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. 2022 Jan 15;15(2):647. doi: 10.3390/ma15020647

Predicting the Mechanical Properties of RCA-Based Concrete Using Supervised Machine Learning Algorithms

Meijun Shang 1,*, Hejun Li 2, Ayaz Ahmad 3,4,*, Waqas Ahmad 3, Krzysztof Adam Ostrowski 4, Fahid Aslam 5, Panuwat Joyklad 6, Tomasz M Majka 7
Editor: Jacek Domski
PMCID: PMC8778266  PMID: 35057364

Abstract

Environment-friendly concrete is gaining popularity these days because it consumes less energy and causes less damage to the environment. Rapid increases in the population and demand for construction throughout the world lead to a significant deterioration or reduction in natural resources. Meanwhile, construction waste continues to grow at a high rate as older buildings are destroyed and demolished. As a result, the use of recycled materials may contribute to improving the quality of life and preventing environmental damage. Additionally, the application of recycled coarse aggregate (RCA) in concrete is essential for minimizing environmental issues. The compressive strength (CS) and splitting tensile strength (STS) of concrete containing RCA are predicted in this article using decision tree (DT) and AdaBoost machine learning (ML) techniques. A total of 344 data points with nine input variables (water, cement, fine aggregate, natural coarse aggregate, RCA, superplasticizers, water absorption of RCA and maximum size of RCA, density of RCA) were used to run the models. The data was validated using k-fold cross-validation and the coefficient correlation coefficient (R2), mean square error (MSE), mean absolute error (MAE), and root mean square error values (RMSE). However, the model’s performance was assessed using statistical checks. Additionally, sensitivity analysis was used to determine the impact of each variable on the forecasting of mechanical properties.

Keywords: mechanical properties, aggregate, concrete, compressive strength, split tensile strength, fiber

1. Introduction

Recently, the use of RA in concrete is gaining favour in the field of research, which gives not only environmentally friendly concrete but also shows satisfactory performance towards the mechanical properties of concrete [1,2]. In the previous decades, the production and utilization trend of sustainable concrete has been significantly increasing due to the high demand of the construction industries [3,4]. The production of concrete is now approximately 1t per human in a one-year period [5]. However, the considerable amount of concrete production fulfills the requirement of construction industries and negatively impacts the environmental conditions [6,7,8,9]. The concrete and aggregates production leads to the emission of carbon dioxide, CO2 gas, dust, and other harmful gases, which ultimately results in environmental pollution [10,11,12]. The demand for waste concrete is also increasing because of natural disasters such as earthquakes around the world, leading to serious environmental problems [13,14,15,16]. RCA concrete is considered as one of the potential solutions to reduce the utilization rate of the resources produced naturally and uses the waste concrete appearing from natural disasters, also from the demolition of construction [17,18]. Although the utilization of RCA in concrete is limited due to low strength, low modulus of elasticity, and high deformation, the desired strength can be achieved by adopting the suitable mix design [19].

The applications of the RCA in concrete can significantly enhance the various properties of concrete by adopting smart techniques of adding other suitable materials to it. Recently, the modern approaches of ML for anticipating results in the field of civil engineering are gaining popularity worldwide. Normally, when it comes to forecasting concrete strength, it normally requires 28 days to achieve its desired strength. The different types of ML approaches may applied to forecast the different properties of concrete without consuming time and money. There are multiple types of ML approaches that are normally applied to forecast the required output such as DT, ANN, and GEP. De-Cheng et al. [20] applied an adaptive boosting approach for the anticipation of CS of concrete in which 1030 data bases were utilized to run the required model and reported 98% accuracy compared with the actual result. Dong et al. [21] used the ANN model for high-performance concrete, and they also used Monte Carlo simulation to forecast the behavior of high-strength concrete. Muhammad et al. [22] employed GEP to foretell the concrete’s strength containing bagasse ash; the predicted accuracy was reported to be more than 80%, indicating better performance. Aliakbar et al. [23] indicated the new formulation for the mechanical properties of RA-based concrete with the help of GEP, and they also analyzed that the prediction level was close to the actual results. They investigated the CS, flexural strength, and STS from the retrieved data. Taihao et al. [24] represented their work on the application of ensemble ML techniques for the forecast and optimization of young’s modulus, having RA concrete, the RF, and SVM employed on data for prediction, which shows the accurate prediction of the outcome.

The focus of this research is based on the prediction of two properties (STS and CS) of concrete containing RCA via supervised ML algorithms [25]. The performance of both models was analyzed and compared to evaluate the better performer for the prediction of results. The accuracy level between the real and anticipated output was observed from the coefficient correlation (R2) value, and a higher value gives the impressive performance of the employed model. The AdaBoost technique was employed for optimization via producing 20 sub-models to obtain a higher R2 value [26]. The application of these ML algorithms is to compare the predictive evaluation of each approach. The significance of this study is to determine the effect of the input factors used to anticipate the mechanical characteristics of concrete and the predictive accuracy of both methodologies. The research is innovative in that it uses the type of ML techniques and individual (DT) and ensemble (AdaBoost) ML algorithms to forecast the two outcomes (CS, STS) of recycled coarse aggregate concrete (RCA). The statistical application of checks was applied to analyze the nature of both techniques. In addition, the sensitivity analysis was also incorporated, which indicates the performance level of each input parameter for the anticipation of both STS and CS.

2. Methodology and Description of Data

The model’s performance is based on the input variables and the number of databases used to run the model. The parameters used in this study for running the models to predict the CS and STS of RCA-based concrete were taken from the published literature and are available in Appendix A [27]. The anaconda navigator software was used in this research and incorporated Python coding to run the models for forecasting the results. The excel file with relevant input and output data was uploaded to the software, which runs the model as per the data available in the file. The outcome from the model was then imported for graphical representation. The running of the models comprised nine input parameters (cement, water, fine aggregate, natural CA, RCA, superplasticizers, maximum size of RCA, density of RCA, water absorption of RCA) and two output parameters (CS and STS). The relative frequency distribution of the nine variables can be seen in Figure 1. The relevant references regarding the application of various ML approaches are listed in Table 1. The descriptive statistical analysis for input parameters is illustrated in Table 2, indicating the various mathematical description and ranges of input parameters. In addition, the methodology of the research approach is presented via flowchart, as depicted in Figure 2, which represents the information of the stepwise adopted procedure of the study. The first phase indicates the information of the data obtained, and then the analysis took place using machine learning algorithms, while result explanation, comparison, and evaluation are presented in the next step of the flowchart.

Figure 1.

Figure 1

Figure 1

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Histograms of the input parameters showing the relative frequency scattering.

Table 1.

Various predicted properties with the application of ML approaches.

