Next Article in Journal
Mapping Buildings’ Energy-Related Features at Urban Level toward Energy Planning
Previous Article in Journal
Utilization of Building Information Modeling for Arranging the Structural Kingposts
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Compressive Strength Prediction via Gene Expression Programming (GEP) and Artificial Neural Network (ANN) for Concrete Containing RCA

1
Department of Civil Engineering, Abbottabad Campus, COMSATS University Islamabad, Abbottabad 22060, Pakistan
2
Faculty of Civil Engineering, Cracow University of Technology, 24 Warszawska Str., 31-155 Cracow, Poland
3
Department of Civil Engineering, Thammasat School of Engineering, Thammasat University, Khong Luang, Pathumthani 12121, Thailand
4
Department of Civil and Environmental Engineering, Faculty of Engineering, Srinakharinwirot University, Bangkok 10110, Thailand
5
Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
*
Author to whom correspondence should be addressed.
Submission received: 27 June 2021 / Revised: 17 July 2021 / Accepted: 24 July 2021 / Published: 27 July 2021
(This article belongs to the Section Building Materials, and Repair & Renovation)

Abstract

:
To minimize the environmental risks and for sustainable development, the utilization of recycled aggregate (RA) is gaining popularity all over the world. The use of recycled coarse aggregate (RCA) in concrete is an effective way to minimize environmental pollution. RCA does not gain more attraction because of the availability of adhered mortar on its surface, which poses a harmful effect on the properties of concrete. However, a suitable mix design for RCA enables it to reach the targeted strength and be applicable for a wide range of construction projects. The targeted strength achievement from the proposed mix design at a laboratory is also a time-consuming task, which may cause a delay in the construction work. To overcome this flaw, the application of supervised machine learning (ML) algorithms, gene expression programming (GEP), and artificial neural network (ANN) was employed in this study to predict the compressive strength of RCA-based concrete. The linear coefficient correlation (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were evaluated to investigate the performance of the models. The k-fold cross-validation method was also adopted for the confirmation of the model’s performance. In comparison, the GEP model was more effective in terms of prediction by giving a higher correlation (R2) value of 0.95 as compared to ANN, which gave a value of R2 equal to 0.92. In addition, a sensitivity analysis was conducted to know about the contribution level of each parameter used to run the models. Moreover, the increment in data points and the use of other supervised ML approaches like boosting, gradient boosting, and bagging to forecast the compressive strength, would give a better response.

Graphical Abstract

1. Introduction

The utilization trend of aggregate obtained from natural resources increases sharply from the increased manufacturing and usage of concrete in the construction sectors [1,2]. The largest consumers of the natural aggregates are construction industries [3]. A total of 15 billion tons of concrete material is produced worldwide, which equates to about two tons of concrete per resident per annum [4]. To reduce this flaw and manage this demand, the origin of good quality natural aggregates is significantly reducing worldwide [5]. The approximate amount of aggregate used in the European Union countries has reached two billion each year. The activities related to construction demand a high number of natural materials to produce cement and aggregate. However, the construction sectors are an enormous consumer of natural resources, producing huge amounts of waste [6]. The application of raw materials in the construction industry is the key factor that causes environmental risks and pollution to earth [7]. The usage of raw materials has also led to the depletion of minerals as well as natural resources [8]. Resources including cement, fine aggregate, and coarse aggregate will be at a deprived status because these resources cannot manage the increasing demand in the construction industry [9]. Furthermore, sustainable waste management is one of the most crucial matters experienced by the world. Therefore, to minimize the environmental impact and energy consistency of concrete applied to construction work, the utilization of demolition and construction wastes can be favorable for a sustainable engineering approach for the mixed design of concrete. The use of recycled coarse aggregate (RCA) can also be a significant and positive aspect to achieve sustainable construction and reduce environmental risks [10].
The main difference between the natural aggregate and recycled coarse aggregate (RCA) is a certain amount of sticky mortar at the surface of RCA [11]. The properties of RCA vary with certain percentages from the natural aggregate. RCA is generally a porous material, having low saturated surface dry density and bulk density, 2310–2620 kg/m3 and 1290–1470 kg/m3, respectively [12]. The porosity of RCA is due to a high content of adhered mortar on its surface, which also reduces its resistance against the chemical and mechanical effects. In comparison, RCA also shows a high value of water absorption (4% to 9%) as opposed to natural aggregate (1% to 2%) [13]. The porosity and water absorption are normally increased in RCA just because of the amount of adhered mortar [14,15]. The effect on density and absorption capacity is also affected by the adhered mortar. These parameters affect the fresh properties of concrete and reduce the strength properties of concrete. The proper mix design for RCA has assured the acceptable properties of concrete which can be used in several construction projects. The properties of concrete material can also be improved by using other waste materials like silica fume, fly ash, and natural and artificial fibers [16,17,18,19].
Several studies were presented regarding the application of recycled aggregate (RA) in concrete at certain percentages [20,21]. Several properties of concrete were investigated upon the inclusion of RA in concrete, including the fresh properties and mechanical properties of RA-based concrete [22,23,24]. The different qualities of RA were employed in concrete for maintaining or increasing the strength properties of concrete [25,26,27,28]. They also showed that the targeted strength was achieved even at an 80% replacement of coarse aggregate with RCA. Khaldoun et al. [23] worked on the effect of mechanical properties of concrete containing RCA. The compressive strength of the specimens at different ages was calculated to analyze the behavior of concrete. Muzaffer et al. [29] described the mechanical and physical properties of RCA concrete GGBFS, in which they concluded that the split tensile strength was improved when tested at various ages of specimens. Etxeberria et al. [30] showed the influence of RCA and the production process on the properties of recycled aggregate-based concrete. They prepared concrete with 0%, 25%, 50%, and 100% recycled aggregate to investigate the properties. Sumayia et al. reported the mechanical properties of three generations of 100% repetition of RCA. They reported the idea that the repeated RA experienced marginally lower compressive strength than the normal concrete.
Supervised machine learning (ML) techniques are extensively used in the fields of artificial inelegance (AI) and computer science and have a positive reflection in engineering. However, it has gained rapid promotion in the field of civil engineering, especially when it comes to predicting the strength properties of concrete. The supervised ML approaches can be employed, which can predict the outcomes at high accuracy. Ayaz et al. [31] predicted the compressive strength of fly ash-based concrete with individual and ensemble ML approaches. Miao et al. [32] used MLR, SVM, and ANN to foretell the bond strength between the FRPs and concrete, in which they compared the accuracy level of the predictions from the employed techniques. Khoa et al. [33] used ML algorithms to forecast the compressive strength of greenfly ash-based geopolymer concrete. Marjana et al. used different ML techniques for predicting the compressive strength of concrete. The predicted accuracy and the error distribution were analyzed in the study. Ayaz et al. [34] used artificial neural network (ANN), gene expression programming (GEP), and decision tree (DT) techniques to forecast the surface chloride concentration in concrete containing waste material. They indicated that the GEP was a more effective technique for prediction than other employed algorithms. This research also focuses on the application of supervised ML approaches to forecast the compressive strength of recycled coarse aggregate-based concrete. The ANN and GEP algorithms have been investigated to predict the compressive strength of concrete containing recycled aggregate. The various statistical checks, k-fold cross-validation method, and error distribution are included to confirm the model performance. The focus of this study is on the application of supervised machine learning algorithms (gene expression programming and artificial neural network) to predict the compressive strength of concrete containing recycled coarse aggregate (RCA) of 344 data points. The aim of this research also describes the performance of gene expression programming (GEP) and an artificial neural network (ANN) in terms of the correlation coefficient (R2) value. The statistical checks, evaluation of errors (MAE, MSE, and RMSR), k-fold cross-validation, and sensitivity analysis were also involved to evaluate the performance of both GEP and ANN models. This study can be useful for researchers in the field of civil engineering to foretell the strength properties without consuming more time on practical work in the laboratory.

