Skip to main content
PMC home page
Materials logoLink to Materials
. 2021 Feb 8;14(4):794. doi: 10.3390/ma14040794

Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

Ayaz Ahmad 1, Furqan Farooq 1,2,*, Pawel Niewiadomski 2, Krzysztof Ostrowski 3, Arslan Akbar 4,*, Fahid Aslam 5, Rayed Alyousef 5
Editor: Dario De Domenico
PMCID: PMC7915283  PMID: 33567526

Abstract

Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on the comparison of algorithms between individuals and ensemble approaches, such as bagging. Optimization for bagging is done by making 20 sub-models to depict the accurate one. Variables like cement content, fine and coarse aggregate, water, binder-to-water ratio, fly-ash, and superplasticizer are used for modeling. Model performance is evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual algorithms show a moderate bias result. However, the ensemble model gives a better result with R2 = 0.911 compared to the decision tree (DT) and gene expression programming (GEP). K-fold cross-validation confirms the model’s accuracy and is done by R2, MAE, MSE, and RMSE. Statistical checks reveal that the decision tree with ensemble provides 25%, 121%, and 49% enhancement for errors like MAE, MSE, and RMSE between the target and outcome response.

Keywords: concrete compressive strength, fly ash waste, ensemble modeling, decision tree, DT-bagging regression, cross-validation python

1. Introduction

Carbon dioxide produced from the cement industry has a malignant adamant effect on environmental conditions [1]. Its utilization and excessive use in modern construction around the world produces greenhouse gases (GHG) [2]. Moreover, countless amounts of gases are emitted during the production of cement due to the burning of natural resources and fossil fuels [3]. Annually, 4 billion tons of Portland cement (PC) is being produced and approximately one ton of cement generates one ton of CO2 gas [4]. This huge amount of carbon dioxide is a serious threat to the environment. The report shows that a 1.6% increment (3.4% to 5%) of global CO2 gas discharge was observed from the year 2000 to 2006. The cement industry contributes 18% of industrial greenhouse gases (GHG to the environment. This is due to the direct process-related activity, energy-related combustion, and remaining use of electricity, which is termed as indirect energy [5]. To overcome the above-mentioned issue, a process of replacing the cement material with an alternative binder is of great research interest [6].

The supplementary cementation materials (SCMs) can be used for many purposes, especially in the concrete industry. Their utilization in concrete gives a benignant effect by reducing the percentage of CO2 gas emitted. SCMs used in the cement industry can be industrial and agricultural waste products, which includes olive oil, bagasse ash, sugarcane, rice husk ash, palm oil fuel ash, etc. However, commonly adopted and used in the construction industry are silica fume, fly ash, and ground granulated blast furnace slag [7,8,9]. Their utilization in concrete reduces the malignant effect on the environment [10]. The replacement of cement in concrete with the waste material helps both in the utilization of the wastes and fulfills the increasing demand for the concrete. What is more, it has been observed that the use of waste materials as pozzolanic in high-strength concrete improves its strength and durability. This alternately helps minimize the impending environmental degradation [11].

Concrete is stated as the second-highest used material after water in the world [12]. This is due to the intensive use of concrete in the construction industries and the field of civil engineering. Concrete requires a comprehensive technique to produce. It is a mixture of multiple materials like coarse aggregate, fine aggregate, water, binder, admixtures, and supplementary raw materials [13]. The concrete matrix is the random distribution of the previously mentioned variables [14]. The extensive use of it can be seen as a building material around the globe. For the effective evaluation of the performance of concrete according to the advanced design technologies, its mechanical properties must be examined [14]. One of its supreme mechanical properties is its compressive strength, which is alternately the sign of structural safety throughout life [15]. This remarkable property of concrete can be affected by numerous factors, like particle size, water-to-cement ratio, waste composition, and use of chemicals. However, casting concrete by using the proper techniques in the laboratory and conducting experimental tests to find the mechanical properties after the setting is quite a time-consuming task [14]. Moreover, using the previously mentioned technique in the recent and modern period of life is quite uneconomical. Thus, the modern methodologies of machine learning techniques can be adapted to predict the desired result in advance [16]. The prediction of variables can be done from regressions and machine learning models. These algorithm-based techniques give a precise relation and predict the accurate model by the use of input variables [17].

Machine learning approaches are raising trends in the domain of civil engineering. They are extensively used in forecasting the mechanical properties of concrete [18,19,20,21]. These techniques use extensive data to build a precise model. Their prediction accuracy is dependent upon the data sample used in experimental work during casting of the specimen or upon the literature study. Researchers use these algorithms for the prediction of the mechanical properties of concrete. Javed et al. [22] predict the compressive strength of sugarcane bagasse ash (SCBA) concrete using gene expression programming (GEP). The author used the experimental test for calibration and validation of the model. Similarly, Aslam et al. [23] predict the compressive strength of high-strength concrete (HSC) by employing GEP. The author used 357 data points and reported an adamant relationship between the target and predicted values. Hosein et al. [24] forecast the mechanical properties of recycled concrete (REC) by using an artificial neural network (ANN). Correspondingly, Getahun et al. [25] forecast the strength of concrete incorporating waste materials using ANN. The author concluded that ANN gives adamant relation with fewer errors. Similarly, Qing et al. [26] predict the diffusion capability of chloride in reinforced concrete structures with ANN. The result indicates better prediction by employing an individual algorithm based on 653 data samples. Farooq et al. [15] predict the compressive nature of HSC by developing two models with random forest (RF) and GEP. RF gives a robust performance with precise correlation with strong predicted values. That machine learning algorithm is not limited to predict only the compressive or tensile nature of concrete but can be used to forecast any response in any engineering or data sciences domain. In turn, Ahmad et al. [27] employ supervised machine learning (ML) algorithms to predict energy in the distinct buildings. Similarly, Wang et al. [28] predict the COVID-19 response by employing different ML-based algorithms. Cladera et al. [29] predict the response of a structural beam with and without stirrups by using ANN. The author achieved a better response from modeled than empirical relations. Similarly, Onyari et al. [30] reveal robust performance by utilizing ANN to predict the flexural and compressive strength of modified mortar. Previously mentioned examples show the overwhelming response of individual algorithms.

Recently, application of ensemble modeling is perceived as a chance for enhancement of the model’s overall efficiency. It can be achieved due to taking a weak leaner to build strong, predictive learners than individual learners [31]. Feng et al. [32] use ensemble algorithm techniques for the prediction of failure mode classification and bearing capacity of reinforced concrete (RC) structural element (column). Both models give robust performance. However, bearing capacity is characterized by better correlation than failure mode classification. Bui et al. [33] employed a modified firefly algorithm with ANN on high performance concrete (HPC) and reported better performance of the model. Moreover, Salamai et al. [34] report good accuracy of R2 = 0.9867 by using the RF algorithm. In turn, Cai et al. [35] use various supervised machine ensemble algorithms for the prediction of chloride penetration in the RC structure situated in a marine environment. Ensemble models outclass individual algorithms to predict chloride penetration in RC. Hacer et al. [36] present the comparative assessment of bagging as the ensemble approach for high-performance concrete mix slump flow. Ensemble models with bagging were found to be superior with regard to standalone approaches. Halil et al. [37] predict the strength of HPC by employing three ensemble modeling approaches. The author used the decision tree as a base learner for other models and found that the hybrid model outperforms with the output result of R2 = 0.9368 among the several proposed models. Kermani et al. [38] represents the performance of five soft, computing base learners for predicting concrete corrosion in sewers. The author used both tree-based and network-based learners and reported that RF ensemble learners give a better result with R2 = 0.872. These ensemble approaches give an enhanced effect with robust performance of the overall models.

Taking the above into consideration, it may seem that ensemble learning models have more favorable features and give better results than individual learning models. The difference between individual and ensemble model is illustrated in Figure 1.

Figure 1.

Figure 1

Open in a new tab

Comparison between individual and ensemble approaches.

