Effects of Drying Temperature and Aggregate Shape on the Concrete Compressive Strength: Experiments and Data Mining Techniques

Document Type : Original Article


1 Department of Mechanical and Instrumental Engineering, Academy of Engineering, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, Moscow, Russian Federation

2 School of Science & Engineering, Division of Solid Mechanics, Sharif University of Technology, International Campus, Kish Island, Iran


The main purpose of this paper is to assess the impact of the geometry and size of the aggregate, as well as the drying temperature on the compressive strength of the ordinary concrete. To this end, two aggregates with sharp and round corners were prepared in three different aggregate sizes. After preparing concrete samples, the drying operations were carried out in the vicinity of room temperature, cold wind, and hot wind. Next, the linear relationship between the concrete strength and the studied parameters was estimated using Multiple Linear Regression (MLR) method. Finally, the Taguchi Sensitivity Analysis (TSA) and Decision Tree Analysis (DTA) were applied in order to determine the importance of the parameters on the compressive strength of concrete. As a result, it is obtained that the aggregate size has the greatest influence on the compressive strength of the ordinary concrete followed by drying temperature as stated by method TSA and DTA. In addition, the influence percentages reported for each parameter by Taguchi approach and decision tree method are matched. The prediction of the strength obtained by Taguchi method and second-order regression with the experimental data are in a good agreement. It was concluded that the impact of drying temperature on the concrete strength is several times greater than the effect of the aggregate geometry. Finally, the main conclusion of this research is related to the application of cold wind for drying operation, which leads to an increase of the compressive strength by 8.67% and 11.55% for ordinary concrete containing a constant aggregate size of 20 and aggregate geometries of round and sharp corners, respectively.