2. Data Description

Supervised machine learning algorithms require various input variables to give the output predicted variable. The data used in this study to forecast the compressive strength of recycled coarse aggregate-based concrete were taken from previously published literature and can be seen in Appendix A. A total of nine parameters including water, cement, sand, natural coarse aggregate, recycled coarse aggregate (RCA), superplasticizers, size of RCA, the density of RCA, and water absorption of RCA were taken as input for running the models, and one variable, compressive strength, was taken as an outcome for the models. Several input parameters and the total number of data points greatly influence the model’s outcome. A total of 344 data points (mixes) for the prediction of RCA-based concrete were used in the study. Anaconda software was introduced to run the model for ANN using python coding, while the GEP model was run on the GEP software. The relative frequency distribution of each parameter used for the mixes can be seen in Figure 1. The descriptive statistical analysis for all the parameters is listed in Table 1. The flowchart of the research approach can be seen in Figure 2.

3. Methodology

Two algorithms (GEP and ANN) were introduced in the study to predict the compressive strength of RAC. Spyder 4.1.1 was selected in the Anaconda navigator to run the model for the artificial neural network (ANN) using python coding. However, the GEP, which is the computer-based software, was adopted for modeling to give a predicted compressive result for the concrete containing recycled coarse aggregate. The GEP and ANN used nine parameters as input and one parameter (compressive strength) as the output during the modeling. The predicted outcome from both models presented the correlation coefficient (R2) value, which is an indication of the accuracy level. The R2 value normally ranges from 0–10, and a higher R2 value indicates a high accuracy between the actual and predicted result. Gene expression programming is from the family of evolutionary algorithms and is generally associated with genetic programming. GEP being from the evolutionary algorithms, can design computer programs and models. Computer programming is considered as a composite tree-like structure that learns and alters by substituting their shapes, compositions, and sizes similar to living organisms. The GEP computer program is included in simple linear chromosomes of fixed length. GEP consists of five components: terminal set, function set, controlee variable, fitness function, and terminate condition. Ferreira presents GEP in 2006, which is a modified form of genetic programming (GP) and depends on the population evolutionary theorem. An exceptional tempering in GEP was that the single gene must be transferred to another generation and has no need to reproduce and mutate the complete structure since every alteration takes place in a linear and simple structure. Each gene in GEP contains a fixed-length variable having terminal sets and arithmetic operations as a set of functions. GEP makes it possible to learn the complex data in the form of input and gives the resulting output in a simple and easy manner. An artificial neural network (ANN) is generally a segment of a computing system that is designed in such a way that it can simulate just like the human brain and inspect and execute a set of information. ANN is the foundation of artificial intelligence (AI), which can resolve problems that would seem difficult or impossible for a human. It is also comprised of self-learning potential, which permits them to generate better results. ANN is designed like a human brain having neuron nodes interrelated just like a web. The brain consists of hundreds of billions of cells known as neurons. Every neuron is prepared with a cell body that is accountable for executing the information by taking information towards and away from the brain. The application of ANN is reflected in every industry and field to predict required outcomes.