2. Research Significance

The aim of this study is to use the machine-learning algorithm with ensemble modeling using Anaconda Python to predict the compressive strength of fly-ash-based concrete using different algorithms. A decision tree with a bagging algorithm is used and optimization is done by making 20 sub-models to give a strong outcome. A comparison is made with the individual, ensemble algorithms, and with gene expression programming to give the best model. Moreover, K-fold cross-validation and a statistical check are applied to evaluate the model performance.

3. Data Description

The efficiency of the model is completely dependent upon the variables and the number of data samples used. The parameters used in models preparation in order to predict the strength of concrete were taken from published literature [39] and are summarized in Appendix A. Eight variables concerning composition of the concrete mixture and including cement, fine and coarse aggregate, superplasticizer, water, waste material, age, and a water-to-binder ratio were taken into analysis. The overall distribution in terms of the relative frequency distribution is illustrated in Figure 2. The range of variables of each parameter used in the study, with a minimum and maximum value, is illustrated in Figure 3. Statistical descriptive analysis for the variables in terms of strength is listed in Table 1.

Figure 2.

Figure 2

Open in a new tab

Relative frequency distribution of variables, (a) cement, (b) fine aggregate, (c) coarse aggregate, (d) fly ash, (e) superplasticizer, (f) age, (g) water, and (h) water-to-binder ratio.

Figure 3.

Figure 3

Open in a new tab

Parameters range of variables with minimum and maximum values.

Table 1.

Statistical measures on variables.

Statistics Cem * FASH * W * SP * CA * FA * Age * W/B *
Mean 361.39 28.15 184.15 3.68 996.90 775.93 53.31 0.53
Standard Error 5.20 2.94 1.17 0.36 4.70 4.86 4.63 0.01
Median 336.25 0.00 189.00 0.00 987.80 781.95 28.00 0.58
Mode 349.00 0.00 192.00 0.00 1125.00 613.00 28.00 0.58
Standard Deviation 85.49 48.35 19.29 5.95 77.26 79.92 76.06 0.11
Sample Variance 7309.14 2337.79 372.16 35.39 5969.32 6387.59 5784.50 0.01
Kurtosis −0.50 −0.44 0.29 3.52 −0.19 −0.07 7.01 −0.04
Skewness 0.83 1.20 −0.38 1.77 −0.26 −0.67 2.62 −0.92
Range 293.20 142.00 88.00 28.20 324.00 305.80 364.00 0.43
Minimum 246.80 0.00 140.00 0.00 801.00 594.00 1.00 0.27
Maximum 540.00 142.00 228.00 28.20 1125.00 899.80 365.00 0.70
Sum 97,574.60 7601.70 49,720.30 993.40 269,163.90 209,502.40 14,394.00 143.89
Count 270.00 270.00 270.00 270.00 270.00 270.00 270.00 270.00
Open in a new tab

* CEM = Cement (kg/m3), FASH = Fly ash (kg/m3), W = Water (kg/m3), SP = Super plasticizer (kg/m3), CA = Coarse aggregate (kg/m3), FA = Fine aggregate (kg/m3), and W/B = Water to binder.

4. Methodology

Individual and ensemble model techniques used to predict the properties in a limited time that are of great interest. The accuracy level between the actual and prediction level is typically obtained from the R2 value (ranges from 0–0.99). A high R2 value indicates the satisfactory results of the selected technique. This study uses three approaches to predict the compressive strength of concrete with waste material. A decision tree with ensemble algorithms such as bagging with a learning rate of 0.9 and gene expression programming is used. These techniques are selected due to their popularity among other algorithms. The overall machine learning model methodology in the form of a diagram is illustrated in Figure 4.

Figure 4.

Figure 4

Open in a new tab

Machine learning model methodology.

4.1. Decision Tree

The decision tree is one of the supervised learning techniques used for categorizing regression problems but is also commonly used for classification problems [40]. There are classes inside the tree. However, if there is no class, then the regression technique can predict the outcome by independent variables [37]. A decision tree is a tree-structured classifier in which the inner nodes reflect the attribute of a database. Branches indicate the conclusion rules, and every leaf node constitutes the outcome. The decision tree consists of two nodes known as a decision node and a leaf node. Decision nodes have multiple branches with the capability to make any decision, while leaf nodes do not have branches and are considered as the output of the decisions. It is known as a decision tree because it has a similar nature to a tree that starts with the root node and distributes in the number of branches, and reflects a tree-like structure [41]. The decision tree splits the data samples at various points. The executed algorithm finds the error between the target and predicted value at every divided point. The errors are calculated at every divided point, and the variable with the least value for the fitness function is selected as a split point, and the same procedure is repeated again.

4.2. Ensemble Bagging Approach

The ensemble technique is the concept of machine learning used to train numerous models by applying a similar learning algorithm [42]. The ensemble involves a substantial group of methods known as multi-classifiers. The group of hundreds or thousands of learners with a common intent are joined together to fix the problem. Bagging is a parallel type ensemble method that explains the variance of the prediction model by producing supplementary data in the stage of training. This production is from irregular sampling including substituting from the real set of data. Some of the observations can be repeated by sampling with replacement in every new training data set. In bagging, every component has an equal chance to appear in the new dataset. The force of prediction cannot be enhanced by increasing the size of the training set. The variance can also be reduced narrowly by tuning the forecast to an anticipated outcome. All these numbers of sets of the given data are normally used to train other numbers of models. This ensemble of different models uses the average of all the predictions from the other various models. In regression, the prediction may be the mean or average of the predictions taken from the different models [43]. The decision tree with bagging is tuned with 20 sub-models to obtain the optimized value that gives an adamant output result.

4.3. Gene Expression Programming

Gene expression programming (GEP) is a computer programming-based algorithm used to develop different models [44]. GEP, which is initially introduced by Ferreira [45], is considered to be a natural development of genetic programming (GP). Multiple numbers of genetic operators that are being used in genetic algorithms (GAs) can also be used in GEP with the help of a few recommended changes. There are five main components of GEP, namely, function set, terminal set, fitness function, control variables, and termination condition. GEP works as a fixed length of character twine to explain the problems, which are next defined as tree-like structures with different dimensions. This type of tree is known as the GEP expression tree (ETs). Selection of individual chromosomes takes place and then they are copied into the next generation, as per the fitness by roulette wheel sampling with elitism [23]. This ensures the durability and replication of the best individual to the next generation. Fluctuation in the population is shown by applying one or more genetic operators (mutation, crossover, or rotation) on the given chromosomes. Among the number of advantages of GEP, the formation of genetic diversity is remarkably simplified because of the working of genetic operators at the chromosome level. This multi-genic approach of GEP permits the natural selection of other complicated and complex programs composed of numerous subprograms. GEP genes along with a function set and terminal set play a vital role during the process [46].

4.4. K-Fold Cross-Validation and Statistical Measures

The model performance in terms of bias and variance is checked by employing K-fold cross-validation. The data is divided into 10 stratified groups, which randomly distribute the data into a training set and test set. This process takes one part of the overall data into the test sample and the remaining into the training set, as illustrated in Figure 5. The model’s overall efficiency by cross-validation is then tested by taking an average of 10 rounds by various errors. Similarly, the model evaluation is also done by using statistical indicators [23]. Three types of the indicator are used in our current study, which is listed below (Equations (1)–(3)).

MAE = 1n∑i = 1n|xi−x| (1)
MSE = 1n∑i = 1n(ypred−yref)2 (2)
RMSE = ∑(ypred−yref)2n (3)

where:

  • n = Total number of data samples,

  • x,yref = reference values in the data sample,

  • xi,ypred = predicted values from models.

Figure 5.

Figure 5

Open in a new tab

K-fold cross-validation algorithm [46].