Sr. No Algorithm Used Notation Data Points Prediction Properties Year Material Used References
1. Support vector machine SVM 144 CS 2021 Fly ash (FA) [28]
2. Gene expression programming GEP 303 Column’s bearing capacity 2019 _ [29]
3. Data Envelopment
Analysis		 DEA 114 Fresh and harden properties of concrete 2021 FA [30]
4. Gene expression programming, Artificial neural network, Decision tree GEP, ANN, DT 642 Surface Chloride Concentration 2021 FA [31]
5. Support vector machine SVM - CS 2020 FA [32]
6. Support vector machine SVM 115 Fresh properties of concrete CS 2020 FA [33]
7. Gene Expression Programming GEP 351 CS 2020 Ground Granulated Blast Furnace Slag [34]
8. Gene Expression Programming GEP 54 CS 2019 NZ (Natural Zeolite) [34]
9. Gene expression programming GEP 357 CS 2020 - [35]
10. Random forest and gene expression programming RF and GEP 357 CS 2020 - [36]
11. Artificial neuron network ANN 205 CS 2019 Fly ash
GGBFS
Rice husk ash		 [37]
12. Intelligent rule-based enhanced multiclass support vector machine and fuzzy rules IREMSVM-FR with
RSM		 114 CS 2019 Fly ash [38]
13. Random forest RF 131 CS 2019 Fly ash
GGBFS
FA		 [39]
14. Multivariate adaptive regression spline M5
MARS		 114 CS
Slump test
L-box test
V-funnel test		 2018 FA [40]
15. Random Kitchen Sink Algorithm RKSA 40 V-funnel test
J-ring test
Slump test
CS		 2018 FA [41]
16. Adaptive neuro fuzzy inference system ANFIS 55 CS 2018 - [42]
17. Artificial neuron network ANN 114 CS 2017 FA [43]
18. Artificial neuron network ANN 69 CS 2017 FA [44]
19 Individual and ensemble algorithm GEP, DT, Bagging 270 CS 2021 FA [45]
20. Individual with ensemble modeling ANN, bagging, boosting 1030 CS 2021 FA [46]
21. Multivariate MV 21 CS 2020 Crumb rubber with SF [47]
22. Gene expression programming GEP 277 Axial capacity 2020 - [48]
23. Adaptive neuro fuzzy inference system ANFIS with ANN 7 CS 2020 Palm oil fuel ash [49]
24. Response Surface Method, Gene expression programming RSM, GEP 108 CS 2020 Steel Fibers [50]
25. Artificial neural network ANN 60 CS 2021 Ceramic waste powder [51]
27. Decision tree, Artificial neural network, Bagging, Gradient boosting DT, ANN, BR, GB 207 CS 2021 FA [52]
28. Gene expression programming, Artificial neural network, Decision tree GEP, ANN, DT 98 CS 2021 FA [53]
29. Individual and Ensemble techniques BR, GEP, DT 1030 CS 2021 FA [54]
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Table 2.

Details of the descriptive analysis.

Statistics Water Cement FA NCA RCA SP RCA Size RCA Density Absorption
Mean 184.62 386.86 681.89 398.07 650.74 1.32 19.76 2231.06 4.80
Standard Error 1.39 4.43 11.07 19.99 20.37 0.11 0.22 31.32 0.12
Median 180.00 380.00 698.00 471.00 552.00 0.00 20.00 2362.50 4.90
Mode 220.00 380.00 693.00 0.00 138.00 0.00 20.00 2320.00 5.30
Standard Deviation 25.84 82.16 205.28 370.71 377.73 2.05 4.02 580.95 2.26
Sample Variance 667.47 6750.28 42,141.11 137,424.94 142,682.56 4.21 16.16 337,504.80 5.12
Minimum 117.6 158 0 0.00 52 0. 10 0 0
Maximum 271. 600 1010 1448 1778 7.8 32 2661 10.9
Sum 63,510.69 133,081.00 234,568 136,937 223,853 455.5 6796 767,484 1652.8
Count 344.00 344.00 344 344 344 344 344 344 344
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Figure 2.

Figure 2

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Flow chart of the research program.

3. Supervised Machine Learning Algorithms

3.1. Decision Tree Algorithm

The DT algorithm is a subset of the supervised machine learning (ML) technique known as individual supervised machine learning (ISML). It is applicable to classification and regression problems. This approach aims to generate a model that can forecast the targeted variable, for which it uses the representation of a tree to solve the problem. In machine learning, the classification process has two steps, the learning and forecasting steps. The learning step belongs to the development of the model based on the given data set, while, in the prediction step, said model is then used to foretell the response of the data. A decision tree is a well-known and effective classification technique that is simple to comprehend and apply. Sub-node creation improves the homogeneity of specific sub-nodes. There are several important terminologies associated with the decision tree. These include root nodes, which indicate the overall population of the sets; splitting, which refers to the process of dividing the nodes; decision nodes, which refers to the process of splitting sub-nodes into further sub-nodes; leaf nodes, which are the type of nodes that do not split; and pruning, which refers to the process of removing sub-nodes.

3.2. AdaBoost Algorithm

The AdaBoost regressor is a supervised ML technique that uses an ensemble approach. It is also known as adaptive boosting because the weights are re-assigned to each instance, with greater weights going to instances that were mistakenly identified. Boosting techniques are commonly used in supervised learning to reduce bias and variation. These ensemble algorithms are used to improve the performance of the weak learner. During the training phase for the input data, it uses an endless number of decision trees. The recorded data that are incorrectly categorized throughout the initial model are given a high priority while developing the initial decision tree/model. These are the only data entries that are utilized as the input for a different model. The preceding technique will be repeated until the desired number of basic learners has been reached. When it comes to binary classification problems, the AdaBoost regressor outperforms the competition in terms of improving decision tree performance. It is also used to boost the efficiency of other machine learning methods. When used with a slow student, it is quite beneficial. The use of these ensemble methods is most common in civil engineering, especially when it comes to predicting the mechanical properties of different types of concrete.

4. Result and Their Analyses

4.1. Statistical Analysis

The result obtained from the statistical analyses indicated that the relationship between the actual and predicted outcomes (CS and STS) from the individual and ensemble ML algorithms, along with the distribution of errors, is explained as follows.

4.1.1. Compressive Strength Result Using Decision Tree

The relationship between the actual and predicted result of compressive strength for the decision tree algorithm can be seen in Figure 3a, along with the distribution of the errors shown in Figure 3b. The errors distribution for DT gives the maximum, minimum, and average values equal to 8.82 MPa, 0.58 MPa, and 3.58 MPa, respectively. However, 11.59% of the error data lie between 0 and 1 MPa, and 50.72% of the data lie between 2 MPa and 6 MPa. In addition, only 8.69% of the data lie above 7 MPa.