4. Results and Their Analyses

4.1. Statistical Analysis

The statistical analysis representation between the actual and predicted outcomes (for compressive strength of RCA-based concrete) from the GEP and ANN models along their error distribution can be seen in Figure 3. The GEP gives high accuracy and less variance between the actual and predicted output. The coefficient correlation (R2) value equals 0.95 and is an indication of its high performance towards the prediction of the result, as shown in Figure 3a. The scattering of errors for the GEP model is also illustrated in Figure 3b. The error distribution in Figure 3b represents that the maximum, minimum, and average values of the training set were 22.37 MPa, 0.00 MPa, and 1.84 MPa, respectively. However, 21.73% of the error data lies below 1 MPa, and 22.96% of the data represented the errors between 2 MPa and 5 MPa. However, only 6.97% of the data lies above the 5 MPa.
The result of the ANN model is also in the acceptable range with less variance as opposed to the GEP model’s result. The relationship between the actual and predicted result from the ANN model with the value of R2 equal to 0.92 can be seen in Figure 3c. The distribution of the errors for the ANN model can be seen in Figure 3d. Figure 3d gives the information of the training set of the ANN model, indicating maximum and minimum values of 21.44 MPa and 0.1 MPa, respectively, while giving an average value of 2.72 MPa. In addition, 21.73% of error data lies below 1 MPa, and 36.23% of data lies between 2 MPa and 5 MPa. However, only 7.24% of the error data indicated above the 5 MPa.

4.2. K-Fold Cross-Validation

The authenticity of the model’s execution was analyzed through the k-fold cross-validation method. To examine the model’s validity, the k-fold cross-validation process is normally adopted, in which the required data has been arranged randomly and divided into ten groups. The nine groups need to be allocated for training and the remaining one for the model’s validation. The procedure also needs repetition (ten times) to have an average output. This detailed process of the k-fold cross-validation results in the high accuracy of the models. In addition, the statistical checks in the form of the error’s (MSE, MAE, and RMSE) evaluation have also been carried out, as illustrated in Table 2. The response of the models towards the prediction was also checked through the statistical analysis, illustrated in the form of the equations stated below. (Equations (1)–(5))
R M S E = i = 1 n   e x i m o i 2 n
M A E = i = 1 n e x i m o i n
R S E = i = 1 n m o i e x i 2 i = 1 n e x ¯ e x i 2
R R M S E = 1 e i = 1 n e x i m o i 2 n
R = i = 1 n e x i e x ¯ i m o i m o ¯ i i = 1 n e x i e x ¯ i 2 i = 1 n m o i m o ¯ i 2
where,
  • e x i = experimental value,
  • m o i = predicted value,
  • e x ¯ i = mean experimental value,
  • m o ¯ i = mean predicted value obtained by the model,
  • n = number of samples.
The resulting evaluation of the k-fold cross-validation comprised of four parameters, including the coefficient correlation (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE), and their distribution can be seen in Figure 4. The lesser error of the GEP model with a high value of R2 indicates the better performer for prediction of outcome. The maximum, minimum, and average values of R2 for the GEP model were equal to 0.77, 0.00, and 0.49, respectively, as shown in Figure 4a. Similarly, the same values of R2 for the ANN model were 2.05, 0.00, and 0.68, as depicted in Figure 4b. However, the maximum values of the MAE, MSE, and RMSE for the GEP model were 14.37 MPa, 14.11 MPa, and 3.76 MPa, respectively, as illustrated in Figure 4a, while the validation result for the ANN model gave maximum values of MAE, MSE, and RMSE as 16.80 MPa, 20.89 MPa, and 4.57 MPa, respectively, as shown in the Figure 4b. The minimum values of the errors (MAE, MSE, and RMSE) for the GEP model were 6.21 MPa, 8.17 MPa, and 2.86 MPa, as reflected in Figure 4a, while for ANN, these values were 5.86 MPa, 4.96 MPa, and 2.23 MPa, as depicted in Figure 4b. Additionally, the validation result for the GEP and ANN models and the statistical checks for both, employing the supervised machine learning algorithms, are illustrated in Table 2 and Table 3, respectively.

5. Sensitivity Analysis

This analysis refers to the effect of parameters on predicting the compressive strength of concrete containing recycled coarse aggregate, as depicted in Figure 5. The input parameters have a significant effect on forecasting the outcomes. The figure illustrates that the highest contributor was the recycled coarse aggregate (RCA) at 41.1%, while the other two main contributors were natural coarse aggregate (NCA) and water at 25% and 20%, respectively. However, the contribution of the other variables was less, and for cement, it showed a 3.8% contribution, fine aggregate 2.3%, superplasticizers 2.6%, the size of coarse aggregate 1.9%, the density of RCA 2%, and water absorption showed 1.3% contribution towards the prediction of the compressive strength of RCA-based concrete. The following equation was used to calculate the contribution of each variable towards the model’s output.
N i =   f m a x x i   f m i n x i
S i = N i j i n N j
where, f m a x x i and f m i n x i are the maximum and minimum of the estimated output over the ith output.

6. Discussion

This research describes the application of supervised machine learning (ML) techniques to foretell the strength property (compressive strength) of recycled coarse aggregate-based concrete. The use of recycled aggregates in concrete is to produce effective material and sustainable construction works. The ML approaches used in this study were gene expression programming (GEP) and an artificial neural network (ANN). The predictive performance of both algorithms was compared to evaluate the better predictor. The GEP model’s outcome was more accurate by indicating the coefficient correlation (R2) value equal to 0.95 as opposed to the ANN model’s outcome which gave an R2 value equal to 0.92. The performance of both models was also confirmed from the statistical checks and k-fold cross-validation method. The lesser values of the errors indicate the high performance of the employed model. Moreover, the sensitivity analysis was also carried out to know about the contribution of each parameter towards the prediction of the compressive strength of concrete containing recycled coarse aggregate. The performance of the models can be affected by the input parameters used to run the model and the number of data points. The contribution level from the sensitivity analysis of all the nine input parameters towards the forecasted result indicates the high contributor parameter.