5. Model Result

5.1. Decision Tree/Ensemble Model

The prediction of concrete strength by employing a decision tree yields an adamantly strong relationship between targets to output strength, as depicted in Figure 6. It can be seen that the individual model gives a better response with less variance, as illustrated in Figure 6a. However, the decision tree with bagging gives precise performance than an individual one, as illustrated in Figure 6d. This is due to an increase in model efficiency as it takes several data to train the best model by using weak base learners [47]. The ensemble model is optimized by making 20 sub-models, as depicted in Figure 6c. The zero number shows the individual model, which is made by using the decision approach and shows R2 = 0.812. After the ensemble approach, there is a significant enhancement in the overall response of the model. Every model shows a surpass effect by giving an average score of about R2 = 0.904 within 20 models. However, the 12th sub-model gives a prime result with R2 = 0.911, as depicted in Figure 6c. Moreover, the model comparison in terms of errors is depicted in Figure 6b,e. Decision tree (DT) with bagging enhances the model accuracy by giving fewer errors. The test data shows that there is a 20.10% prediction capacity of average errors by bagging than in the individual model. Besides, DT shows the minimum and maximum error of 0 and 21.97 MPa, respectively. Similarly, DT with an ensemble model shows the minimum and maximum error of 0.11, and 12.77 MPa, respectively. The detailed result is shown in Table 2.

Figure 6.

Figure 6

Open in a new tab

Decision tree (DT) with the ensemble model. (a) Predicted regression model with DT. (b) Model errors between targets and predictions from the DT technique. (c) Optimize model of ensemble. (d) Predicted regression model with DT-bagging. (e) Model Errors between targets and predictions from the DT-bagging technique.

Table 2.

Evaluation of models.

Data Points Decision Tree (DT) Targets DT Predictions Ensemble Prediction Gene Expression Programming (GEP) Targets GEP Predictions DT Errors Ensemble Errors GEP Errors
1 56.74 55.64 51.14 26.74 27.66 1.10 5.60 0.92
2 32.72 44.87 33.11 37.44 37.21 12.15 0.39 0.23
3 14.31 13.52 14.94 51.04 49.48 0.79 0.63 1.56
4 39.06 39.05 38.35 18.13 21.68 0.01 0.71 3.55
5 38.11 36.15 35.22 51.33 49.31 1.96 2.89 2.02
6 42.64 42.64 37.67 37.91 39.76 0.00 4.97 1.85
7 34.49 36.15 33.57 25.10 36.42 1.66 0.92 11.32
8 21.65 25.18 25.55 74.17 77.61 3.53 3.90 3.44
9 14.7 19.11 20.62 37.27 40.69 4.41 5.92 3.42
10 40.06 40.06 37.74 15.05 14.58 0.00 2.32 0.47
11 38.21 31.65 34.74 23.52 22.76 6.56 3.47 0.76
12 13.52 13.52 14.79 41.89 42.56 0.00 1.27 0.67
13 21.78 21.02 20.63 48.79 46.44 0.76 1.15 2.35
14 69.84 58.52 63.61 40.68 40.59 11.32 6.23 0.09
15 24 30.14 24.11 32.92 34.99 6.14 0.11 2.07
16 39.58 31.35 37.01 25.18 26.87 8.23 2.57 1.69
17 20.28 18.13 18.00 59.20 85.40 2.15 2.28 26.20
18 14.84 18.91 17.15 33.94 32.67 4.07 2.31 1.27
19 41.37 41.37 48.22 53.30 49.35 0.00 6.85 3.95
20 50.51 46.9 44.65 42.22 46.77 3.61 5.86 4.55
21 38.6 34.57 29.43 30.96 18.90 4.03 9.17 12.06
22 33.61 44.87 32.51 21.75 25.93 11.26 1.10 4.18
23 29.59 36.15 30.80 12.54 9.95 6.56 1.21 2.59
24 41.24 38.89 39.02 31.18 37.50 2.35 2.22 6.32
25 44.86 44.87 39.55 14.20 16.26 0.01 5.31 2.06
26 54.32 54.28 50.46 33.80 36.88 0.04 3.86 3.08
27 48.4 55.94 51.65 30.14 29.77 7.54 3.25 0.37
28 36.45 39 37.80 31.88 36.23 2.55 1.35 4.35
29 22.5 22.95 22.03 30.12 36.52 0.45 0.47 6.40
30 40.66 37.91 40.12 32.72 32.57 2.75 0.54 0.15
31 14.99 15.05 15.71 30.85 41.47 0.06 0.72 10.62
32 43.89 43.94 44.89 43.70 45.88 0.05 1.00 2.18
33 6.27 19.11 19.05 24.50 25.95 12.84 12.78 1.45
34 33.94 50.6 41.89 39.29 41.35 16.66 7.95 2.06
35 14.2 18.91 17.38 32.07 35.21 4.71 3.18 3.14
36 23.8 22.95 21.86 9.01 13.37 0.85 1.94 4.36
37 35.76 34.68 33.99 22.50 18.93 1.08 1.77 3.57
38 32.72 41.05 36.38 14.50 16.89 8.33 3.66 2.39
39 36.8 36.8 37.17 39.06 37.75 0.00 0.37 1.31
40 42.13 42.62 43.28 42.42 43.95 0.49 1.15 1.53
41 56.83 78.8 59.50 42.13 38.72 21.97 2.67 3.41
42 33.08 36.94 38.20 42.03 48.82 3.86 5.12 6.79
43 41.3 41.64 39.45 43.89 46.77 0.34 1.85 2.88
44 58.61 56.85 58.01 40.06 31.79 1.76 0.60 8.27
45 26.85 21.75 27.39 48.28 47.22 5.10 0.54 1.06
46 33.21 34.57 28.27 37.42 37.11 1.36 4.94 0.31
47 31.97 31.45 32.39 39.49 41.69 0.52 0.42 2.20
48 39.49 37.91 40.61 36.94 38.73 1.58 1.12 1.79
49 37.33 36.15 34.77 25.22 27.85 1.18 2.56 2.63
50 22.53 27.04 26.86 55.64 55.64 4.51 4.33 0.00
51 40.68 38.63 36.96 60.95 60.16 2.05 3.72 0.79
52 26.06 18.13 20.51 37.33 40.76 7.93 5.55 3.43
53 55.16 51.04 49.54 25.45 29.65 4.12 5.62 4.20
54 48.28 51.33 42.55 - - 3.05 5.73 -
Open in a new tab

5.2. Gene Expression Programming

The performance of the model by GEP yielded a robust relationship between targets and predicted, as illustrated in Figure 7. It can be seen that R2 by employing GEP is close to 1. Moreover, Figure 7b represents the error distribution of the testing set with fewer errors. Similarly, the predicted value shows a lower error to target values with a minimum, maximum, and average value of 0.00 MPa, 26.20 MPa, and 3.48 MPa, respectively. Table 2 presents detailed results from the models.

Figure 7.

Figure 7

Open in a new tab

Gene expression programming (GEP) model: (a) Predicted regression model. (b) Model Errors between targets and predictions from the GEP model.

5.3. Evaluation of the Model by K-Fold and Statistical Checks

Cross-validation is a statistical practice used to evaluate or estimate the actual performance of the machine learning models. It is necessary to know the performance of the selected models. For this purpose, a validation technique is required to find the accuracy level of the model’s data. Shuffling of the data set randomly and splitting a dataset into k-groups is required for the k-fold validation test. In the described study, data of experimental samples are equally divided into 10 subsets. It uses nine out of ten subsets, while the only subset is utilized for the validation of the model. The same approach of this process is then repeated 10 times for obtaining the average accuracy of these 10 repetitions. It is clarified widely that the 10-fold cross-validation method well represents the conclusion and accuracy of the model performance [48].