1.   Meguid, S. A., Benidir, A., Mahdad, M. H. and Brara, A., “Aggregate size and lateral dimension effects on core compressive strength of concrete”, Proceedings IRF2018: 6th International Conference Integrity-Reliability-Failure, (2018).
2.   Ogundipe, O. M., Olanike, A. O., Nnochiri, E. S. and Ale, P. O., “Effects of coarse aggregate size on the compressive strength of concrete”, Civil Engineering Journal, Vol. 4, No. 4, (2018), 836-842. http://dx.doi.org/10.28991/cej-0309137
3.     Kılıç, A., Teymen, A., Özdemir, O. and Atiş, C. D., “Estimation of Compressive Strength of Concrete Using Physico-Mechanical Properties of Aggregate Rock”, Iranian Journal of Science and Technology - Transactions of Civil Engineering, Vol. 43, (2019), 171-178. https://doi.org/10.1007/s40996-018-0156-6
4.     Li, M., Hao, H., Shi, Y., Hao and Y., “Specimen shape and size effects on the concrete compressive strength under static and dynamic tests”, Construction and Building Materials, Vol. 161, (2018), 84-93.
5.     Nguyen, C. T. and Aniskin, N. A., “Temperature regime during the construction massive concrete with pipe cooling”, Magazine of Civil Engineering, Vol. 89, No. 5, (2019), 156-166. DOI: 10.18720/MCE.89.13
6.     Kotov, D., “Shrinkage deformations of the concrete modified by chemical and fine mineral additives”, Magazine of Civil Engineering, Vol. 7, (2009), 11-21. DOI: 10.18720/MCE.9.5
7.     Zinevich, L., “Application of numerical modeling at technology designing of heating and solidification of concrete in monolithic structures”, Magazine of Civil Engineering, Vol. 2, (2011), 24-28. DOI: 10.18720/MCE.20.5
8.     Darayani, D. H., Tavio, T., Raka I. G. P. and Puryanto, P., “The effect of styrofoam artificial lightweight aggregate (ALWA) on compressive strength of self compacting concrete (SCC)”, Civil Engineering Journal, Vol. 4, No. 9, (2018), 211-2123. http://dx.doi.org/10.28991/cej-03091143
9.     Buller, A. H., Memon, B. A. and Oad, M., “Effect of 12-hour fire on flexural behavior of recyclable aggregate reinforced concrete beams”, Civil Engineering Journal, Vol. 5, No. 7, (2019), 1533–1542. http://dx.doi.org/10.28991/cej-2019-03091350
10.   Khademi, F. and Behfarnia, K., “Evaluation of concrete compressive strength using artificial neural network and multiple linear regression models”, International Journal of Optimization in Civil Engineering, Vol. 6, No. 3, (2016), 423-432.
11.   Nikoo, M., Zarfam, P. and Sayahpour, H., “Determination of compressive strength of concrete using Self Organization Feature Map (SOFM)”, Engineering with Computers, Vol. 31, No. 1, (2013), 113-121. https://doi.org/10.1007/s00366-013-0334-x
12. Nikoo, M., Torabian Moghadam, F. and Sadowski, L., “Prediction of concrete compressive strength by evolutionary artificial neural networks”, Advances in Materials Science and Engineering, Vol. 2015, (2015), 1-8.
13.   Khademi, F., Akbari, M., Jamal, S. M. and Nikoo, M., “Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete”, Frontiers of Structural and Civil Engineering, Vol. 11, No. 1, (2017), 90–99. https://doi.org/10.1007/s11709-016-0363-9
14.   Young, B. A., Hall, A., Pilon, L., Gupta, P. and Sant, G., “Can the compressive strength of concrete be estimated from knowledge of the mixture proportions?: New insights from statistical analysis and machine learning methods”, Cement and Concrete Research, Vol. 115, (2019), 379-388. https://doi.org/10.1016/j.cemconres.2018.09.006
15.   Rastegarian, S. and Sharifi, A., “An investigation on the correlation of inter-story drift and performance objectives in conventional RC frames”, Emerging Science Journal, Vol. 2, No. 3, (2018), 140-147. http://dx.doi.org/10.28991/esj-2018-01137
16.   Silva, R. V., De Brito, J. and Dhir, R. K., “The influence of the use of recycled aggregates on the compressive strength of concrete: A review”, European Journal of Environmental and Civil Engineering, Vol. 19, No. 7, (2015), 825-849. https://doi.org/10.1080/19648189.2014.974831
17.   Deng, F., He, Y., Zhou, S., Yu, Y., Cheng, H. and Wu, X., “Compressive strength prediction of recycled concrete based on deep learning”, Construction and Building Materials, Vol. 175, (2018), 562-569. https://doi.org/10.1016/j.conbuildmat.2018.04.169
18.   Nour, A. I. and Güneyisi, E. M., “Prediction model on compressive strength of recycled aggregate concrete filled steel tube columns”, Composites Part B: Engineering, Vol. 173, (2019), 106938.