Figure 3.

Figure 3

Figure 3

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Numerical analyses representing the relationship between the predicted variables and targeted variables (a) along with their error distribution (b) for compressive strength using DT.

4.1.2. Splitting Tensile Strength Result Using Decision Tree

The relation of the actual and predicted outcome of splitting tensile strength using the DT approach in depicted in Figure 4a along with its error distribution depicted in Figure 4b. The error distribution indicates the higher, lower, and average values equal to 2.47 MPa, 0, and 0.31 MPa, respectively. In contrast, 42.02% of the error data lie between 0 and 0.1 MPa, while 34.78% of the data lie between 0.1 MPa and 0.5 MPa. However, only 8.69% of the error data were reported as above 1 MPa.

Figure 4.

Figure 4

Figure 4

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Numerical analyses representing the relationship between the predicted variables and targeted variables (a) along with their error distribution (b) for splitting tensile strength using DT.

4.1.3. Compressive Strength Result with AdaBoost Regressor

AdaBoost regressor gives strong relation between the real and anticipated output, as shown in Figure 5a, while the distribution of the error’s value can be seen in Figure 5b. It shows the maximum, lower, and average values of the error data equal to 13 MPa, 0.06 MPa, and 2.33 MPa, respectively. Additionally, 26.08% of the error data were reported between 0 and 1 MPa, while 34.78% of the data lie between 2 MPa and 6 MPa. However, 4.34% of the error data were reported to be above 7 MPa.

Figure 5.

Figure 5

Figure 5

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Numerical analyses representing the relationship between the predicted variables and targeted variables (a) along with their error distribution (b) for compressive strength using the AdaBoost regressor.

4.1.4. Splitting Tensile Strength with AdaBoost Regressor

The statistical result of splitting tensile strength using the AdaBoost regressor also shows strong relations with less variance among the experimental results obtained from the model, as depicted in Figure 6a. The distribution of the errors obtained from the application of the AdaBoost regressor can be seen in Figure 6b. The error distribution shows the maximum, minimum, and average values equal to 1.46 MPa, 0, and 0.30 MPa, respectively. However, 36.26% of the error data lie between 0 and 0.1 MPa, while 34.78% of the data lie between 0.1 MPa and 0.5 MPa. In addition, only 4.34% of the errors data lie above 1 MPa.

Figure 6.

Figure 6

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Numerical analyses representing the relationship between the predicted variables and targeted variables (a) along with their error distribution (b) for splitting tensile strength using the AdaBoost regressor.

4.2. K-Fold Cross-Validation and Statistical Checks

This process is normally adopted to check the authentic execution of the models. The authentic performance of the employed models is being verified from the k-fold cross-validation process. In this method, the available data set is arranged randomly and split up into ten groups. A total of 60% of the dataset from total data points were used to train the model, 30% of the dataset were used to test the model, and 10% of the data were used for validation purposes. The process takes place in such a way that nine groups from ten are assigned for training the models, while the remaining one is for validation of the models. The said process was again repeated ten times to obtain the suitable average value. The K-fold cross-validation process also confirms the performance accuracy of the models. The statistical checks to confirm the accuracy level of the model’s prediction were also employed using the equations illustrated below from (1)–(5)

RMSE=∑i=1n (exi−moi)2n (1)
MAE=∑i=1n|exi−moi|n (2)
RSE=∑i=1n(moi−exi)2∑i=1n(ex¯−exi)2 (3)
RRMSE=1e∑i=1n(exi−moi)2n (4)
R=∑i=1n(exi−ex¯i)(moi−mo¯i)∑i=1n(exi−ex¯i)2∑i=1n(moi−mo¯i)2 (5)

where,

exi = experimental value,

moi = predicted value,

ex¯i = mean experimental value,

mo¯i = mean predicted value obtained by the model,

n = number of samples.

As seen in Figure 7, Figure 8, Figure 9 and Figure 10, the coefficient correlation (R2), mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE) were used to evaluate the k-fold cross-validation of each employed model against its output. The variation was also noticed in the outcomes of both ML algorithms used (DT and AdaBoost). The lower the number of errors in the AdaBoost model, the higher the coefficient correlation (R2) value, indicating a higher accuracy level than the decision tree. The information obtained from the analysis for both CS and STS used for k-fold cross-validation is listed in Table 3 and Table 4, respectively.

Figure 7.

Figure 7

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Representation of statistical information of k-fold cross-validation using DT for compressive strength.

Figure 8.

Figure 8

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Representation of statistical information of k-fold cross-validation using DT for splitting tensile strength.

Figure 9.

Figure 9

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Representation of statistical information of k-fold cross-validation using AdaBoost for Compressive strength.

Figure 10.

Figure 10

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Representation of statistical information of k-fold cross-validation using AdaBoost for Splitting tensile strength.

Table 3.

Result of k-fold cross-validation for compressive strength.

AdaBoost Decision Tree
K-Fold MAE MSE RMSE R2 K-Fold MAE MSE RMSE R2
1 7.49 87.20 9.34 0.95 1 13.45 216.60 14.72 0.76
2 11.08 124.99 11.18 0.91 2 23.10 271.26 16.47 0.89
3 7.04 114.49 10.70 0.83 3 14.85 204.20 14.29 0.97
4 8.45 90.82 9.53 0.79 4 9.59 131.79 11.48 0.92
5 6.52 85.74 9.26 0.90 5 11.17 217.26 14.74 0.98
6 16.74 435.13 20.86 9.98 6 12.51 197.40 14.05 0.82
7 9.88 193.48 13.91 0.93 7 9.07 210.54 14.51 0.77
8 8.72 123.43 11.11 0.85 8 10.15 194.60 13.95 0.88
9 7.02 40.44 6.36 0.87 9 9.00 108.16 10.42 0.90
10 9.00 87.42 9.35 0.90 10 7.44 90.44 9.51 0.87
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Table 4.

Result of k-fold cross-validation for splitting tensile strength.

AdaBoost Decision Tree
K-Fold MAE MSE RMSE R2 K-Fold MAE MSE RMSE R2
1 0.76 1.44 1.20 0.91 1 0.52 0.50 0.71 0.73
2 1.04 2.37 1.54 0.88 2 1.76 7.02 2.65 0.88
3 0.78 1.39 1.18 0.74 3 0.77 0.98 0.99 0.97
4 0.84 1.82 1.35 0.90 4 1.41 3.45 1.86 0.91
5 0.45 0.28 0.53 0.98 5 1.17 2.72 1.65 0.79
6 1.11 2.13 1.46 0.95 6 1.25 3.68 1.92 0.84
7 0.93 2.62 1.62 0.79 7 1.20 4.24 2.06 0.80
8 1.31 3.31 1.82 0.81 8 1.25 2.22 1.49 0.96
9 0.59 0.57 0.76 0.93 9 0.43 0.57 0.75 0.91
10 1.16 3.57 1.89 0.87 10 1.85 5.15 2.27 0.85
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Additionally, the information of statistical checks in the form of MAE, MSE, and RMSE were assessed for both CS and STS and can be seen in the Table 5 and Table 6, respectively. The lesser error shows a higher coefficient correlation value (R2).