Bias and a variance decrease for the test set can be checked by employing K-fold cross-validation. The results of cross-validation are evaluated by a correlation coefficient (R2), a mean absolute error (MAE), a mean square error (MSE), and a root mean square error (RMSE), as illustrated in Figure 8. The ensemble model shows fewer errors and better R2 as compared to GEP. The average R2 for ensemble modeling is 0.905 with a maximum and minimum values of 0.84 and 0.96, as depicted in Figure 8a. Whereas the GEP model shows an average R2 = 0.873 of ten folds with 0.76 and 0.95 for a minimum and maximum correlation, respectively, as shown in Figure 8b. Each model shows fewer errors for validation. The validation indicator result shows that ensemble means values of MAE, MSE, and RMSE come to be 6.43 MPa, 6.66 MPa, and 2.55 MPa, respectively. Similarly, the GEP model shows the same trend by showing fewer errors. The GEP model shows mean values of 7.30 MPa, 9.60 MPa, and 3.06 MPa for MAE, MSE, and RMSE, respectively (see Figure 8b). Table 3 represents the validation results of both models.

Figure 8.

Figure 8

Open in a new tab

Statistical indicators from K-Fold Cross-validation; (a) Ensemble model; (b) GEP model.

Table 3.

Result of K-Fold Cross-validation.

K Fold Ensemble Model GEP Model
R2 MAE MSE RMSE R2 MAE MSE RMSE
1 0.96 8.46 4.45 2.10 0.86 10.71 13.57 3.68
2 0.91 5.17 7.44 2.72 0.94 7.45 7.97 2.82
3 0.84 3.73 8.54 2.92 0.89 6.18 11.24 3.35
4 0.90 9.52 5.84 2.41 0.95 5.84 14.51 3.80
5 0.94 6.81 6.44 2.53 0.93 7.81 9.64 3.10
6 0.90 5.65 5.88 2.42 0.86 7.51 6.51 2.55
7 0.85 7.91 6.87 2.62 0.81 8.47 7.25 2.69
8 0.88 5.81 9.85 3.13 0.76 6.58 7.58 2.75
9 0.95 6.37 4.97 2.22 0.84 5.64 9.47 3.07
10 0.92 4.92 6.35 2.51 0.89 6.84 8.35 2.88
Open in a new tab

Statistical check is also applied to evaluate the model with regard to the testing results. The statistical check is an indicator that shows the model response towards prediction, as shown in Table 4. It can be seen that models depict bottom-most errors. However, the ensemble model shows a 25% error reduction for MAE as compared to the individual and GEP. Similarly, the bagging approach indicates the robust performance of the model. Moreover, MSE and RMSE for strong learners show 121% and 49% enhancement in the predictions by showing reduced errors between the target and predicted outcomes, as shown in Table 4.

Table 4.

Statistical checks.

Statistics MAE (MPa) MSE (MPa) RMSE (MPa)
DT 3.896 36.01 6.00
DT-BAG 3.113 16.28 4.03
GEP 3.478 29.91 5.46
Open in a new tab

Moreover, permutation feature importance via python is conducted to check the influence of variables on strength, as depicted in Figure 9. These variables have a vital influence on the prediction of compressive strength of concrete. The concrete age, cement, and water-to-cement ratio have a significant influence on model analysis. Whereas water, filler material (fly ash), superplasticizer, fine aggregate, and coarse aggregate have moderate influences in making the model. Thus, it can be concluded that every parameter is crucial in the forecasting of the strength properties. However, cement, age, and the water-to-cement ratio should be given more importance while casting of specimens.

Figure 9.

Figure 9

Open in a new tab

Variable influence on compressive strength of fly ash-based concrete.

5.4. Limitation and Future Work

Despite the fact that, in the work, a thorough analysis based on a large number of data points was conducted and an extensive machine learning algorithm with evaluation was implemented, the limitations of work should be mentioned. Described in the paper selection, an approach can be enhanced by using other appropriate methods. A clear limitation of work is the number of data points equal to 270. The study is also limited to predict only one result from various mechanical properties of concrete. Tensile strength, durability, corrosion, toughness, and abrasion behavior of concrete is not considered in this work. Other algorithm-based techniques, like artificial neural network (ANN), support vector machine (SVM), gradient boosting, and AdaBoost may also be applied to the same dataset for a better understanding. However, this research work does not only focus on algorithm-based techniques but also involves the programming-based GEP, which indicated the wide scope of this work.

Since concrete is the most widely used material after water on this earth, it is further recommended that other properties of this material should be incorporated except for its compressive strength. Machine learning techniques should also be used to predict the environmental effects on concrete properties. To achieve high accuracy in the actual and predicted results, the multi-stage genetic programming approach may also be used. It is also recommended that models can be run for the concrete modified with different fibers as: jute fibers, glass fibers, polypropylene fibers, nylon fibers, and steel fibers.

6. Conclusions

This study describes the supervised machine learning approaches with ensemble modeling and gene expression programming to predict concrete strength. The following points are drawn from the analysis:

  1. A decision tree with ensemble modeling gives a robust performance compared to a decision tree individually and with gene expression programming. The correlation coefficient of R2 = 0.911 is reported for DT with bagging.

  2. Optimization of the model for the decision tree with bagging is done by making twenty sub-models. Magnificent enhancement is observed from the twelve, which shows R2 = 0.911 as compared to the individual model with R2 = 0.812.

  3. Validation score is conducted by different indicators. Both models (DT with bagging and GEP) show better anticipation for testing results.

  4. Statistical analysis checks reveal that the decision tree with bagging shows enhancement in model accuracy by minimizing the error difference between targeted and predicted values.

To summarize, all applied algorithms show a significant effect on the model’s quality by predicting the target response more accurately. As described in the paper, machine learning approaches can save experimental time and predict the outcome by gathering extensive data from laboratory and published papers. It can help the scientific society to predict the properties and responses in the coming month or year.

Appendix A

Table A1.

Experimental variable data.