19.   Kazemi, M., Madandoust, R. and de. Brito, J., “Compressive strength assessment of recycled aggregate concrete using Schmidt rebound hammer and core testing”, Construction and Building Materials, Vol. 224, (2019), 630-638. https://doi.org/10.1016/j.conbuildmat.2019.07.110
20.   Mohamed, A. M., “Influence of nano materials on flexural behavior and compressive strength of concrete”, HBRC Journal, Vol. 12, No. 2, (2016), 212-225. https://doi.org/10.1016/j.hbrcj.2014.11.006
21.   Sorathiya, J., Shah, S. and Kacha, S., “Effect on Addition of Nano “Titanium Dioxide” (TiO2) on Compressive Strength of Cementitious Concrete”, Kalpa Publications in Civil Engineering, Vol. 1, (2017), 219-225. https://doi.org/10.29007/sq9d
22.   Chithra, S., Kumar, S. R. R. S., Chinnaraju, K. and Alfin Ashmita, F., “A comparative study on the compressive strength prediction models for High Performance Concrete containing nano silica and copper slag using regression analysis and Artificial Neural Networks”, Construction and Building Materials, Vol. 114, (2016), 528-535. https://doi.org/10.1016/j.conbuildmat.2016.03.214
23.   Bui, D. K., Nguyen, T., Chou, J. S., Nguyen-Xuan, H. and T. D. Ngo, “A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete”, Construction and Building Materials, Vol. 180, (2018), 320-333. https://doi.org/10.1016/j.conbuildmat.2018.05.201
24.   Vakhshouri, B. and Nejadi, S., “Prediction of compressive strength of self-compacting concrete by ANFIS models” Neurocomputing, Vol. 280, (2018), 13-22. https://doi.org/10.1016/j.neucom.2017.09.099
25.   ACI-211.1-91, “Standard practice for selecting proportions for normal, heavyweight, and mass concrete”, American Concrete Institute, (2002).
26.   ISO-1920-3, “Testing of concrete-part 3: Making and curing test specimens”, International Standard Organization, (2004).
27.   ISO-1920-4, “Testing of concrete-part 4: Strength of hardened concrete”, International Standard Organization, (2005).
28.   Brown, A., “Reinforced concrete”, Wilmott, Vol. 82, (2016), 8-13. https://doi.org/10.1002/wilm.10519
29.   Farrahi, G. H., Reza Kashyzadeh, K., Minaei, M., Sharifpour, A. and Riazi, S., “Analysis of resistance spot welding process parameters on the weld quality of three-steel sheets used in automotive industry: Experimental and finite element simulation”, International Journal of Engineering, IJE TRANSACTION A: Basis, Vol. 33, No. 1, (2020), 148-157. doi: 10.5829/ije.2020.33.01a.17
30.   Omidi-bidgoli, M., Reza-Kashyzadeh, K. and Amiri-Asfarjani, A., “Estimation of critical dimensions for the crack and pitting corrosion defects in the oil storage tank using finite element method”, International Journal of Solid Materials, Vol. 1, No.1, (2019), 1-26.
31.   Maleki, E. Unal, O. and Kashyzadeh, K. R., “Efficiency analysis of shot peening parameters on variations of hardness, grain size and residual stress via taguchi approach”, Metals and Materials International, Vol. 25, No. 6, (2019), 1436-1447. https://doi.org/10.1007/s12540-019-00290-7
32.   Mehmed, K. and Hoboken, N., “Data Mining: Concepts, Models, Methods, and Algorithms”, Technometrics, Vol. 45, No. 3, (2003), 277–277.
33.   Coops, N. C., Waring, R. H., Beier, C., Roy-Jauvin, R. and Wang, T., “Modeling the occurrence of 15 coniferous tree species throughout the Pacific Northwest of North America using a hybrid approach of a generic process-based growth model and decision tree analysis”, Applied Vegetation Science, Vol. 14, No. 3, (2011), 402-414. https://doi.org/10.1111/j.1654-109X.2011.01125.x
34.   Ajamu, S. O. and Ige, J. A., “Effect of Coarse Aggregate Size on the Compressive Strength and the Flexural Strength of Concrete Beam”, Journal of Engineering Research and Applications, Vol. 5, No. 4, (2015), 2248–2267.
35.   Roy, B., Vilane, T. and Sabelo, N., “The Effect of Aggregate Size on the Compressive Strength of Concrete”, Journal of Agricultural Science and Engineering, Vol. 2, No. 6, (2016), 66-69.
36.  Polat, R., Yadollahi, M. M., Sagsoz, A. E., Arasan, S., “The Correlation between Aggregate Shape and Compressive Strength of Concrete: Digital Image Processing Approach”, International Journal of Structural and Civil Engineering, Vol. 2, No. 3, (2013), 62-80.
37.   Lie, H. A., Nurhuda, I. and Setiawan, Y., “The Effect of Aggregate Shape and Configuration to the Concrete Behavior”, Smart Science, Vol. 2, No. 2, (2014), 85-90. https://doi.org/10.1016/j.jobe.2019.100871
38.           Zarehparvar-Shoja, M. and Eskandari-Naddaf, H., “Optimizing compressive strength of Micro- and Nano-silica concrete by statistical method”, Civil Engineering Journal, Vol. 3, No. 11, (2017), 1084-1096. http://dx.doi.org/10.28991/cej-030939