Table 5.

Statistical checks for compressive strength.

Algorithms Used MAE (MPa) MSE (MPa) RMSE (MPa)
Decision tree (DT) 3.58 11.02 3.32
AdaBoost 2.33 7.8 2.79
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Table 6.

Statistical checks for splitting tensile strength.

Algorithm Used MAE (MPa) MSE (MPa) RMSE (MPa)
Decision tree (DT) 0.31 0.29 0.54
AdaBoost 0.30 0.20 0.45
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5. Sensitivity Analyses

The input variables have a remarkable effect on the execution of the model’s outcome. The sensitivity analyses were done to investigate the effect of each variable on the anticipation of both STS and CS, as depicted in Figure 5. The cement significantly contributed (36.8%) towards the prediction of CS, while other parameters contributed the least towards the forecasting of concrete CS containing RCA, as shown in Figure 11. However, the contribution of parameters for predicting the STS can be seen in Figure 12. The significant contributions for the prediction of the STS of concrete were cement (41.2%) and natural coarse aggregate (NCA) (19%), while superplasticizers and RCA were the next highest contributors for the prediction of outcomes. The equations mentioned below were used to calculate the contribution of each parameter towards the model’s outcome.

Ni=fmax(xi)−fmin(xi) (6)
Si=Ni∑j−inNj  (7)

where—fmin (xi) and fmax (xi) are the lower and higher of the estimated output over the ith output.

Figure 11.

Figure 11

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Contributing level of the input variables towards the prediction of output for CS.

Figure 12.

Figure 12

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Contributing level of the input variables towards the prediction of output for STS.

6. Discussion

As demonstrated by the data, the ML-based strategy for forecasting the mechanical characteristics of concrete is clearly better than traditional mechanics-based methods. The advantages are as follows: (1) ML does not at all require complex mechanics/theoretical equations but instead finds the mapping between the input and output utilizing numerical and/or computer knowledge of science, making it very accessible to the readers; (2) Unlike most empirical models, which typically consider a limited number of variables when deriving the formula, ML can consider an infinite number of variables; (3) Meanwhile, inherent uncertainties [55] can be incorporated into the training process; and (4) The precision, reliability, and robustness of machine learning-based models are significantly higher than those of traditional models: they can provide objective and accurate results in a matter of seconds.

The research approach of this study was to predict the mechanical properties (CS and STS) of concrete containing recycled coarse aggregates (RCA) via supervised machine learning algorithms. The anaconda navigator software was used to incorporate the Python coding for each employed machine learning algorithm. An excel file having a relevant database was used in the software, which allowed it to show the output results in the form of R2, MAE, MSE, and RMSE. The AdaBoost technique performs well, as proven by the coefficient correlation (R2) value of 0.95 for CS prediction and 0.92 for STS prediction., Feng et al. [56] Additionally, AdaBoost was used to classify failure modes, yielding an accuracy of 0.96, and to determine the bearing capacity of reinforced concrete, yielding an R2 value of 0.98. However, the value of R2 for DT in predicting the CS was 0.93, and in forecasting, the STS was equal to 0.90. In comparison, Ahmad et al. [57] also employed DT to predict the CS of geopolymer concrete, which shows a reasonable and almost similar value of R2 equal to 0.90 for its outcome. The higher value of R2 (0.95 for compressive and 0.92 for STS) for AdaBoost indicates the high performance towards the prediction of the outcomes as compared to the R2 value for DT (0.93 for compressive and 0.90 for STS). The lesser values of each error (MAE, MSE, RMSE) for AdaBoost also confirm the model’s better accuracy level as opposed to the errors values for the DT. In addition, the sensitivity analysis describes the contribution level of each parameter used to run the model for predicting the mechanical properties of concrete containing recycled coarse aggregates. Cement and natural coarse aggregate (NCA) contributed significantly, up to 41.2% and 19%, respectively, while superplasticizers and RCA were the next highest contributors for the prediction of outcomes. It was noted that the accuracy level of the ensemble machine learning approach (AdaBoost) was higher than the individual machine learning technique (DT).

7. Conclusions and Future Recommendations

This research describes the application of both individual and ensemble ML algorithms to forecast the mechanical properties such as compressive strength (CS) and splitting tensile strength (STS) of concrete having recycled coarse aggregate (RCA). The decision tree (DT) and AdaBoost approaches were incorporated for prediction purposes. The input variables were analyzed by indicating their relative frequency distribution. The Python coding was used in the Spyder (Anaconda software) to run the required models for further investigation. The statistical checks in the form of various errors (MAE, RMSE, MSE) were evaluated to confirm the accuracy of each employed model. However, the k-fold cross-validation method was also included in the study for the confirmation of the model’s accuracy. In addition, the contribution of each input variable was investigated via sensitivity analysis. The following conclusions and future recommendations can be drawn from the study.

  • The ensemble machine learning algorithm (AdaBoost) shows a better response with less variance towards the prediction of both the CS and splitting tensile strength of RCA-based concrete.

  • The AdaBoost regressor gives the values of coefficient correlation (R2) for CS and STS of concrete equal to 0.95 and 0.92, respectively, as opposed to the values of R2 for DT equal to 0.93 (CS) and 0.90 (STS).

  • The higher values of R2 for the AdaBoost regressor towards the prediction of both CS and STS indicate the high accuracy of the model.

  • From the statistical checks, the lesser value of the errors (MAE, MSE, RMSE) also indicates high performance for the AdaBoost approach compared to the DT algorithm.

  • The K-fold cross-validation method also confirms the high accuracy level of the AdaBoost algorithm.

  • Sensitivity analysis reveals that the cement contributed effectively (32%) as compared to other parameters towards the forecasting of the CS of RCA-based concrete, while the superplasticizers were the higher contributor towards the prediction of the STS of concrete containing RCA.