S. No. Cement Fly Ash Water Super Plasticizer Coarse Aggregate Fine Aggregate Days W/C Strength
1 540 0 162 2.5 1040 676 28 0.3 79.99
2 540 0 162 2.5 1055 676 28 0.3 61.89
3 475 0 228 0 932 594 28 0.48 39.29
4 380 0 228 0 932 670 90 0.6 52.91
5 475 0 228 0 932 594 180 0.48 42.62
6 380 0 228 0 932 670 365 0.6 52.52
7 380 0 228 0 932 670 270 0.6 53.3
8 475 0 228 0 932 594 7 0.48 38.6
9 475 0 228 0 932 594 270 0.48 42.13
10 475 0 228 0 932 594 90 0.48 42.23
11 380 0 228 0 932 670 180 0.6 53.1
12 349 0 192 0 1047 806.9 3 0.55 15.05
13 475 0 228 0 932 594 365 0.48 41.93
14 310 0 192 0 971 850.6 3 0.62 9.87
15 485 0 146 0 1120 800 28 0.3 71.99
16 531.3 0 141.8 28.2 852.1 893.7 3 0.27 41.3
17 531.3 0 141.8 28.2 852.1 893.7 7 0.27 46.9
18 531.3 0 141.8 28.2 852.1 893.7 28 0.27 56.4
19 531.3 0 141.8 28.2 852.1 893.7 56 0.27 58.8
20 531.3 0 141.8 28.2 852.1 893.7 91 0.27 59.2
21 290.4 96.2 168.1 9.4 961.2 865 3 0.58 22.5
22 290.4 96.2 168.1 9.4 961.2 865 14 0.58 34.67
23 290.4 96.2 168.1 9.4 961.2 865 28 0.58 34.74
24 290.4 96.2 168.1 9.4 961.2 865 56 0.58 45.08
25 290.4 96.2 168.1 9.4 961.2 865 100 0.58 48.97
26 277.1 97.4 160.6 11.8 973.9 875.6 3 0.58 23.14
27 277.1 97.4 160.6 11.8 973.9 875.6 14 0.58 41.89
28 277.1 97.4 160.6 11.8 973.9 875.6 28 0.58 48.28
29 277.1 97.4 160.6 11.8 973.9 875.6 56 0.58 51.04
30 277.1 97.4 160.6 11.8 973.9 875.6 100 0.58 55.64
31 295.7 95.6 171.5 8.9 955.1 859.2 3 0.58 22.95
32 295.7 95.6 171.5 8.9 955.1 859.2 14 0.58 35.23
33 295.7 95.6 171.5 8.9 955.1 859.2 28 0.58 39.94
34 295.7 95.6 171.5 8.9 955.1 859.2 56 0.58 48.72
35 295.7 95.6 171.5 8.9 955.1 859.2 100 0.58 52.04
36 251.8 99.9 146.1 12.4 1006 899.8 3 0.58 21.02
37 251.8 99.9 146.1 12.4 1006 899.8 14 0.58 33.36
38 251.8 99.9 146.1 12.4 1006 899.8 28 0.58 33.94
39 251.8 99.9 146.1 12.4 1006 899.8 56 0.58 44.14
40 251.8 99.9 146.1 12.4 1006 899.8 100 0.58 45.37
41 249.1 98.8 158.1 12.8 987.8 889 3 0.63 15.36
42 249.1 98.8 158.1 12.8 987.8 889 14 0.63 28.68
43 249.1 98.8 158.1 12.8 987.8 889 28 0.63 30.85
44 249.1 98.8 158.1 12.8 987.8 889 56 0.63 42.03
45 249.1 98.8 158.1 12.8 987.8 889 100 0.63 51.06
46 252.3 98.8 146.3 14.2 987.8 889 3 0.58 21.78
47 252.3 98.8 146.3 14.2 987.8 889 14 0.58 42.29
48 252.3 98.8 146.3 14.2 987.8 889 28 0.58 50.6
49 252.3 98.8 146.3 14.2 987.8 889 56 0.58 55.83
50 252.3 98.8 146.3 14.2 987.8 889 100 0.58 60.95
51 246.8 125.1 143.3 12 1086.8 800.9 3 0.58 23.52
52 246.8 125.1 143.3 12 1086.8 800.9 14 0.58 42.22
53 246.8 125.1 143.3 12 1086.8 800.9 28 0.58 52.5
54 246.8 125.1 143.3 12 1086.8 800.9 56 0.58 60.32
55 246.8 125.1 143.3 12 1086.8 800.9 100 0.58 66.42
56 275.1 121.4 159.5 9.9 1053.6 777.5 3 0.58 23.8
57 275.1 121.4 159.5 9.9 1053.6 777.5 14 0.58 38.77
58 275.1 121.4 159.5 9.9 1053.6 777.5 28 0.58 51.33
59 275.1 121.4 159.5 9.9 1053.6 777.5 56 0.58 56.85
60 275.1 121.4 159.5 9.9 1053.6 777.5 100 0.58 58.61
61 297.2 117.5 174.8 9.5 1022.8 753.5 3 0.59 21.91
62 297.2 117.5 174.8 9.5 1022.8 753.5 14 0.59 36.99
63 297.2 117.5 174.8 9.5 1022.8 753.5 28 0.59 47.4
64 297.2 117.5 174.8 9.5 1022.8 753.5 56 0.59 51.96
65 297.2 117.5 174.8 9.5 1022.8 753.5 100 0.59 56.74
66 376 0 214.6 0 1003.5 762.4 3 0.57 16.28
67 376 0 214.6 0 1003.5 762.4 14 0.57 25.62
68 376 0 214.6 0 1003.5 762.4 28 0.57 31.97
69 376 0 214.6 0 1003.5 762.4 56 0.57 36.3
70 376 0 214.6 0 1003.5 762.4 100 0.57 43.06
71 500 0 140 4 966 853 28 0.28 67.57
72 475 59 142 1.9 1098 641 28 0.3 57.23
73 505 60 195 0 1030 630 28 0.39 64.02
74 451 0 165 11.3 1030 745 28 0.37 78.8
75 516 0 162 8.2 801 802 28 0.31 41.37
76 520 0 170 5.2 855 855 28 0.33 60.28
77 528 0 185 6.9 920 720 28 0.35 56.83
78 520 0 175 5.2 870 805 28 0.34 51.02
79 385 136 158 20 903 768 28 0.41 55.55
80 500.1 0 200 3 1124.4 613.2 28 0.4 44.13
81 405 0 175 0 1120 695 28 0.43 52.3
82 516 0 162 8.3 801 802 28 0.31 41.37
83 475 0 162 9.5 1044 662 28 0.34 58.52
84 500 0 151 9 1033 655 28 0.3 69.84
85 436 0 218 0 838.4 719.7 28 0.5 23.85
86 289 0 192 0 913.2 895.3 90 0.66 32.07
87 289 0 192 0 913.2 895.3 3 0.66 11.65
88 393 0 192 0 940.6 785.6 3 0.49 19.2
89 393 0 192 0 940.6 785.6 90 0.49 48.85
90 393 0 192 0 940.6 785.6 28 0.49 39.6
91 480 0 192 0 936.2 712.2 28 0.4 43.94
92 480 0 192 0 936.2 712.2 7 0.4 34.57
93 480 0 192 0 936.2 712.2 90 0.4 54.32
94 480 0 192 0 936.2 712.2 3 0.4 24.4
95 333 0 192 0 931.2 842.6 3 0.58 15.62
96 289 0 192 0 913.2 895.3 7 0.66 14.6
97 333 0 192 0 931.2 842.6 28 0.58 31.97
98 333 0 192 0 931.2 842.6 7 0.58 23.4
99 289 0 192 0 913.2 895.3 28 0.66 25.57
100 333 0 192 0 931.2 842.6 90 0.58 41.68
101 393 0 192 0 940.6 785.6 7 0.49 27.74
102 397 0 185.7 0 1040.6 734.3 28 0.47 33.08
103 382.5 0 185.7 0 1047.8 739.3 7 0.49 24.07
104 295.8 0 185.7 0 1091.4 769.3 7 0.63 14.84
105 397 0 185.7 0 1040.6 734.3 7 0.47 25.45
106 381.4 0 185.7 0 1104.6 784.3 28 0.49 22.49
107 295.8 0 185.7 0 1091.4 769.3 28 0.63 25.22
108 339.2 0 185.7 0 1069.2 754.3 7 0.55 21.18
109 381.4 0 185.7 0 1104.6 784.3 7 0.49 14.54
110 339.2 0 185.7 0 1069.2 754.3 28 0.55 31.9
111 382.5 0 185.7 0 1047.8 739.3 28 0.49 37.44
112 339 0 197 0 968 781 3 0.58 13.22
113 339 0 197 0 968 781 7 0.58 20.97
114 339 0 197 0 968 781 14 0.58 27.04
115 339 0 197 0 968 781 28 0.58 32.04
116 339 0 197 0 968 781 90 0.58 35.17
117 339 0 197 0 968 781 180 0.58 36.45
118 339 0 197 0 968 781 365 0.58 38.89
119 277 0 191 0 968 856 14 0.69 21.26
120 277 0 191 0 968 856 28 0.69 25.97
121 277 0 191 0 968 856 3 0.69 11.36
122 277 0 191 0 968 856 90 0.69 31.25
123 277 0 191 0 968 856 180 0.69 32.33
124 277 0 191 0 968 856 360 0.69 33.7
125 307 0 193 0 968 812 180 0.63 34.49
126 307 0 193 0 968 812 365 0.63 36.15
127 307 0 193 0 968 812 3 0.63 12.54