7. Conclusions and Future Recommendations

This study describes the application of supervised machine learning approaches to predict the compressive strength of concrete containing recycled coarse aggregate (RCA). The gene expression programming (GEP) and artificial neural network (ANN) algorithms were employed for forecasting the compressive strength of concrete. The GEP model was more effective in terms of prediction as compared to the ANN model, which is confirmed from its higher value of linear correlation coefficient (R2) and lesser values of the errors. The following conclusions can be drawn.
The results of the GEP model indicate the high performance towards the prediction of concrete containing recycled coarse aggregate (RCA) as opposed to the ANN model.
The results from the ANN model are also in the acceptable range and can be used for predicting the outcomes.
The high performance of the GEP model has also been confirmed from statistical checks and the k-fold cross-validation process.
The application of GEP and ANN was proposed in this study to predict the strength property of concrete. The use of ML approaches can predict the strength properties without casting the samples in the laboratory. However, the use of other supervised machine learning algorithms would give a better idea about the accuracy of the employed ML techniques.
The RCA also showed a significant effect (41.1%) towards predicting the concrete’s compressive strength compared to other input variables.
It would be easier to understand the effect of the models by making comparisons of more than two algorithms towards the prediction of the outcomes.
It is recommended for future research that datasets should be enhanced from experimental work, field tests, and other numerical analyses using different approaches (e.g., Monte–Carlo simulation).
The input parameters can also be increased by adding the environmental effects (e.g., high temperature and humidity) to provide a better response from the models.
The application of the other ensemble ML algorithms (e.g., Adaboost, bagging, and boosting) can be more effective to predict the compressive strength of concrete.

Author Contributions

A.A.: conceptualization, methodology, investigation, formal analysis, modeling, visualization, and writing—original draft preparation. K.C.: funding acquisition, methodology, investigation, formal analysis, writing—reviewing and editing, and supervision. F.F.: resources, methodology, and writing—reviewing and editing. W.A.: methodology, and writing—reviewing and editing. S.S.: conceptualization, methodology, and writing—reviewing and editing. F.A.: conceptualization, methodology, and writing—reviewing and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Thammasat University Research Fund, Contract No. TUFT 59/2564.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