In conclusion, this study was based on the application of supervised machine learning (ML) algorithms to foretell the two parameters (CS and STS) of concrete having recycled coarse aggregate (RCA). It also gives an idea of the importance of multiple aspects like the input variables, the number of data points for running the models, and the types of ML approaches to be used for high accuracy of the outcomes. The algorithms employed in this study show a strong relationship between the actual and predicted output. The importance of these approaches in civil engineering is indicated by their high accuracy level among the real and forecasted results. The supervised ML approaches are gaining more popularity, as their application gives high accuracy results/outcomes and minimizes the physical approach of the practical work and total cost of the project. Additionally, it is essential to incorporate laboratory work to compare machine learning approaches’ findings to better understand their effectiveness. Additionally, the data points, type of material used, size of specimens, environmental conditions, curing conditions, loading rate, and increase in the input parameters can be modified or added to study and compare the results of various machine learning algorithms. Moreover, various ML techniques such as artificial neural networks (ANN), support vector machines (SVM), and boosting can be included to evaluate their performance.

Appendix A

Water (kg/m3). Cement (kg/m3) Sand (kg/m3) NCA (kg/m3) RCA (kg/m3) SP (kg/m3) RCA Size (kg/m3) Density (kg/m3) Absorption (%) CS (MPa) STS (MPa)
165 370 650 850.5 364.5 2.22 20 2400 4.9 50.6 -
165 370 650 607.5 607.5 2.22 20 2400 4.9 50.8 -
165 370 650 - 1215 2.22 20 2400 4.9 50.2 -
165 460 575 850.5 364.5 2.22 20 2400 4.9 60.8 -
165 460 575 607.5 607.5 2.22 20 2400 4.9 61.2 -
165 460 575 - 1215 2.22 20 2400 4.9 60.2 -
165 560 495 850.5 364.5 2.59 20 2400 4.9 70.2 -
165 560 495 607.5 607.5 2.59 20 2400 4.9 70.8 -
165 560 495 - 1215 2.59 20 2400 4.9 70 -
180 500 486.6 - 1135.4 - 16 - - 44.5 4
180 500 - - 1574.3 - 16 - - 38.7 3.5
180 500 486.6 - 1135.4 - 16 - - 46.1 3.4
180 500 - - 1574.3 - 16 - - 42.4 3.2
180 500 486.6 - 1135.4 - 16 - - 52.5 4
180 500 - - 1574.3 - 16 - - 50.7 3.6
180 500 486.6 - 1135.4 - 16 - - 45.2 3.5
180 500 - - 1574.3 - 16 - - 42 3.2
180 500 486.6 - 1135.4 - 16 - - 49.6 3.8
180 500 - - 1574.3 - 16 - - 45.1 3.5
180 500 509.6 - 1135.4 - 16 - - 54.4 4
180 500 - - 1574.3 - 16 - - 48.2 3.8
207.6 400 662 863 153 - 20 2410 5.8 38.1 3.7
207.6 400 662 697 298 - 20 2410 5.8 37 3.6
207.6 400 662 383 573 - 20 2410 5.8 35.8 3.4
207.6 400 662 - 903 - 20 2410 5.8 34.5 3.3
217 353 660 861 209 - 20 2330 6.3 44.9 -
229 353 647 527 513 - 20 2330 6.3 44.7 -
241 353 625 - 993 - 20 2330 6.3 46.8 -
230 353 661 853 202 - 20 2330 6.3 43.2 -
247 353 647 524 496 - 20 2330 6.3 39.7 -
271 353 625 - 959 - 20 2330 6.3 43.3 -
206 353 661 864 216 - 20 2330 6.3 43 -
207 353 649 531 531 - 20 2330 6.3 38.1 -
165 300 765 905 267 4.98 25 2430 4.4 42 3
165 318 739 608 537 6.042 25 2430 4.4 41 3.2
162 325 683 - 1123 6.175 25 2430 4.4 40 3.2
160.6 380 598 1182 52 4.9 20 2165 6.8 62.2 -
165.4 380 529 1175 103 4.9 20 2165 6.8 58.4 -
170.2 380 460 1168 154 4.9 20 2165 6.8 61.3 3.7
175.6 380 327 1162 254 4.9 20 2165 6.8 60.8 -
180.9 380 - 1162 509 4.9 20 2165 6.8 61 3
225 410 642 840 204 - 20 2570 3.5 45.3 -
225 410 642 524 506 - 20 2570 3.5 42.5 -
225 410 642 210 814 - 20 2570 3.5 39.2 -
225 410 642 - 1017 - 20 2570 3.5 37.1 -
180 400 708 886 215 - 20 2570 3.5 62.4 3.2
180 400 708 554 538 - 20 2570 3.5 55.8 3
180 400 708 - 1075 - 20 2570 3.5 42 2.8
225 410 642 840 204 - 20 2570 3.5 45.3 3.2
225 410 642 524 506 - 20 2570 3.5 42.5 3.2
225 410 642 - 1017 - 20 2570 3.5 38.1 3.1
234 360 705 - 1100 - 19 2390 4.4 22.1 -
190 380 705 - 1100 - 19 2390 4.4 25.1 -
192 400 705 - 1100 - 19 2390 4.4 27.2 -
181 420 705 - 1100 - 19 2390 4.4 28.7 -
184 460 705 - 1100 - 19 2390 4.4 29.5 -
178 264 835 - 1030 - 30 2520 3.8 18 -
174 262 830 - 1020 - 30 2510 3.9 15.4 -
148 427 760 - 1000 4.2 30 2520 3.8 36.4 -
153 423 755 - 990 4.1 30 2510 3.9 35.7 -
152 443 855 - 885 3.9 30 2520 3.8 44.4 -
225 410 642 840 204 - 20 2580 3.5 45.3 3.6
225 410 642 524 506 - 20 2580 3.5 42.5 3.4
225 410 642 - 1017 - 20 2580 3.5 38.1 3.3
205 410 662 865 210 - 20 2580 3.5 51.7 3.6