128 307 0 193 0 968 812 28 0.63 27.53
129 307 0 193 0 968 812 90 0.63 32.92
130 325 0 184 0 1063 783 7 0.57 17.54
131 325 0 184 0 1063 783 28 0.57 30.57
132 275 0 183 0 1088 808 7 0.67 14.2
133 275 0 183 0 1088 808 28 0.67 24.5
134 300 0 184 0 1075 795 7 0.61 15.58
135 300 0 184 0 1075 795 28 0.61 26.85
136 375 0 186 0 1038 758 7 0.5 26.06
137 375 0 186 0 1038 758 28 0.5 38.21
138 400 0 187 0 1025 745 28 0.47 43.7
139 400 0 187 0 1025 745 7 0.47 30.14
140 350 0 186 0 1050 770 7 0.53 20.28
141 350 0 186 0 1050 770 28 0.53 34.29
142 310 0 192 0 1012 830 3 0.62 11.85
143 310 0 192 0 1012 830 7 0.62 17.24
144 310 0 192 0 1012 830 28 0.62 27.83
145 310 0 192 0 1012 830 90 0.62 35.76
146 310 0 192 0 1012 830 120 0.62 38.7
147 331 0 192 0 1025 821 3 0.58 14.31
148 331 0 192 0 1025 821 7 0.58 17.44
149 331 0 192 0 1025 821 28 0.58 31.74
150 331 0 192 0 1025 821 90 0.58 37.91
151 331 0 192 0 1025 821 120 0.58 39.38
152 349 0 192 0 1056 809 3 0.55 15.87
153 349 0 192 0 1056 809 7 0.55 9.01
154 349 0 192 0 1056 809 28 0.55 33.61
155 349 0 192 0 1056 809 90 0.55 40.66
156 349 0 192 0 1056 809 120 0.55 40.86
157 296 0 186 0 1090 769 7 0.63 18.91
158 296 0 186 0 1090 769 28 0.63 25.18
159 297 0 186 0 1040 734 7 0.63 30.96
160 480 0 192 0 936 721 28 0.4 43.89
161 480 0 192 0 936 721 90 0.4 54.28
162 397 0 186 0 1040 734 28 0.47 36.94
163 281 0 186 0 1104 774 7 0.66 14.5
164 281 0 185 0 1104 774 28 0.66 22.44
165 500 0 200 0 1125 613 1 0.4 12.64
166 500 0 200 0 1125 613 3 0.4 26.06
167 500 0 200 0 1125 613 7 0.4 33.21
168 500 0 200 0 1125 613 14 0.4 36.94
169 500 0 200 0 1125 613 28 0.4 44.09
170 540 0 173 0 1125 613 7 0.32 52.61
171 540 0 173 0 1125 613 14 0.32 59.76
172 540 0 173 0 1125 613 28 0.32 67.31
173 540 0 173 0 1125 613 90 0.32 69.66
174 540 0 173 0 1125 613 180 0.32 71.62
175 540 0 173 0 1125 613 270 0.32 74.17
176 350 0 203 0 974 775 7 0.58 18.13
177 350 0 203 0 974 775 14 0.58 22.53
178 350 0 203 0 974 775 28 0.58 27.34
179 350 0 203 0 974 775 56 0.58 29.98
180 350 0 203 0 974 775 90 0.58 31.35
181 350 0 203 0 974 775 180 0.58 32.72
182 385 0 186 0 966 763 1 0.48 6.27
183 385 0 186 0 966 763 3 0.48 14.7
184 385 0 186 0 966 763 7 0.48 23.22
185 385 0 186 0 966 763 14 0.48 27.92
186 385 0 186 0 966 763 28 0.48 31.35
187 331 0 192 0 978 825 180 0.58 39
188 331 0 192 0 978 825 360 0.58 41.24
189 349 0 192 0 1047 806 3 0.55 14.99
190 331 0 192 0 978 825 3 0.58 13.52
191 382 0 186 0 1047 739 7 0.49 24
192 382 0 186 0 1047 739 28 0.49 37.42
193 382 0 186 0 1111 784 7 0.49 11.47
194 281 0 186 0 1104 774 28 0.66 22.44
195 339 0 185 0 1069 754 7 0.55 21.16
196 339 0 185 0 1069 754 28 0.55 31.84
197 295 0 185 0 1069 769 7 0.63 14.8
198 295 0 185 0 1069 769 28 0.63 25.18
199 296 0 192 0 1085 765 7 0.65 14.2
200 296 0 192 0 1085 765 28 0.65 21.65
201 296 0 192 0 1085 765 90 0.65 29.39
202 331 0 192 0 879 825 3 0.58 13.52
203 331 0 192 0 978 825 7 0.58 16.26
204 331 0 192 0 978 825 28 0.58 31.45
205 331 0 192 0 978 825 90 0.58 37.23
206 349 0 192 0 1047 806 7 0.55 18.13
207 349 0 192 0 1047 806 28 0.55 32.72
208 349 0 192 0 1047 806 90 0.55 39.49
209 349 0 192 0 1047 806 180 0.55 41.05
210 349 0 192 0 1047 806 360 0.55 42.13
211 302 0 203 0 974 817 14 0.67 18.13
212 302 0 203 0 974 817 180 0.67 26.74
213 525 0 189 0 1125 613 180 0.36 61.92
214 500 0 200 0 1125 613 90 0.4 47.22
215 500 0 200 0 1125 613 180 0.4 51.04
216 500 0 200 0 1125 613 270 0.4 55.16
217 540 0 173 0 1125 613 3 0.32 41.64
218 339 0 185 0 1060 754 28 0.55 31.65
219 393 0 192 0 940 758 3 0.49 19.11
220 393 0 192 0 940 758 28 0.49 39.58
221 393 0 192 0 940 758 90 0.49 48.79
222 382 0 185 0 1047 739 7 0.48 24
223 382 0 185 0 1047 739 28 0.48 37.42
224 310 0 192 0 970 850 7 0.62 14.99
225 310 0 192 0 970 850 28 0.62 27.92
226 310 0 192 0 970 850 90 0.62 34.68
227 310 0 192 0 970 850 180 0.62 37.33
228 310 0 192 0 970 850 360 0.62 38.11
229 525 0 189 0 1125 613 3 0.36 33.8
230 525 0 189 0 1125 613 7 0.36 42.42
231 525 0 189 0 1125 613 14 0.36 48.4
232 525 0 189 0 1125 613 28 0.36 55.94
233 525 0 189 0 1125 613 90 0.36 58.78
234 525 0 189 0 1125 613 270 0.36 67.11
235 322 0 203 0 974 800 14 0.63 20.77
236 322 0 203 0 974 800 28 0.63 25.18
237 322 0 203 0 974 800 180 0.63 29.59
238 302 0 203 0 974 817 28 0.67 21.75
239 397 0 185 0 1040 734 28 0.47 39.09
240 480 0 192 0 936 721 3 0.4 24.39
241 522 0 146 0 896 896 7 0.28 50.51
242 522 0 146 0 896 896 28 0.28 74.99
243 374 0 190 7 1013 730 28 0.51 39.05
244 305 100 196 10 959 705 28 0.64 30.12
245 298 107 186 6 879 815 28 0.62 42.64
246 318 126 210 6 861 737 28 0.66 40.06
247 356 142 193 11 801 778 28 0.54 40.87
248 314 113 170 10 925 783 28 0.54 38.46
249 321 128 182 11 870 780 28 0.57 37.26
250 298 107 210 11 880 744 28 0.7 31.87
251 322 116 196 10 818 813 28 0.61 31.18
252 313 113 178 8 1002 689 28 0.57 36.8
253 326 138 199 11 801 792 28 0.61 40.68
254 336 0 182 3 986 817 28 0.54 44.86
255 298 107 164 13 953 784 28 0.55 35.86
256 313 0 178 8 1000 822 28 0.57 25.1
257 313.3 113 178.5 8 1001.9 688.7 28 0.57 36.8
258 326.5 137.9 199 10.8 801.1 792.5 28 0.61 38.63
259 336.5 0 181.9 3.4 985.8 816.8 28 0.54 44.87
260 298.1 107.5 163.6 12.8 953.2 784 28 0.55 35.87
261 312.7 0 178.1 8 999.7 822.2 28 0.57 25.1
262 374.3 0 190.2 6.7 1013.2 730.4 28 0.51 39.06
263 304.8 99.6 196 9.8 959.4 705.2 28 0.64 30.12
264 298.1 107 186.4 6.1 879 815.2 28 0.63 42.64
265 317.9 126.5 209.7 5.7 860.5 736.6 28 0.66 40.06
266 355.9 141.6 193.3 11 801.4 778.4 28 0.54 40.87
267 313.8 112.6 169.9 10.1 925.3 782.9 28 0.54 38.46
268 321.4 127.9 182.5 11.5 870.1 779.7 28 0.57 37.27
269 298.2 107 209.7 11.1 879.6 744.2 28 0.7 31.88
270 322.2 115.6 196 10.4 817.9 813.4 28 0.61 31.18
Open in a new tab