This study was supported by the Thammasat University Research Fund, Contract No. TUFT 59/2564.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Water (kg/m3)Cement (kg/m3)FA (kg/m3)NCA (kg/m3)RCA (kg/m3)SP (kg/m3)SRCA (mm)DRCA (kg/m3)WRCA (%)Strength (MPa)
165370650850.5364.52.222024004.950.6
165370650607.5607.52.222024004.950.8
165370650012152.222024004.950.2
165460575850.5364.52.222024004.960.8
165460575607.5607.52.222024004.961.2
165460575012152.222024004.960.2
165560495850.5364.52.592024004.970.2
165560495607.5607.52.592024004.970.8
165560495012152.592024004.970
180500486.601135.40160044.5
180500001574.30160038.7
180500486.601135.40160046.1
180500001574.30160042.4
180500486.601135.40160052.5
180500001574.30160050.7
180500486.601135.40160045.2
180500001574.30160042
180500486.601135.40160049.6
180500001574.30160045.1
180500509.601135.40160054.4
180500001574.30160048.2
207.640066286315302024105.838.1
207.640066269729802024105.837
207.640066238357302024105.835.8
207.6400662090302024105.834.5
21735366086120902023306.344.9
22935364752751302023306.344.7
241353625099302023306.346.8
23035366185320202023306.343.2
24735364752449602023306.339.7
271353625095902023306.343.3
20635366186421602023306.343
20735364953153102023306.338.1
1653007659052674.982524304.442
1653187396085376.0422524304.441
162325683011236.1752524304.440
160.63805981182524.92021656.862.2
165.438052911751034.92021656.858.4
170.238046011681544.92021656.861.3
175.638032711622544.92021656.860.8
180.9380011625094.92021656.861
22541064284020402025703.545.3
22541064252450602025703.542.5
22541064221081402025703.539.2
2254106420101702025703.537.1
18040070888621502025703.562.4
18040070855453802025703.555.8
1804007080107502025703.542
22541064284020402025703.545.3
22541064252450602025703.542.5
2254106420101702025703.538.1
2343607050110001923904.422.1
1903807050110001923904.425.1
1924007050110001923904.427.2
1814207050110001923904.428.7
1844607050110001923904.429.5
1782648350103003025203.818
1742628300102003025103.915.4
148427760010004.23025203.836.4
15342375509904.13025103.935.7
15244385508853.93025203.844.4
22541064284020402025803.545.3
22541064252450602025803.542.5
2254106420101702025803.538.1
20541066286521002025803.551.7
20541066254152502025803.547.1
2054106620104902025803.543.4
1804007088862155.62025803.562.4
1804007085545385.62025803.556.8
180400708010755.62025803.552.1
1604007299122217.82025803.569.6
1604007295705547.82025803.565.3
160400729011077.82025803.558.5
1753507307112971.682525301.936.7
1753507305084941.682525301.938
17535073009891.682525301.936
1753507307112821.68250032.6
1753507305084691.682524006.230.4
17535073009381.682524006.229.5
190380744.45756.97189.242.662023385.247.4
190380709.54471.13471.122.662023385.247.3
190380714.560874.045.322023385.254.8
1403507325195564.21224206.843.3
1533407235125493.41224006.839.6
1653307155075432.641224006.838.1
1763207085025371.921224006.834.5
1863107024975331.241224006.831.6
1403507325535234.22224208.846.1
1533407235475173.42224208.845.8
1653307155415112.642224208.839.9
1763207085355061.922224208.836.3
1863107025315011.242224208.834.7
186372617.651030.22257.560202400027.2
186372617.65772.67515.550202400026.5
186372617.65515.11772.670202400025.4
186372617.65257.561030.220202400025.1
186372494.12128.78123.530202630026.4
186372370.59128.78247.060202630025.9
186372247.06128.78370.590202630023.5
186372123.53128.78494.120202630015.4
2002707506752001.081924405.818.5
2102707504504001.351924405.818
2202707502256001.621924405.816.5
1653708657602301.481924405.833
1653708655054551.851924405.834.5
1653708652506802.591924405.834
178.5275938.05723.07180.771.925162400531.7
178.5275962.73423.77423.771.925162400532.4
178.52751005.180756.461.925162400530.1
190380794.31750.04187.572.66162400543.7
190380811.37443.71443.712.66162400537.5
190380838.290807.972.66162400540.5
1513356304147201.2661924205.441.4
15634988807921.676161924205.443.9
1613586452818131.35841925003.344.8
15634985708671.25641925003.345.9
172.434015749113030.20052026611.947
172.434015745855850.701752026022.646
172.43401574011190.902252025103.942.5
190.8424770098001924904.841
192.53508000101501924904.833.3
191.752958140103901924904.824.8
1502507628582864.375190026.7
1502507535645644.375190021.5
1502507432798364.375190021.4
150250734011004.375190020
1804006857702573190038.3
1804006765075073190037
1804006672507513190035
18040065909883190033.3
1753250017623.45322263633.2
2223500017784.53222834.235.6
2213500017714.53222924.334.6
1953250017103.25322301537.3
1233000192172833226091.545.4
144325076811523.253225182.754.3
1233250754.41131.63.253225841.654.4
13230001448.25482.7533225941.653.4
18027562588237802023405.320
18029559563563502023405.319
1803106100124002023405.318
18033058587237302023405.323
18035556062362302023405.324
1803725360125202023405.321
18035556087237302023405.325
18038555061361302023405.329
1804095250122602023405.330
18037554486937202023405.339
18040550862462402023405.331
1804264940124102023405.334
193350661106157012201010.940
1943505151061170012201010.938.6
1963503681061283012201010.937.6
19915801061566012201010.938.6
1583506931111593.512201010.953.7
16335053611051773.512201010.951
16835038111002943.512201010.947.8
178350010895823.512201010.945.1
1373507131143613.512201010.964.6
13935055511431833.512201010.965.4
14335039511383043.512201010.963.2
150350011326053.512201010.963
180281802097001023604.738.6
170293648091901022806.238.1
165337841087901022207.839.3
190463621097001023604.760.1
19050062109193.241022806.260.2
18060056708795.041022207.862.8
22053769378213802023304.450.8
