1. Introduction
Fly ash (FA) is the unburned leftover residue from thermal coal plants [
1]. Which is transported by gases emitted from the burning zone in the boiler. FA is collected through mechanical or electrostatic separator [
2]. Annually around 375 million tons of FA is produced throughout the globe, with a disposal cost as high as
$20–
$40 per ton [
3]. It is dumped into landfills in sub-urban areas [
4]. However, dumping tons of FA exclusive of treatment sets off a malignant impact on the green environment [
5]. The hazardous materials in FA like silica, alumina, and oxides such as a ferric oxide (Fe
2O
3) are intervening factors in water, soil, and air pollution. This ultimately leads to health issues and different geo-environmental problems [
6,
7]. A good waste management employment is desirable for the sustainability of a safe environment [
8]. FA, if not properly disposed of, will affect the whole ecological cycle. Ultra-fine particles of FA act in the same way as poison when they reach the respiratory system. Consequently, causing physiological disorders and other related health issues like cancer, hepatic disorder, anemia, dermatitis, and gastroenteritis. FA pollutes surface and underground water which stresses aquatic life and causes skin diseases and diarrhea [
7].
Concrete is used worldwide as a construction material and is classified as the second most consumable material after water [
9,
10]. It is reported that for every human about three tons of concrete is produced [
11]. Around 25 billion tons of concrete is manufactured every year globally [
12]. According to current world stats, approximately 2.6 billion tons of cement is produced per year. This will rise by 25 percent in the next 10 years [
13]. However, the manufacturing of cement has an adverse effect on the environment. One ton of CO
2 is emitted into the air to produce one ton of cement. This creates an alarming situation for the environment. Limestone is the major resource of ordinary Portland cement. Severe limestone unavailability could occur in 25–50 years [
14,
15]. The worldwide construction industry consumes one-third of the entire resources and is liable for 30 percent CO
2 release globally. Thus, production of green concrete is important to reduce adverse environmental effects [
16,
17]. FA can be used as supplementary material in the cementitious matrix. It has been utilized by researchers to make green concrete [
18,
19,
20,
21]. FA utilization in the construction industry is a smart choice as it will reduce the usage of cement and the harmful effect of its disposal into landfills.
The utilization geopolymer concrete made of FA-like waste, is on the rise for the last two decades as lesser amounts of cement are used in geopolymer concrete (GPC) [
22,
23,
24,
25,
26]. FA has been used effectively in the construction industry but its application is still limited due to the anomalous behavior of FA [
27]. FA-dependent GPC is adopted extensively by builders. No method is available to estimate the mechanical properties of FA-based GPC based on a mix ratio with maximum variables. The mechanical properties of FA-based GPC critically depends on several parameters like 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 SiO
2 solids to water ratio (
) in sodium silicate (Na
2SiO
3) solution, the NaOH solution molarity (
), the activator or alkali to FA ratio (
), the sodium oxide (Na
2O) to water ratio (
) for preparing Na
2SiO
3 solution, and the Na
2SiO
3 to NaOH ratio (
) [
13,
28,
29,
30,
31,
32,
33,
34,
35]. This creates ambiguity in the prediction properties of GPC. Moreover, a rapid spike in the use of soft computing techniques to build an empirical model has been observed recently [
36,
37]. Gene expression programming (GEP) is one of the popular soft computing methods utilized by various researchers in several engineering perspectives. Actual GEP is inspired by the reproduction of DNA molecules at gene level [
38]. Tanyildizi et al. [
39] predicted different mechanical properties of lightweight concrete subjected to elevated temperature. The author projected two different GEP models with chromosome levels equal to 30, head size 8, and number of genes equal to 4. Multiplication and addition are the two different linking functions used. The execution time of the GEP depends on the chromosome level, which dictates the size of the population. Genetic operators help in the genetic variation of the chromosomes. The chromosome that delivers the best results is forwarded to subsequent generations and the process is repeated until the achievement of an acceptable fitness.
