Silva et al 2020a - Revision history

Rimni at 14:35, 2 October 2020

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Rimni at 14:31, 2 October 2020

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Rimni at 14:22, 2 October 2020

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Rimni at 12:51, 2 October 2020

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Rimni at 12:43, 2 October 2020

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Rimni at 12:17, 2 October 2020

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Rimni at 14:23, 1 October 2020

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Rimni at 13:48, 1 October 2020

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−	[[#_Ref45643995\|Figure 4]] presents the correlation matrix of the data used in the model. The correlation matrix shows no correlation between the variables used in the computational model. Therefore, it can be said that the components are mostly independent of each other.~~</span>~~	+	[[#_Ref45643995\|Figure 4]] presents the correlation matrix of the data used in the model. The correlation matrix shows no correlation between the variables used in the computational model. Therefore, it can be said that the components are mostly independent of each other.

	<div id='_Ref45643995'></div>		<div id='_Ref45643995'></div>

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+[9] Feng D.C., Liu Z.T., Wang X.D., Chen Y., Chang J.Q., Wei D.F., Jiang Z.M. Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach. Constr. Build. Mater., 230:117000, 2020.
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 ==References==
 [1] Hibbeler R.C.C. Resistência dos materiais. 7th ed., Pearson, São Paulo, SP, 2010.
@@ Line 583: / Line 584: @@
 [46] Kaveh, A., Kalateh-Ahani, M., Fahimi-Farzam, M., Constructability optimal design of reinforced concrete retaining walls using a multi-objective genetic algorithm, Struct. Eng. Mech. 47 (2013) 227–245.

@@ Line 480: / Line 480: @@
 This work aimed to present the study of computational intelligence applied to define the concrete compressive strength from a database obtained in the studies of Yeh [22]. Three computational methods of machine learning and artificial intelligence were used, namely Random Forest, Support Vector Machines and Artificial Neural Networks. Data pre-processing and data visualization methods were also used to improve the results. Preprocessed methods known as GridsearchCV and cross-validation were employed to improve the performance of machine learning methods.
-The obtained results show that the Random Forest gave the best performance (RMSE = 5.614 and R² = 0.902) and an average execution time. It can be noted that the SVM method had the worst performance of the three used methods but presented the shortest execution time. On the other hand, the ANN showed the second-best result and the longest execution time. The overall error rate can be considered low and the techniques can adequately be used to predict the concrete compressive strength, staying within the acceptable safety range for engineering practices.
+The obtained results show that the Random Forest gave the best performance (<math>R^2 = 0.902</math> and <math>RMSE = 5.614</math>) and an average execution time. It can be noted that the SVM method had the worst performance of the three used methods but presented the shortest execution time. On the other hand, the ANN showed the second-best result and the longest execution time. The overall error rate can be considered low and the techniques can adequately be used to predict the concrete compressive strength, staying within the acceptable safety range for engineering practices.
 The computational intelligence models used are reliable to solve different complex problems, such as prediction problems. These models can be used to solve a specific problem when a deviation in available data is expected and accepted, and when a defined methodology is not available. Therefore, to predict the properties of concrete with high reliability, instead of using expensive experimental investigation, conventional and innovative models can be replaced by computational intelligence models.
@@ Line 486: / Line 486: @@
 Computational intelligence models can be used to predict the compressive strength of concrete specimens, as shown in this study. The prediction of mean percent error values for these simulations shows a high degree of consistency with compressive strength and is experimentally evaluated from the concrete specimens used. Thus, the present study suggests an alternative approach to evaluate compressive strength against destructive testing methods.
-==5. Acknowledgments==
+==Acknowledgments==
 CAPES and CEFET-MG supported the work described in this paper.
-==6. References==
+==References==
-[1] Hibbeler, R. C. C., Resistência dos Materiais, 7th ed., Pearson, São Paulo, SP, 2010.
+[1] Hibbeler R.C.C. Resistência dos materiais. 7th ed., Pearson, São Paulo, SP, 2010.
-[2] Babanajad, S. K., Application of genetic programming for uniaxial and multiaxial modeling of concrete, Springer, Switzerland, 2015.
+[2] Babanajad S.K. Application of genetic programming for uniaxial and multiaxial modeling of concrete. Springer, Switzerland, 2015.
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-[8] Rajeshwari, R., Mandal, S., Prediction of compressive strength of high-volume fly ash concrete using artificial neural network, Lect. Notes Civ. Eng. 25 (2019) 471–483.
+[8] Rajeshwari R., Mandal S. Prediction of compressive strength of high-volume fly ash concrete using artificial neural network. Lect. Notes Civ. Eng., 25:471–483,  2019.
 [9] Feng, D. C., Liu, Z. T., Wang, X.D., Chen, Y., Chang, J. Q., Wei, D. F., Jiang, Z. M., Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach, Constr. Build. Mater. (2020).

@@ Line 446: / Line 446: @@
 |  style="text-align: center;"|<span style="text-align: center;">5.5</span>
 |-
-|  rowspan='3' style="text-align: center;"|<span style="text-align: center;">Young et al.[21]</span>
+|  rowspan='3' style="text-align: center;"|<span style="text-align: center;">Young et al. [21]</span>
 |  style="text-align: center;"|<span style="text-align: center;">Random Forest</span>
 |  style="text-align: center;"|<span style="text-align: center;">0.86</span>

@@ Line 459: / Line 459: @@
 |  style="text-align: center;"|<span style="text-align: center;">6.4</span>
 |-
-|  rowspan='3' style="border-bottom: 1pt solid black;text-align: center;"|<span style="text-align: center; ">This paper</span>
+|  rowspan='3' style="text-align: center;"|<span style="text-align: center; ">This paper</span>
 |  style="text-align: center;"|<span style="text-align: center;">Random Forest</span>
 |  style="text-align: center;"|<span style="text-align: center;">0.90</span>

@@ Line 398: / Line 398: @@
 {| class="wikitable" style="margin: 1em auto 0.1em auto;border-collapse: collapse;font-size:85%;width:auto;"
 |-style="text-align:center"
-! !! <math>R^2</math> !! 'RMSE  (MPa)!! Execution time (seconds)
+! !! <math>R^2</math> !! RMSE  (MPa)!! Execution time (seconds)
 |-
 |  style="text-align: center;"|<span style="text-align: center;">Random forest</span>