205 410 662 541 525 - 20 2580 3.5 47.1 3.6
205 410 662 - 1049 - 20 2580 3.5 43.4 3.4
180 400 708 886 215 5.6 20 2580 3.5 62.4 3.7
180 400 708 554 538 5.6 20 2580 3.5 56.8 3.7
180 400 708 - 1075 5.6 20 2580 3.5 52.1 3.5
160 400 729 912 221 7.8 20 2580 3.5 69.6 4.1
160 400 729 570 554 7.8 20 2580 3.5 65.3 4
160 400 729 - 1107 7.8 20 2580 3.5 58.5 3.8
175 350 730 711 297 1.68 25 2530 1.9 36.7 4.2
175 350 730 508 494 1.68 25 2530 1.9 38 4
175 350 730 - 989 1.68 25 2530 1.9 36 3.8
175 350 730 711 282 1.68 25 - - 32.6 3.6
175 350 730 508 469 1.68 25 2400 6.2 30.4 3.4
175 350 730 - 938 1.68 25 2400 6.2 29.5 3.2
190 380 744.45 756.97 189.24 2.66 20 2338 5.2 47.4 -
190 380 709.54 471.13 471.12 2.66 20 2338 5.2 47.3 -
190 380 714.56 - 874.04 5.32 20 2338 5.2 54.8 -
140 350 732 519 556 4.2 12 2420 6.8 43.3 -
153 340 723 512 549 3.4 12 2400 6.8 39.6 -
165 330 715 507 543 2.64 12 2400 6.8 38.1 -
176 320 708 502 537 1.92 12 2400 6.8 34.5 -
186 310 702 497 533 1.24 12 2400 6.8 31.6 -
140 350 732 553 523 4.2 22 2420 8.8 46.1 -
153 340 723 547 517 3.4 22 2420 8.8 45.8 -
165 330 715 541 511 2.64 22 2420 8.8 39.9 -
176 320 708 535 506 1.92 22 2420 8.8 36.3 -
186 310 702 531 501 1.24 22 2420 8.8 34.7 -
186 372 617.65 1030.22 257.56 - 20 2400 - 27.2 3.1
186 372 617.65 772.67 515.55 - 20 2400 - 26.5 2.8
186 372 617.65 515.11 772.67 - 20 2400 - 25.4 2.7
186 372 617.65 257.56 1030.22 - 20 2400 - 25.1 2.2
186 372 494.12 128.78 123.53 - 20 2630 - 26.4 2.6
186 372 370.59 128.78 247.06 - 20 2630 - 25.9 2.5
186 372 247.06 128.78 370.59 - 20 2630 - 23.5 2.3
186 372 123.53 128.78 494.12 - 20 2630 - 15.4 2
200 270 750 675 200 1.08 19 2440 5.8 18.5 1.9
210 270 750 450 400 1.35 19 2440 5.8 18 1.9
220 270 750 225 600 1.62 19 2440 5.8 16.5 1.4
165 370 865 760 230 1.48 19 2440 5.8 33 3.1
165 370 865 505 455 1.85 19 2440 5.8 34.5 3.1
165 370 865 250 680 2.59 19 2440 5.8 34 2.9
178.5 275 938.05 723.07 180.77 1.925 16 2400 5 31.7 2.4
178.5 275 962.73 423.77 423.77 1.925 16 2400 5 32.4 2.5
178.5 275 1005.18 - 756.46 1.925 16 2400 5 30.1 2.6
190 380 794.31 750.04 187.57 2.66 16 2400 5 43.7 3.1
190 380 811.37 443.71 443.71 2.66 16 2400 5 37.5 2.9
190 380 838.29 - 807.97 2.66 16 2400 5 40.5 2.9
151 335 630 414 720 1.266 19 2420 5.4 41.4 -
156 349 888 - 792 1.67616 19 2420 5.4 43.9 -
161 358 645 281 813 1.3584 19 2500 3.3 44.8 -
156 349 857 - 867 1.2564 19 2500 3.3 45.9 -
172.43 401 574 911 303 0.2005 20 2661 1.9 47 2.3
172.43 401 574 585 585 0.70175 20 2602 2.6 46 2.1
172.43 401 574 - 1119 0.90225 20 2510 3.9 42.5 2
190.8 424 770 - 980 - 19 2490 4.8 41 -
192.5 350 800 - 1015 - 19 2490 4.8 33.3 -
191.75 295 814 - 1039 - 19 2490 4.8 24.8 -
150 250 762 858 286 4.375 19 - - 26.7 2.2
150 250 753 564 564 4.375 19 - - 21.5 1.8
150 250 743 279 836 4.375 19 - - 21.4 1.4
150 250 734 - 1100 4.375 19 - - 20 1.2
180 400 685 770 257 3 19 - - 38.3 3.1
180 400 676 507 507 3 19 - - 37 2.7
180 400 667 250 751 3 19 - - 35 2.5
180 400 659 - 988 3 19 - - 33.3 2.1
175 325 - - 1762 3.45 32 2263 6 33.2 -
222 350 - - 1778 4.5 32 2283 4.2 35.6 -
221 350 - - 1771 4.5 32 2292 4.3 34.6 -
195 325 - - 1710 3.25 32 2301 5 37.3 -
123 300 - 192 1728 3 32 2609 1.5 45.4 -
144 325 - 768 1152 3.25 32 2518 2.7 54.3 -
123 325 - 754.4 1131.6 3.25 32 2584 1.6 54.4 -
132 300 - 1448.25 482.75 3 32 2594 1.6 53.4 -
180 275 625 882 378 - 20 2340 5.3 20 -
180 295 595 635 635 - 20 2340 5.3 19 -
180 310 610 - 1240 - 20 2340 5.3 18 -
180 330 585 872 373 - 20 2340 5.3 23 -
180 355 560 623 623 - 20 2340 5.3 24 -
180 372 536 - 1252 - 20 2340 5.3 21 -
180 355 560 872 373 - 20 2340 5.3 25 -
180 385 550 613 613 - 20 2340 5.3 29 -
180 409 525 - 1226 - 20 2340 5.3 30 -
180 375 544 869 372 - 20 2340 5.3 39 -
180 405 508 624 624 - 20 2340 5.3 31 -
180 426 494 - 1241 - 20 2340 5.3 34 -
193 350 661 1061 57 - 12 2010 10.9 40 2.9
194 350 515 1061 170 - 12 2010 10.9 38.6 2.7
196 350 368 1061 283 - 12 2010 10.9 37.6 2.6
199 158 - 1061 566 - 12 2010 10.9 38.6 2.5
158 350 693 1111 59 3.5 12 2010 10.9 53.7 3.4
163 350 536 1105 177 3.5 12 2010 10.9 51 3.3
168 350 381 1100 294 3.5 12 2010 10.9 47.8 3.1
178 350 - 1089 582 3.5 12 2010 10.9 45.1 3
137 350 713 1143 61 3.5 12 2010 10.9 64.6 4.2
139 350 555 1143 183 3.5 12 2010 10.9 65.4 4.5
143 350 395 1138 304 3.5 12 2010 10.9 63.2 3.7
150 350 - 1132 605 3.5 12 2010 10.9 63 3.4