Author Contributions

A.A. (Ayaz Ahmad)—conceptualization, modeling, and write up. F.F.—visualization and review. P.N.—writing, validation and supervision. K.O.—funding and review. A.A. (Arslan Akbar)—review, editing and visualization. F.A. and R.A.—data acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

The APC was funded by Wroclaw University of Science and Technology.

Data Availability Statement

The data presented in this article is available within the article.

Conflicts of Interest

The authors declared no conflict of interest.

Footnotes

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Zhang N., Duan H., Miller T.R., Tam V.W.Y., Liu G., Zuo J. Mitigation of carbon dioxide by accelerated sequestration in concrete debris. Renew. Sustain. Energy Rev. 2020;117:109495. doi: 10.1016/j.rser.2019.109495. [DOI] [Google Scholar]
  • 2.Monkman S., MacDonald M. On carbon dioxide utilization as a means to improve the sustainability of ready-mixed concrete. J. Clean. Prod. 2017;167:365–375. doi: 10.1016/j.jclepro.2017.08.194. [DOI] [Google Scholar]
  • 3.Qian X., Wang J., Fang Y., Wang L. Carbon dioxide as an admixture for better performance of OPC-based concrete. J. CO2 Util. 2018;25:31–38. doi: 10.1016/j.jcou.2018.03.007. [DOI] [Google Scholar]
  • 4.Akbar A., Liew K.M. Assessing recycling potential of carbon fiber reinforced plastic waste in production of eco-efficient cement-based materials. J. Clean. Prod. 2020;274:123001. doi: 10.1016/j.jclepro.2020.123001. [DOI] [Google Scholar]
  • 5.Jahanzaib Khalil M., Aslam M., Ahmad S. Utilization of sugarcane bagasse ash as cement replacement for the production of sustainable concrete—A review. Constr. Build. Mater. 2020:121371. doi: 10.1016/j.conbuildmat.2020.121371. [DOI] [Google Scholar]
  • 6.Wang L., Chen L., Tsang D.C.W., Guo B., Yang J., Shen Z., Hou D., Ok Y.S., Poon C.S. Biochar as green additives in cement-based composites with carbon dioxide curing. J. Clean. Prod. 2020;258:120678. doi: 10.1016/j.jclepro.2020.120678. [DOI] [Google Scholar]
  • 7.Kang S., Lloyd Z., Kim T., Ley M.T. Predicting the compressive strength of fly ash concrete with the Particle Model. Cem. Concr. Res. 2020;137:106218. doi: 10.1016/j.cemconres.2020.106218. [DOI] [Google Scholar]
  • 8.Farooq F., Akbar A., Khushnood R.A., Muhammad W.L.B., Rehman S.K.U., Javed M.F. Experimental investigation of hybrid carbon nanotubes and graphite nanoplatelets on rheology, shrinkage, mechanical, and microstructure of SCCM. Materials. 2020;13:230. doi: 10.3390/ma13010230. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Akbar A., Farooq F., Shafique M., Aslam F., Alyousef R., Alabduljabbar H. Sugarcane bagasse ash-based engineered geopolymer mortar incorporating propylene fibers. J. Build. Eng. 2021;33:101492. doi: 10.1016/j.jobe.2020.101492. [DOI] [Google Scholar]
  • 10.Ghouleh Z., Shao Y. Turning municipal solid waste incineration into a cleaner cement production. J. Clean. Prod. 2018;195:268–279. doi: 10.1016/j.jclepro.2018.05.209. [DOI] [Google Scholar]
  • 11.Lee M.G., Kang D., Jo H., Park J. Carbon dioxide utilization with carbonation using industrial waste-desulfurization gypsum and waste concrete. J. Mater. Cycles Waste Manag. 2016;18:407–412. doi: 10.1007/s10163-015-0461-0. [DOI] [Google Scholar]
  • 12.Possan E., Thomaz W.A., Aleandri G.A., Felix E.F., dos Santos A.C.P. CO2 uptake potential due to concrete carbonation: A case study. Case Stud. Constr. Mater. 2017;6:147–161. doi: 10.1016/j.cscm.2017.01.007. [DOI] [Google Scholar]
  • 13.Farooq F., Ahmed W., Akbar A., Aslam F., Alyousef R. Predictive modelling for sustainable high-performance concrete from industrial wastes: A comparison and optimization of models using ensemble learners. J. Clean. Prod. 2021;292:126032. doi: 10.1016/j.jclepro.2021.126032. [DOI] [Google Scholar]
  • 14.Bin Khairul anuar M.A.R., Ngamkhanong C., Wu Y., Kaewunruen S. Recycled Aggregates Concrete Compressive Strength Prediction Using Artificial Neural Networks (ANNs) Infrastructures. 2021;6:17. doi: 10.3390/infrastructures6020017. [DOI] [Google Scholar]
  • 15.Farooq F., Amin M.N., Khan K., Sadiq M.R., Javed M.F., Aslam F., Alyousef R. A comparative study of random forest and genetic engineering programming for the prediction of compressive strength of high strength concrete (HSC) Appl. Sci. 2020;10:7330. doi: 10.3390/app10207330. [DOI] [Google Scholar]
  • 16.Javed M.F., Farooq F., Memon S.A., Akbar A., Khan M.A., Aslam F., Alyousef R., Alabduljabbar H., Rehman S.K.U., Ur Rehman S.K., et al. New Prediction Model for the Ultimate Axial Capacity of Concrete-Filled Steel Tubes: An Evolutionary Approach. Crystals. 2020;10:741. doi: 10.3390/cryst10090741. [DOI] [Google Scholar]
  • 17.Iqbal M.F., Liu Q.F., Azim I., Zhu X., Yang J., Javed M.F., Rauf M. Prediction of mechanical properties of green concrete incorporating waste foundry sand based on gene expression programming. J. Hazard. Mater. 2020;384:121322. doi: 10.1016/j.jhazmat.2019.121322. [DOI] [PubMed] [Google Scholar]
  • 18.De Domenico D., Ricciardi G. Shear strength of RC beams with stirrups using an improved Eurocode 2 truss model with two variable-inclination compression struts. Eng. Struct. 2019;198 doi: 10.1016/j.engstruct.2019.109359. [DOI] [Google Scholar]
  • 19.Sadowski Ł., Nikoo M., Nikoo M. Concrete compressive strength prediction using the imperialist competitive algorithm. Comput. Concr. 2018;22:355–363. doi: 10.12989/cac.2018.22.4.355. [DOI] [Google Scholar]
  • 20.Czarnecki S., Shariq M., Nikoo M., Sadowski Ł. An intelligent model for the prediction of the compressive strength of cementitious composites with ground granulated blast furnace slag based on ultrasonic pulse velocity measurements. Measurement. 2021:108951. doi: 10.1016/j.measurement.2020.108951. [DOI] [Google Scholar]
  • 21.Sadowski Ł., Piechówka-Mielnik M., Widziszowski T., Gardynik A., Mackiewicz S. Hybrid ultrasonic-neural prediction of the compressive strength of environmentally friendly concrete screeds with high volume of waste quartz mineral dust. J. Clean. Prod. 2019;212:727–740. doi: 10.1016/j.jclepro.2018.12.059. [DOI] [Google Scholar]
  • 22.Javed M.F., Amin M.N., Shah M.I., Khan K., Iftikhar B., Farooq F., Aslam F., Alyousef R., Alabduljabbar H. Applications of gene expression programming and regression techniques for estimating compressive strength of bagasse ash based concrete. Crystals. 2020;10:737. doi: 10.3390/cryst10090737. [DOI] [Google Scholar]