Recently, different researchers use the GEP for the estimation of various mechanical characteristics of different types of concrete. The researchers use experimental and literature-based data for the prediction of compressive strength of sugar cane bagasse ash (SCBA) concrete via GEP [
36]. Furthermore, the authors proposed a formula using GEP for estimating the axial capacity of concrete filled steel tube (CFST) with just 277 instances [
37]. Furthermore, Nour et al. [
40] worked with GEP algorithms for the estimation of compressive strength of CFST containing recycled aggregates.
2. Supervised Machine Learning Algorithms
Artificial neural networks (ANN), fuzzy logic, genetic algorithms (GA), and genetic programming (GP) use AI techniques built on natural tools. These methods have been used to resolve the problems of the pre-mix design of rubberized concrete and waste foundry sand concrete by training of the available data collected from the literature [
41,
42]. The configuration detection capabilities of the AI methods (support vector regression or ANN) lead to the generalization of complicated patterns. Therefore, they can be applied in the vast field of engineering [
43]. By employing such approaches, the presence of an enormous sum of hidden or concealed neurons often makes it impossible to establish accurate relations between the inputs and outcomes. ANN can be exercised for estimating the mechanical properties of concrete. Recently, Getahun et al. used ANN on 66 experimental datasets to estimate the compressive strength of rice husk ash-based concrete [
44]. While Mashhadban et al. predict the workability of self-compacting concrete using ANN [
45]. These models give a strong correlation with no empirical expression which can be practically used. This is because of the complexity of the ANN model structure which is considered as the main obstruction in the wide-scale implementation of the ANN approach [
46]. Multicollinearity is the hindrance in such methods [
47]. The updated ANN technique was likewise extended to assess silica fume concrete compressive strength (
) and elastic modulus (
) of concrete incorporates recycled aggregate. Because of the complexities of the relationship proposed, a devoted graphical interface was created for the model functional usage [
48].
A strong soft computing technique, namely, genetic programming (GP), is valuable as it ignores the previous forms of established relationships for the development of the model [
49,
50]. An extension of GP, namely, gene expression programming (GEP), which encodes a small program and uses fixed-length linear chromosomes, was recently introduced [
51]. GEP has an advantage in that a simple mathematical expression can represent the outcome that is appropriate for practicable usage of better predictive accuracy. It is currently exercised as a substitute to the common techniques of prediction [
52,
53,
54,
55,
56,
57,
58].
Compressive strength (
) is considered as the primary factor in designing and analyzing concrete [
59]. The researchers focused on the experimental route to estimate the
of FA dependent GPC [
60,
61]. To save time, cost, and to sustain fly-ash and cement for future use, the development of accurate and reliable expressions is needed to relate the mix design variables and
of GPC made with FA. A complete and thorough revision of the literature discloses that there are few empirical models for the estimation the
of FA based GPC [
41,
55,
58]. Though, the predictions of such empirical equations are confined to a specific dataset, for example, to the corresponding experimental study results. The prediction from such models is not viable and accurate outside the corresponding database file. Alkaroosh et al. [
62] developed an empirical equation to estimate the
of FA based GPC, based on 56 data points collected from previous research [
63]. In the proposed equation, no factor was used for making the sodium silicate solution. Their equation shows a pure linear relationship between the NaOH solution molarity and
for FA-based GPC. While other researchers reported a decrease in compressive strength by increasing the molarity of the NaOH solution [
64]. To fill the research gap, the GEP approach is employed to establish a generalized and more effective empirical equation for the estimation of
of FA-based GPC with a tolerable error. A detailed database has been developed from published research that incorporates cylindrical specimen of size 200 × 100 mm, height × diameter, and cubic specimens of size 150 mm and 100 mm. The comprehensive database accomplishment guarantees that the models are consistent and accessible for the data that is not exercised in the model’s establishment. The model’s performance is also verified by observation of the statistical errors, parametric analysis, sensitivity checks, and linear and non-linear regression methods.
