling 0.95, 3.07 MPa, and 4.80 MPa for training, whereas, for validation, these values were recorded as 0.95, 3.16 MPa, and 4.85 MPa, respectively. The sensitivity analysis revealed that the aging of the concrete was the most influential parameter, followed by the addition of GGBFS. The effect of the contributing parameters was observed, as corroborated in the literature.

Prediction Models for Estimating Compressive Strength of Concrete Made of Manufactured Sand Using Gene Expression Programming Model

“…GEP has been successfully employed for tackling numerous complicated engineering challenges due to a number of its inherent advantages [ 41 ]. The GEP algorithm has been successfully used for a variety of concrete-related applications [ 42 , 43 , 44 , 45 , 46 , 47 ]. GEP uses a population-based technique, which is inspired by the traditional genetic algorithms (GAs) procedure for prediction and optimal solution finding.…”

“…Because of the intricacy of the output’s mathematical equation, the number of genes has a significant impact on the model’s performance. Previously, majority of researchers employed different of genes, i.e., 3 [ 49 ], 4 [ 42 ], and 5 [ 47 ]. Increasing the number of genes may boost performance, but it will also complicate the output’s mathematical equation, and thus the number of genes in this analysis ranged from 3 to 5.…”

Estimating Flexural Strength of FRP Reinforced Beam Using Artificial Neural Network and Random Forest Prediction Models

“…The previous researchers recommended the use of a variety of AI models for solving engineering problems [ 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ]. Modern engineering values numerical [ 42 , 43 , 44 ] and artificial intelligence (AI) models for solving complex and nonlinear problems.…”

Prediction of Rapid Chloride Penetration Resistance to Assess the Influence of Affecting Variables on Metakaolin-Based Concrete Using Gene Expression Programming

“…Following the same methodology, Saridemir [ 28 ] studied the influence of fly ash on the compressive strength of concrete. In addition to these AI models, a variety of other models, such as multi-layer neural network (MLNN) [ 29 , 30 ], extreme learning machine (ELM) [ 31 ], decision tree (DT) and gradient boosting tree (GBT) models [ 32 ] have been successfully used for modelling the compressive strength of concrete with different constituents. Similarly, Kumar et al [ 33 ] successfully employed a multivariate adaptive regression spline and minimax probability machine regression approach to study the influence of elevated temperature curing, fly ash and silica fumes on the RCPT value of self-compacting concrete.…”