22053769364427602023304.444.9
22053769350641402023304.444.6
22053769336855202023304.442.4
2205376937821380202370454
2205376936442760202370456
2205376935064140202370454.4
2205376933685520202370440.6
22053769378213802023903.655.2
22053769364427602023903.653.5
22053769350641402023903.656.9
22053769336855202023903.654.7
22053769378213802023204.650.5
22053769364427602023204.648.9
22053769350641402023204.645.8
22053769336855202023204.640
22053769378213802023903.754.4
22053769364427602023903.750.2
22053769350641402023903.749.5
22053769336855202023903.740.4
22053769378213802023903.545
22053769364427602023903.546.9
22053769350641402023903.551.4
22053769336855202023903.553.2
22053769378213802023803.855.3
22053769364427602023803.855.9
22053769350641402023803.852.6
22053769336855202023803.848
22053769378213802023803.849.1
22053769364427602023803.849.9
22053769350641402023803.850.3
22053769336855202023803.847.5
22053769378213802024003.543.2
22053769364427602024003.553.7
22053769350641402024003.550
22053769336855202024003.543.3
2205376937821380202370452.9
2205376936442760202370449.9
2205376935064140202370453.7
2205376933685520202370446
206413606098702524524.151
206413606098702524524.149
206413606098702524524.148
20641360653749402524524.151
20641360653749402524524.151
20641360653749402524524.151
20641360680524502524524.152
20641360680524502524524.150
20641360680524502524524.149
145.6520577.20104002522607.538.3
145.6520577.20104002522607.532.9
119.6520577.20104002522607.533.2
146.2430653.60103202522607.531.3
146.2430653.60103202522607.528.4
120.4430653.60103202522607.528
145.77339728.8501050.902522607.526.5
145.77339728.8501050.902522607.523.3
118.65339728.8501050.902522607.521.6
144.06294767.340102902522607.521.6
144.06294767.340102902522607.518
117.6294767.340102902522607.518.8
146.91249804.2701045.802522607.516.1
146.91249804.2701045.802522607.513.4
119.52249804.2701045.802522607.513.9
17927587873518402023205.341
17927584945545502023205.344
179275868083002023205.345
19038074475718902023205.350.5
19038071047147102023205.345
190380715087402023205.356
17927596174018502023205.333.5
17927597840840802023205.332
1792751010064002023205.332
19038081376719202023205.344
19038082242642702023205.341
190380836068302023205.341.5
17932579983921002023205.344
17932583149049002023205.341
179325825092302023205.333.5
17338569889222302023205.353.5
17338574251551502023205.354
173385746096302023205.340
159.6380862.4489.3489.35.72023306.141.6
193.8380934.10867.76.462023306.131.4
197.6380862.4489.3489.35.72023306.135.5
231.8380934.10867.76.462023306.126
167.2380862.4489.3489.35.72023205.844.6
193.8380934.10867.76.462023205.836.7
235.6380934.10867.76.462023205.829.5
155.8380818.5840.9210.24.562023603.946.1
159.6380862.4489.3489.35.72023603.945.1
171380934.10867.76.462023603.942.9
190380818.5840.9210.24.562023603.939.3
197.6380862.4489.3489.35.72023603.939.5
205.2380934.10867.76.462023603.937.7
159.6380818.5840.9210.24.562023504.548.1
163.4380862.4489.3489.35.72023504.541
152380934.10867.76.462023504.538.7
193.8380818.5840.9210.24.562023504.542.7
197.6380862.4489.3489.35.72023504.535.4
190380934.10867.76.462023504.531.4
159.6380818.5840.9210.24.562023504.748.5
159.6380862.4489.3489.35.72023504.745.4
163.4380934.10867.76.462023504.737
197.6380818.5840.9210.24.562023504.741.3
197.6380862.4489.3489.35.72023504.736.8
212.8380934.10867.76.462023504.731.2
159.8340556102023802023363.650
159.834055663859602023153.645.3
159.834055631989402022953.644
137.1380927869.220201024703.7108
146.5380927543.2505.101024703.7104.8
162.338092701010.201024703.7108.5
138.2380927869.219501023904.9102.5
149.8380927543.2487.501023904.9103.1
170.43809270975.101023904.9100.8
139.7380927869.2187.801023005.9104.3
153.1380927543.4469.401023005.996.8
1753809270938.801023005.991.2
185.43098648482111.01971623806.942.9
191.7320817.55385381.0561623806.942.5
201.6336785010601.10881623806.940.9
192.53868298082022.04581623806.951.6
2003997955045042.11471623806.951.6
210420738010142.2261623806.950.3
2053006970107502024503.135
2053006970102702023707.129.2
2053006970102702023607.827.7
1803507060108902024503.147.6
1803507060104102023707.142
1803507060104102023607.842.9
1854256960102802024503.160
185425696098202023707.153.7
185425696098202023607.853.2
1654856850103902024503.178.2
165485685097902023707.171.2
165485685098202023607.865.4
178.3358730.4783.6299.30.31925702.733.6
178.3358730.4458.3598.40.31925702.730.4
178.3358730.4010200.31925702.729.1
195300787.1756.4189.102023005.239.5
195300737.4485.5485.502023005.240.8
195300712.60951.402023005.243.7
195300814.4733183.202023005.541
195300804.2450.7450.702023005.538.8
195300807.90855.202023005.539.9
214.2210929096602224517.819.7
196280866094002223876.935.7
16135085809743.52223624.266.8
212.1210932097002224567.521.8
193.2280870097002224556.436.1
157.5350858010293.52224964.268.5
207.9210938095302224017.621
187.6280877098802224845.441.1
150.535086809823.52223633.670.2
205.8210943097702224476.923.6
190.4280873096202224585.839.7
157.5350858010163.52224643.966.5
17927587873518401923205.349.3
17927584945545501923205.347.5
179275868083001923205.353.7
19038071475718901923205.364.8
19038071047147101923205.363.5
190380715087401923205.365.1
17927596174018501923205.364.8
17927597840840801923205.363.5
1792751010064001923205.365.1
19038081376719201923205.354.9
19038082242642701923205.351.5
190380836068301923205.350.3
17932579983921001923205.356.5
17932583149049001923205.348.9
179325825092301923205.343.1
17338569889223301923205.367.4
17338574251551501923205.361.2
173385746096301923205.353.7