  • 23.Aslam F., Farooq F., Amin M.N., Khan K., Waheed A., Akbar A., Javed M.F., Alyousef R., Alabdulijabbar H. Applications of Gene Expression Programming for Estimating Compressive Strength of High-Strength Concrete. Adv. Civ. Eng. 2020 doi: 10.1155/2020/8850535. [DOI] [Google Scholar]
  • 24.Naderpour H., Rafiean A.H., Fakharian P. Compressive strength prediction of environmentally friendly concrete using artificial neural networks. J. Build. Eng. 2018;16:213–219. doi: 10.1016/j.jobe.2018.01.007. [DOI] [Google Scholar]
  • 25.Getahun M.A., Shitote S.M., Abiero Gariy Z.C. Artificial neural network based modelling approach for strength prediction of concrete incorporating agricultural and construction wastes. Constr. Build. Mater. 2018;190:517–525. doi: 10.1016/j.conbuildmat.2018.09.097. [DOI] [Google Scholar]
  • 26.Liu Q., Iqbal M.F., Yang J., Lu X., Zhang P., Rauf M. Prediction of chloride diffusivity in concrete using artificial neural network: Modelling and performance evaluation. Constr. Build. Mater. 2020;268:121082. doi: 10.1016/j.conbuildmat.2020.121082. [DOI] [Google Scholar]
  • 27.Ahmad T., Chen H., Huang R., Yabin G., Wang J., Shair J., Azeem Akram H.M., Hassnain Mohsan S.A., Kazim M. Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment. Energy. 2018;158:17–32. doi: 10.1016/j.energy.2018.05.169. [DOI] [Google Scholar]
  • 28.Wang P., Zheng X., Li J., Zhu B. Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics. Chaos Solitons Fractals. 2020;139:110058. doi: 10.1016/j.chaos.2020.110058. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Cladera A., Marí A.R. Shear design procedure for reinforced normal and high-strength concrete beams using artificial neural networks. Part I: Beams without stirrups. Eng. Struct. 2004;26:917–926. doi: 10.1016/j.engstruct.2004.02.010. [DOI] [Google Scholar]
  • 30.Onyari E.K., Ikotun B.D. Prediction of compressive and flexural strengths of a modified zeolite additive mortar using artificial neural network. Constr. Build. Mater. 2018;187:1232–1241. doi: 10.1016/j.conbuildmat.2018.08.079. [DOI] [Google Scholar]
  • 31.Feng D.C., Liu Z.T., Wang X.D., Chen Y., Chang J.Q., Wei D.F., Jiang Z.M. Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach. Constr. Build. Mater. 2020;230:117000. doi: 10.1016/j.conbuildmat.2019.117000. [DOI] [Google Scholar]
  • 32.Feng D.C., Liu Z.T., Wang X.D., Jiang Z.M., Liang S.X. Failure mode classification and bearing capacity prediction for reinforced concrete columns based on ensemble machine learning algorithm. Adv. Eng. Inform. 2020;45 doi: 10.1016/j.aei.2020.101126. [DOI] [Google Scholar]
  • 33.Bui D.K., Nguyen T., Chou J.S., Nguyen-Xuan H., Ngo T.D. A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete. Constr. Build. Mater. 2018;180:320–333. doi: 10.1016/j.conbuildmat.2018.05.201. [DOI] [Google Scholar]
  • 34.Salami B.A., Rahman S.M., Oyehan T.A., Maslehuddin M., Al Dulaijan S.U. Ensemble machine learning model for corrosion initiation time estimation of embedded steel reinforced self-compacting concrete. Meas. J. Int. Meas. Confed. 2020;165:108141. doi: 10.1016/j.measurement.2020.108141. [DOI] [Google Scholar]
  • 35.Cai R., Han T., Liao W., Huang J., Li D., Kumar A., Ma H. Prediction of surface chloride concentration of marine concrete using ensemble machine learning. Cem. Concr. Res. 2020;136:106164. doi: 10.1016/j.cemconres.2020.106164. [DOI] [Google Scholar]
  • 36.Aydogmus H.Y., Erdal H.I., Karakurt O., Namli E., Turkan Y.S., Erdal H. A comparative assessment of bagging ensemble models for modeling concrete slump flow. Comput. Concr. 2015;16:741–757. doi: 10.12989/cac.2015.16.5.741. [DOI] [Google Scholar]
  • 37.Erdal H.I. Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction. Eng. Appl. Artif. Intell. 2013;26:1689–1697. doi: 10.1016/j.engappai.2013.03.014. [DOI] [Google Scholar]
  • 38.Zounemat-Kermani M., Stephan D., Barjenbruch M., Hinkelmann R. Ensemble data mining modeling in corrosion of concrete sewer: A comparative study of network-based (MLPNN & RBFNN) and tree-based (RF, CHAID, & CART) models. Adv. Eng. Inform. 2020;43:101030. doi: 10.1016/j.aei.2019.101030. [DOI] [Google Scholar]
  • 39.Lichman M. UCI Machine Learning Repository. University of California, School of Information and Computer Science; Irvine, CA, USA: 2013. [(accessed on 27 November 2020)]. Available online: http://archive.ics.uci.edu/ml. [Google Scholar]
  • 40.Karbassi A., Mohebi B., Rezaee S., Lestuzzi P. Damage prediction for regular reinforced concrete buildings using the decision tree algorithm. Comput. Struct. 2014;130:46–56. doi: 10.1016/j.compstruc.2013.10.006. [DOI] [Google Scholar]
  • 41.Ben Chaabene W., Flah M., Nehdi M.L. Machine learning prediction of mechanical properties of concrete: Critical review. Constr. Build. Mater. 2020;260:119889. doi: 10.1016/j.conbuildmat.2020.119889. [DOI] [Google Scholar]
  • 42.Ribeiro M.H.D.M., dos Santos Coelho L. Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series. Appl. Soft Comput. J. 2020;86:105837. doi: 10.1016/j.asoc.2019.105837. [DOI] [Google Scholar]
  • 43.Dou J., Yunus A.P., Bui D.T., Merghadi A., Sahana M., Zhu Z., Chen C.W., Han Z., Pham B.T. Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan. Landslides. 2020;17:641–658. doi: 10.1007/s10346-019-01286-5. [DOI] [Google Scholar]
  • 44.Kaboli S.H.A., Fallahpour A., Selvaraj J., Rahim N.A. Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming. Energy. 2017;126:144–164. doi: 10.1016/j.energy.2017.03.009. [DOI] [Google Scholar]
  • 45.Ferreira C. Soft Computing and Industry. Springer; London, UK: 2002. Gene Expression Programming in Problem Solving; pp. 635–653. [Google Scholar]
  • 46.Shah M.I., Javed M.F., Abunama T. Proposed formulation of surface water quality and modelling using gene expression, machine learning, and regression techniques. Environ. Sci. Pollut. Res. 2020:1–19. doi: 10.1007/s11356-020-11490-9. [DOI] [PubMed] [Google Scholar]
  • 47.Khan M.A., Memon S.A., Farooq F., Javed M.F., Aslam F., Alyousef R. Compressive Strength of Fly-Ash-Based Geopolymer Concrete by Gene Expression Programming and Random Forest. Adv. Civ. Eng. 2021;2021:1–17. doi: 10.1155/2021/6618407. [DOI] [Google Scholar]
  • 48.Kohavi R. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection; Proceedings of the International Joint Conference of Artificial Intelligence; Montreal, QC, Canada. 20–25 August 1995. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data presented in this article is available within the article.

Articles from Materials are provided here courtesy of Multidisciplinary Digital Publishing Institute (MDPI)

RESOURCES