180 281 802 - 970 - 10 2360 4.7 38.6 3.5
170 293 648 - 919 - 10 2280 6.2 38.1 3.1
165 337 841 - 879 - 10 2220 7.8 39.3 3.3
190 463 621 - 970 - 10 2360 4.7 60.1 3.8
190 500 621 - 919 3.24 10 2280 6.2 60.2 3.7
180 600 567 - 879 5.04 10 2220 7.8 62.8 3.7
220 537 693 782 138 - 20 2330 4.4 50.8 -
220 537 693 644 276 - 20 2330 4.4 44.9 -
220 537 693 506 414 - 20 2330 4.4 44.6 -
220 537 693 368 552 - 20 2330 4.4 42.4 -
220 537 693 782 138 - 20 2370 4 54 -
220 537 693 644 276 - 20 2370 4 56 -
220 537 693 506 414 - 20 2370 4 54.4 -
220 537 693 368 552 - 20 2370 4 40.6 -
220 537 693 782 138 - 20 2390 3.6 55.2 -
220 537 693 644 276 - 20 2390 3.6 53.5 -
220 537 693 506 414 - 20 2390 3.6 56.9 -
220 537 693 368 552 - 20 2390 3.6 54.7 -
220 537 693 782 138 - 20 2320 4.6 50.5 -
220 537 693 644 276 - 20 2320 4.6 48.9 -
220 537 693 506 414 - 20 2320 4.6 45.8 -
220 537 693 368 552 - 20 2320 4.6 40 -
220 537 693 782 138 - 20 2390 3.7 54.4 -
220 537 693 644 276 - 20 2390 3.7 50.2 -
220 537 693 506 414 - 20 2390 3.7 49.5 -
220 537 693 368 552 - 20 2390 3.7 40.4 -
220 537 693 782 138 - 20 2390 3.5 45 -
220 537 693 644 276 - 20 2390 3.5 46.9 -
220 537 693 506 414 - 20 2390 3.5 51.4 -
220 537 693 368 552 - 20 2390 3.5 53.2 -
220 537 693 782 138 - 20 2380 3.8 55.3 -
220 537 693 644 276 - 20 2380 3.8 55.9 -
220 537 693 506 414 - 20 2380 3.8 52.6 -
220 537 693 368 552 - 20 2380 3.8 48 -
220 537 693 782 138 - 20 2380 3.8 49.1 -
220 537 693 644 276 - 20 2380 3.8 49.9 -
220 537 693 506 414 - 20 2380 3.8 50.3 -
220 537 693 368 552 - 20 2380 3.8 47.5 -
220 537 693 782 138 - 20 2400 3.5 43.2 -
220 537 693 644 276 - 20 2400 3.5 53.7 -
220 537 693 506 414 - 20 2400 3.5 50 -
220 537 693 368 552 - 20 2400 3.5 43.3 -
220 537 693 782 138 - 20 2370 4 52.9 -
220 537 693 644 276 - 20 2370 4 49.9 -
220 537 693 506 414 - 20 2370 4 53.7 -
220 537 693 368 552 - 20 2370 4 46 -
206 413 606 - 987 - 25 2452 4.1 51 -
206 413 606 - 987 - 25 2452 4.1 49 -
206 413 606 - 987 - 25 2452 4.1 48 -
206 413 606 537 494 - 25 2452 4.1 51 -
206 413 606 537 494 - 25 2452 4.1 51 -
206 413 606 537 494 - 25 2452 4.1 51 -
206 413 606 805 245 - 25 2452 4.1 52 -
206 413 606 805 245 - 25 2452 4.1 50 -
206 413 606 805 245 - 25 2452 4.1 49 -
145.6 520 577.2 - 1040 - 25 2260 7.5 38.3 -
145.6 520 577.2 - 1040 - 25 2260 7.5 32.9 -
119.6 520 577.2 - 1040 - 25 2260 7.5 33.2 -
146.2 430 653.6 - 1032 - 25 2260 7.5 31.3 -
146.2 430 653.6 - 1032 - 25 2260 7.5 28.4 -
120.4 430 653.6 - 1032 - 25 2260 7.5 28 -
145.77 339 728.85 - 1050.9 - 25 2260 7.5 26.5 -
145.77 339 728.85 - 1050.9 - 25 2260 7.5 23.3 -
118.65 339 728.85 - 1050.9 - 25 2260 7.5 21.6 -
144.06 294 767.34 - 1029 - 25 2260 7.5 21.6 -
144.06 294 767.34 - 1029 - 25 2260 7.5 18 -
117.6 294 767.34 - 1029 - 25 2260 7.5 18.8 -
146.91 249 804.27 - 1045.8 - 25 2260 7.5 16.1 -
146.91 249 804.27 - 1045.8 - 25 2260 7.5 13.4 -
119.52 249 804.27 - 1045.8 - 25 2260 7.5 13.9 -
179 275 878 735 184 - 20 2320 5.3 41 2.8
179 275 849 455 455 - 20 2320 5.3 44 3.1
179 275 868 - 830 - 20 2320 5.3 45 2.4
190 380 744 757 189 - 20 2320 5.3 50.5 3.5
190 380 710 471 471 - 20 2320 5.3 45 2.7
190 380 715 - 874 - 20 2320 5.3 56 3.7
179 275 961 740 185 - 20 2320 5.3 33.5 2.5
179 275 978 408 408 - 20 2320 5.3 32 2.5
179 275 1010 - 640 - 20 2320 5.3 32 2.3
190 380 813 767 192 - 20 2320 5.3 44 2.8
190 380 822 426 427 - 20 2320 5.3 41 2.6
190 380 836 - 683 - 20 2320 5.3 41.5 2.3
179 325 799 839 210 - 20 2320 5.3 44 2.8
179 325 831 490 490 - 20 2320 5.3 41 2.7
179 325 825 - 923 - 20 2320 5.3 33.5 2.3
173 385 698 892 223 - 20 2320 5.3 53.5 3.1
173 385 742 515 515 - 20 2320 5.3 54 3.9
173 385 746 - 963 - 20 2320 5.3 40 2.4
159.6 380 862.4 489.3 489.3 5.7 20 2330 6.1 41.6 -
193.8 380 934.1 - 867.7 6.46 20 2330 6.1 31.4 -
197.6 380 862.4 489.3 489.3 5.7 20 2330 6.1 35.5 -
231.8 380 934.1 - 867.7 6.46 20 2330 6.1 26 -
167.2 380 862.4 489.3 489.3 5.7 20 2320 5.8 44.6 -
193.8 380 934.1 - 867.7 6.46 20 2320 5.8 36.7 -
235.6 380 934.1 - 867.7 6.46 20 2320 5.8 29.5 -
155.8 380 818.5 840.9 210.2 4.56 20 2360 3.9 46.1 -
159.6 380 862.4 489.3 489.3 5.7 20 2360 3.9 45.1 -
171 380 934.1 - 867.7 6.46 20 2360 3.9 42.9 -
190 380 818.5 840.9 210.2 4.56 20 2360 3.9 39.3 -
197.6 380 862.4 489.3 489.3 5.7 20 2360 3.9 39.5 -
205.2 380 934.1 - 867.7 6.46 20 2360 3.9 37.7 -
159.6 380 818.5 840.9 210.2 4.56 20 2350 4.5 48.1 -