References

  1. Cachim, P.B. Mechanical properties of brick aggregate concrete. Constr. Build. Mater. 2009, 23, 1292–1297. [Google Scholar] [CrossRef]
  2. Kumar Mehta, P. Point of view: Reflections about technology choices. Concr. Int. 1970, 69–76. Available online: http://maquinamole.net/EcoSmartConcrete.com/docs/trmehta99.pdf (accessed on 17 June 2021).
  3. Mikulčić, H.; Klemeš, J.J.; Vujanović, M.; Urbaniec, K.; Duić, N. Reducing greenhouse gasses emissions by fostering the deployment of alternative raw materials and energy sources in the cleaner cement manufacturing process. J. Clean. Prod. 2016, 136, 119–132. [Google Scholar] [CrossRef]
  4. Naik, T.R. Sustainability of Concrete Construction. Pract. Period. Struct. Des. Constr. 2008, 13, 98–103. [Google Scholar] [CrossRef] [Green Version]
  5. Barreto Santos, M.; de Brito, J.; Santos Silva, A.; Hawreen, A. Effect of the source concrete with ASR degradation on the mechanical and physical properties of coarse recycled aggregate. Cem. Concr. Compos. 2020, 111, 103621. [Google Scholar] [CrossRef]
  6. Lehmann, S. Optimizing urban material flows and waste streams in urban development through principles of zero waste and sustainable consumption. Sustainability 2011, 3, 155–183. [Google Scholar] [CrossRef] [Green Version]
  7. Massari, S.; Ruberti, M. Rare earth elements as critical raw materials: Focus on international markets and future strategies. Resour. Policy 2013, 38, 36–43. [Google Scholar] [CrossRef]
  8. Northey, S.A.; Mudd, G.M.; Werner, T.T. Unresolved Complexity in Assessments of Mineral Resource Depletion and Availability. Nat. Resour. Res. 2018, 27, 241–255. [Google Scholar] [CrossRef]
  9. Safiuddin, M.; Jumaat, M.Z.; Salam, M.A.; Islam, M.S.; Hashim, R. Utilization of solid wastes in construction materials. Int. J. Phys. Sci. 2010, 5, 1952–1963. [Google Scholar] [CrossRef]
  10. Maghool, F.; Arulrajah, A.; Du, Y.J.; Horpibulsuk, S.; Chinkulkijniwat, A. Environmental impacts of utilizing waste steel slag aggregates as recycled road construction materials. Clean Technol. Environ. Policy 2017, 19, 949–958. [Google Scholar] [CrossRef]
  11. Marie, I.; Quiasrawi, H. Closed-loop recycling of recycled concrete aggregates. J. Clean. Prod. 2012, 37, 243–248. [Google Scholar] [CrossRef]
  12. Al-Ali, A.Y. Assessment of recycled aggregate structural suitability for road construction. In Proceedings of the First International Conference and Exhibition on Quality Control and Quality Assurance of Construction Materials, Dubai, United Arab Emirates, 2001; Available online: https://scholarworks.uaeu.ac.ae/all_thesesTheses.383.https://scholarworks.uaeu.ac.ae/all_theses/383 (accessed on 17 June 2021).
  13. Li, J. Study on Mechanical Behavior of Recycled Aggregate Concrete. Master’s Thesis, Tongji University, Shanghai, China, October 2004. [Google Scholar]
  14. Vázquez, E. Recycled concrete. In Progress of Recycling in the Built Environment; RILEM State-of-the-Art Reports; Springer: Berlin, Germany, 2013; pp. 175–194. [Google Scholar] [CrossRef]
  15. Buck, A.D. Recycled concrete as a source of aggregate. J. Am. Concr. Inst. 1977, 74, 212–219. [Google Scholar] [CrossRef]
  16. Khan, M.; Ali, M. Use of glass and nylon fibers in concrete for controlling early age micro cracking in bridge decks. Constr. Build. Mater. 2016, 125, 800–808. [Google Scholar] [CrossRef]
  17. Arshad, S.; Sharif, M.B.; Irfan-ul-Hassan, M.; Khan, M.; Zhang, J.L. Efficiency of Supplementary Cementitious Materials and Natural Fiber on Mechanical Performance of Concrete. Arab. J. Sci. Eng. 2020, 45, 8577–8589. [Google Scholar] [CrossRef]
  18. Khan, M.; Cao, M.; Ali, M. Effect of basalt fibers on mechanical properties of calcium carbonate whisker-steel fiber reinforced concrete. Constr. Build. Mater. 2018, 192, 742–753. [Google Scholar] [CrossRef]
  19. Cao, Y.F.; Tao, Z.; Pan, Z.; Wuhrer, R. Effect of calcium aluminate cement on geopolymer concrete cured at ambient temperature. Constr. Build. Mater. 2018, 191, 242–252. [Google Scholar] [CrossRef]
  20. Tabsh, S.W.; Abdelfatah, A.S. Influence of recycled concrete aggregates on strength properties of concrete. Constr. Build. Mater. 2009, 23, 1163–1167. [Google Scholar] [CrossRef]
  21. Li, X. Recycling and reuse of waste concrete in China. Part I. Material behaviour of recycled aggregate concrete. Resour. Conserv. Recycl. 2008, 53, 36–44. [Google Scholar] [CrossRef]
  22. Xiao, J.Z.; Lan, Y. Investigation on the tensile behavior of recycled aggregate concrete. Jianzhu Cailiao Xuebao. J. Build. Mater. 2006, 9, 154–158. [Google Scholar]
  23. Rahal, K. Mechanical properties of concrete with recycled coarse aggregate. Build. Environ. 2007, 42, 407–415. [Google Scholar] [CrossRef]
  24. Xiao, J.Z.; Li, J.B. Study on relationships between strength indexes of recycled concrete. Jianzhu Cailiao Xuebao/J. Build. Mater. 2005, 8, 197–201. Available online: https://en.cnki.com.cn/Article_en/CJFDTotal-JZCX20050200H.htm (accessed on 17 June 2021).
  25. Khatib, J.M. Properties of concrete incorporating fine recycled aggregate. Cem. Concr. Res. 2005, 35, 763–769. [Google Scholar] [CrossRef]
  26. Chen, H.J.; Yen, T.; Chen, K.H. Use of building rubbles as recycled aggregates. Cem. Concr. Res. 2003, 33, 125–132. [Google Scholar] [CrossRef]
  27. Tam, V.W.Y.; Tam, C.M.; Wang, Y. Optimization on proportion for recycled aggregate in concrete using two-stage mixing approach. Constr. Build. Mater. 2007, 21, 1928–1939. [Google Scholar] [CrossRef] [Green Version]
  28. Padmini, A.K.; Ramamurthy, K.; Mathews, M.S. Influence of parent concrete on the properties of recycled aggregate concrete. Constr. Build. Mater. 2009, 23, 829–836. [Google Scholar] [CrossRef]