5. Recommendations for Future Study
Fly-ash (FA)- based geopolymer concrete (GPC) has a great potential to be used in the construction industry, as a replacement of ordinary Portland cement (OPC) concrete. The data set used in this paper is limited to 298 samples. In fact, proper testing must be carried out by varying maximum explanatory variables for a more efficient predictive model. Although, this paper considers a wide range comprehensive data base consisting of ten explanatory parameters for modelling the compressive strength of geopolymer concrete made with wasted fly-ash.
Moreover, study of other mechanical characteristics of fly-ash based GPC like tensile strength, elastic modulus, poison ratio, and flexural strength, is highly necessary; at normal temperature as well as at elevated temperature. A new data base is also needed for the durability study of fly-ash-based GPC. Furthermore, it is recommended to predict the stated mechanical properties of fly-ash-based GPC via different artificial intelligence (AI) techniques, such as fuzzy logic, adaptive fuzzy interface system (ANFIS), response surface methodology (RSM), support vector machine (SVM) analysis, random forest regression (RFR), decision tree (DT), artificial neural network (ANN), recurrent neural network (RNN), convolutional neural network (CNN), M5P tree and restricted Boltzmann machine (RBM), et cetera. Furthermore, an extensive study related to the interaction of geopolymer concrete and reinforcing steel is needed. It would also be worthwhile formalizing the different mechanical properties of fiber reinforced geopolymer concrete.
Normally it is considered that the production cost of GPC is greater than OPC concrete. It can be reduced by the use of different types of waste materials such as sand replacement that are rich in alumina silicates; like the use of locally available waste foundry sand, glass waste, and marble wastes, et cetera. The authors replaced fine aggregates with waste foundry sand in GPC. They reported that the initial production cost of M50 grade GPC is 11% lower than OPC concrete [
108]. However, the M30 grades of GPC and OPC concrete have almost similar of production costs [
108]. Environmental safety delivered by GPC production from waste materials is worthwhile as it reduces the carbon-dioxide emission from the manufacture of cement and adds a carbon credit to the economy of the country as well. Comparing the overall cost, including the maintenance and durability, the cost of GPC is similar to OPC concrete as the geopolymer concrete is much more durable and resistive to chemical attacks than OPC concrete [
109]. The authors immersed GPC and OPC concrete in a magnesium sulfate solution for 45 days and reported that the reduction of compressive strength of GPC is 13% lower than OPC concrete [
109]. Additionally, the immersion for the same duration in a sulfuric acid solution resulted in 8% lower reduction of compressive strength of GPC as compared to OPC concrete [
109].
6. Conclusions
This research utilizes the gene expression programming technique (GEP) to establish an expression for the estimation of the compressive strength, , of geopolymer concrete (GPC) made with fly-ash. The projected GEP model is empirical and is built on the broadly distributed database, consisting of different parameters, that comes from the published literature. For the prediction of the of fly-ash-based GPC, highly prominent and influential parameters are considered as explanatory variables. The predicted model results satisfy the experimental results. From the parametric analysis, it has been shown that the projected model successfully encompasses the impact of the input parameters to predict the exact pattern of fly-ash-based GPC. The accurateness of the projected models is verified by the examination and assessment of statistical checks MAE, RSE, R, and RMSE and fitness functions () for training and validation samples. Furthermore, the model correctly meets the appropriate requirements considered for external validation. The comparison of the proposed model with the simple linear and non-linear equations shows that the GEP model possesses a higher generality and predictive capability and is appropriate to practice in the preliminary design of fly-ash-based GPC. Furthermore, before adding fly-ash as a geopolymer binder, it is suggested to perform a leachate analysis. The projected models can provide a detailed and practical foundation for increasing the use of toxic fly-ash for construction practices, instead of disposal in landfill sites. This would lead to effective and sustainable construction as green concrete is made by the incorporation of waste fly-ash that reduces the consumption of energy, emissions of greenhouse gases, disposal, and construction costs.