163.4 380 862.4 489.3 489.3 5.7 20 2350 4.5 41 -
152 380 934.1 - 867.7 6.46 20 2350 4.5 38.7 -
193.8 380 818.5 840.9 210.2 4.56 20 2350 4.5 42.7 -
197.6 380 862.4 489.3 489.3 5.7 20 2350 4.5 35.4 -
190 380 934.1 - 867.7 6.46 20 2350 4.5 31.4 -
159.6 380 818.5 840.9 210.2 4.56 20 2350 4.7 48.5 -
159.6 380 862.4 489.3 489.3 5.7 20 2350 4.7 45.4 -
163.4 380 934.1 - 867.7 6.46 20 2350 4.7 37 -
197.6 380 818.5 840.9 210.2 4.56 20 2350 4.7 41.3 0-
197.6 380 862.4 489.3 489.3 5.7 20 2350 4.7 36.8 -
212.8 380 934.1 - 867.7 6.46 20 2350 4.7 31.2 -
159.8 340 556 1020 238 - 20 2336 3.6 50 -
159.8 340 556 638 596 - 20 2315 3.6 45.3 -
159.8 340 556 319 894 - 20 2295 3.6 44 -
137.1 380 927 869.2 202 - 10 2470 3.7 108 5.7
146.5 380 927 543.2 505.1 - 10 2470 3.7 104.8 5
162.3 380 927 - 1010.2 - 10 2470 3.7 108.5 5.1
138.2 380 927 869.2 195 - 10 2390 4.9 102.5 6.3
149.8 380 927 543.2 487.5 - 10 2390 4.9 103.1 5.1
170.4 380 927 - 975.1 - 10 2390 4.9 100.8 5.9
139.7 380 927 869.2 187.8 - 10 2300 5.9 104.3 5.3
153.1 380 927 543.4 469.4 - 10 2300 5.9 96.8 6.2
175 380 927 - 938.8 - 10 2300 5.9 91.2 4.2
185.4 309 864 848 211 1.0197 16 2380 6.9 42.9 -
191.7 320 817.5 538 538 1.056 16 2380 6.9 42.5 -
201.6 336 785 - 1060 1.1088 16 2380 6.9 40.9 -
192.5 386 829 808 202 2.0458 16 2380 6.9 51.6 -
200 399 795 504 504 2.1147 16 2380 6.9 51.6 -
210 420 738 - 1014 2.226 16 2380 6.9 50.3 -
205 300 697 - 1075 - 20 2450 3.1 35 2.5
205 300 697 - 1027 - 20 2370 7.1 29.2 2.4
205 300 697 - 1027 - 20 2360 7.8 27.7 1.9
180 350 706 - 1089 - 20 2450 3.1 47.6 3.4
180 350 706 - 1041 - 20 2370 7.1 42 2.6
180 350 706 - 1041 - 20 2360 7.8 42.9 2.6
185 425 696 - 1028 - 20 2450 3.1 60 3.9
185 425 696 - 982 - 20 2370 7.1 53.7 3.7
185 425 696 - 982 - 20 2360 7.8 53.2 3.4
165 485 685 - 1039 - 20 2450 3.1 78.2 4.7
165 485 685 - 979 - 20 2370 7.1 71.2 4.1
165 485 685 - 982 - 20 2360 7.8 65.4 4.2
178.3 358 730.4 783.6 299.3 0.3 19 2570 2.7 33.6 3.9
178.3 358 730.4 458.3 598.4 0.3 19 2570 2.7 30.4 3.9
178.3 358 730.4 - 1020 0.3 19 2570 2.7 29.1 3.3
195 300 787.1 756.4 189.1 - 20 2300 5.2 39.5 -
195 300 737.4 485.5 485.5 - 20 2300 5.2 40.8 -
195 300 712.6 - 951.4 - 20 2300 5.2 43.7 -
195 300 814.4 733 183.2 - 20 2300 5.5 41 -
195 300 804.2 450.7 450.7 - 20 2300 5.5 38.8 -
195 300 807.9 - 855.2 - 20 2300 5.5 39.9 -
214.2 210 929 - 966 - 22 2451 7.8 19.7 2
196 280 866 - 940 - 22 2387 6.9 35.7 2.9
161 350 858 - 974 3.5 22 2362 4.2 66.8 4.6
212.1 210 932 - 970 - 22 2456 7.5 21.8 2
193.2 280 870 - 970 - 22 2455 6.4 36.1 2.9
157.5 350 858 - 1029 3.5 22 2496 4.2 68.5 4.8
207.9 210 938 - 953 - 22 2401 7.6 21 2.1
187.6 280 877 - 988 - 22 2484 5.4 41.1 3
150.5 350 868 - 982 3.5 22 2363 3.6 70.2 4.9
205.8 210 943 - 977 - 22 2447 6.9 23.6 2.2
190.4 280 873 - 962 - 22 2458 5.8 39.7 3
157.5 350 858 - 1016 3.5 22 2464 3.9 66.5 5
179 275 878 735 184 - 19 2320 5.3 49.3 4.1
179 275 849 455 455 - 19 2320 5.3 47.5 4.7
179 275 868 - 830 - 19 2320 5.3 53.7 4.9
190 380 714 757 189 - 19 2320 5.3 64.8 4.7
190 380 710 471 471 - 19 2320 5.3 63.5 4.8
190 380 715 - 874 - 19 2320 5.3 65.1 5
179 275 961 740 185 - 19 2320 5.3 64.8 2.5
179 275 978 408 408 - 19 2320 5.3 63.5 2.4
179 275 1010 - 640 - 19 2320 5.3 65.1 2.3
190 380 813 767 192 - 19 2320 5.3 54.9 3.2
190 380 822 426 427 - 19 2320 5.3 51.5 2.7
190 380 836 - 683 - 19 2320 5.3 50.3 2.4
179 325 799 839 210 - 19 2320 5.3 56.5 2.9
179 325 831 490 490 - 19 2320 5.3 48.9 2.6
179 325 825 - 923 - 19 2320 5.3 43.1 2.4
173 385 698 892 233 - 19 2320 5.3 67.4 3.5
173 385 742 515 515 - 19 2320 5.3 61.2 2.9
173 385 746 - 963 - 19 2320 5.3 53.7 2.5
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Author Contributions

M.S.—conceptualization, writing—original draft, and validation; H.L.—data curation, formal analysis, and writing—review & editing; A.A.—software and writing—review & editing; W.A.—data curation and writing—review & editing; K.A.O.—project administration and supervision; F.A.—resources and writing—review & editing; P.J.—formal analysis and investigation; T.M.M.—visualization and writing—review & editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Cracow University of Technology.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

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

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