  29. Tüfekçi, M.M.; Çakır, Ö. An Investigation on Mechanical and Physical Properties of Recycled Coarse Aggregate (RCA) Concrete with GGBFS. Int. J. Civ. Eng. 2017, 15, 549–563. [Google Scholar] [CrossRef]
  30. Etxeberria, M.; Vázquez, E.; Marí, A.; Barra, M. Influence of amount of recycled coarse aggregates and production process on properties of recycled aggregate concrete. Cem. Concr. Res. 2007, 37, 735–742. [Google Scholar] [CrossRef]
  31. Ahmad, A.; Farooq, F.; Niewiadomski, P.; Ostrowski, K.; Akbar, A.; Aslam, F.; Alyousef, R. Prediction of compressive strength of fly ash based concrete using individual and ensemble algorithm. Materials 2021, 14, 794. [Google Scholar] [CrossRef]
  32. Su, M.; Zhong, Q.; Peng, H.; Li, S. Selected machine learning approaches for predicting the interfacial bond strength between FRPs and concrete. Constr. Build. Mater. 2021, 270, 121456. [Google Scholar] [CrossRef]
  33. Nguyen, K.T.; Nguyen, Q.D.; Le, T.A.; Shin, J.; Lee, K. Analyzing the compressive strength of green fly ash based geopolymer concrete using experiment and machine learning approaches. Constr. Build. Mater. 2020, 247, 118581. [Google Scholar] [CrossRef]
  34. Ahmad, A.; Farooq, F.; Ostrowski, K.A.; Śliwa-Wieczorek, K.; Czarnecki, S. Application of novel machine learning techniques for predicting the surface chloride concentration in concrete containing waste material. Materials 2021, 14, 2297. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Histograms indicating the relative frequency distribution of the input parameters.
Figure 1. Histograms indicating the relative frequency distribution of the input parameters.
Buildings 11 00324 g001aBuildings 11 00324 g001b
Figure 2. Flowchart of the research approach.
Figure 2. Flowchart of the research approach.
Buildings 11 00324 g002
Figure 3. Numerical analysis results illustrating the relationship among the actual and predicted outcomes and reflection of errors distribution of the models. ANN (a,b); GEP (c,d).
Figure 3. Numerical analysis results illustrating the relationship among the actual and predicted outcomes and reflection of errors distribution of the models. ANN (a,b); GEP (c,d).
Buildings 11 00324 g003aBuildings 11 00324 g003b
Figure 4. Statistical representation for the k-fold cross-validation process. GEP (a); ANN (b).
Figure 4. Statistical representation for the k-fold cross-validation process. GEP (a); ANN (b).
Buildings 11 00324 g004
Figure 5. Sensitivity analysis indicates the contribution of parameters towards the prediction.
Figure 5. Sensitivity analysis indicates the contribution of parameters towards the prediction.
Buildings 11 00324 g005
Table 1. Descriptive analysis of the input parameters.
Table 1. Descriptive analysis of the input parameters.
Parameter’s DescriptionsWaterCement*FA*NCA*RCA*SP*SRCA*DRCA*WRCA
Mean184.62386.86681.89398.07650.741.3219.762231.064.80
Standard Error1.394.4311.0719.9920.370.110.2231.320.12
Median180.00380.00698.00471.00552.000.0020.002362.504.90
Mode220.00380.00693.000.00138.000.0020.002320.005.30
Standard Deviation25.8482.16205.28370.71377.732.054.02580.952.26
Sample Variance667.476750.2842,141.11137,424.94142,682.564.2116.16337,504.805.12
Kurtosis−0.13−0.194.17−1.13−0.320.612.2310.551.07
Skewness−0.010.43−1.820.300.511.360.08−3.450.06
Range153.40442.001010.001448.251726.007.8022.002661.0010.90
Minimum117.60158.000.000.0052.000.0010.000.000.00
Maximum271.00600.001010.001448.251778.007.8032.002661.0010.90
Sum63,510.69133,081.00234,568.66136,937.02223,853.20455.506796.00767,484.001652.80
Count344.00344.00344.00344.00344.00344.00344.00344.00344.00
*FA = Fine aggregate, *NCA = Natural coarse aggregate, *SP = Superplasticizer, *SRCA = Maximum size of recycled coarse aggregate, *DRCA = Density of recycled coarse aggregate, *WRCA = Water absorption of recycled-coarse aggregate.
Table 2. Statistical checks of the GEP and ANN models.
Table 2. Statistical checks of the GEP and ANN models.
Machine Learning AlgorithmsMAEMSERMSE
Gene Expression Programming (GEP)1.849.33.05
Artificial Neural Network (ANN)2.73194.36
Table 3. Analysis of the k-fold cross-validation of ANN and GEP models.
Table 3. Analysis of the k-fold cross-validation of ANN and GEP models.
ANN GEP
K-FoldMAEMSERMSER2K-FoldMAEMSERMSER2
111.7716.134.020.2218.178.812.970.49
25.867.282.700.91214.3714.113.760.70
39.0410.723.270.70310.5712.793.580.77
410.8114.603.821.8249.3110.043.170.43
57.467.232.690.2058.5110.993.320.12
616.8020.894.572.05613.5513.253.640.74
77.5410.343.220.49712.0713.673.700.00
810.7014.503.810.0088.778.222.870.56
98.864.962.230.2696.218.172.860.69
1014.5815.533.940.16107.499.683.110.44
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Ahmad, A.; Chaiyasarn, K.; Farooq, F.; Ahmad, W.; Suparp, S.; Aslam, F. Compressive Strength Prediction via Gene Expression Programming (GEP) and Artificial Neural Network (ANN) for Concrete Containing RCA. Buildings 2021, 11, 324. https://0-doi-org.brum.beds.ac.uk/10.3390/buildings11080324

AMA Style

Ahmad A, Chaiyasarn K, Farooq F, Ahmad W, Suparp S, Aslam F. Compressive Strength Prediction via Gene Expression Programming (GEP) and Artificial Neural Network (ANN) for Concrete Containing RCA. Buildings. 2021; 11(8):324. https://0-doi-org.brum.beds.ac.uk/10.3390/buildings11080324

Chicago/Turabian Style

Ahmad, Ayaz, Krisada Chaiyasarn, Furqan Farooq, Waqas Ahmad, Suniti Suparp, and Fahid Aslam. 2021. "Compressive Strength Prediction via Gene Expression Programming (GEP) and Artificial Neural Network (ANN) for Concrete Containing RCA" Buildings 11, no. 8: 324. https://0-doi-org.brum.beds.ac.uk/10.3390/buildings11080324

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop