Hybrid Artificial Intelligence Model Development for Roller-compacted Concrete Compressive Strength Estimation

Document Type: Original Article


Department of Civil Engineering, Larestan Branch, Islamic Azad University, Larestan, Iran


This study implemented the artificial bee colony (ABC) metaheuristic algorithm to optimize the Artificial Neural Network (ANN) values for improving the accuracy of model and evaluate the developed model. Compressive strength of RCC was investigated using mix design materials in three forms, namely volumetric weight input (cement, water, coarse aggregate, fine aggregate, and binder), value ratio (water to cement ratio, water to binder ratio, and coarse aggregate to fine aggregate ratio), as well as the percentage of mix design values of different ages. A comprehensive, proper-range dataset containing 333 mix designs was collected from various papers. The accuracy of the research models was investigated using error indices, namely correlation coefficient, root-mean-square-error (RMSE), mean absolute error (MAE), and developed hybrid models were compared. External validation and Monte Carlo simulation (MCS)-based uncertainty analysis was also used to validate the models and their results were reported. The experimental stage of the prediction of compressive strength values showed significant accuracy of the ANN-ABC model with (MAE=11.49, RMSE=0.920, RME=5.21) compared to other models in this study. Besides, the sensitivity analysis of predictor variables in this study revealed that the variables “specimen age,” “binder,” and “fine aggregate” were more effective and important in this research. Comparison of the results showed that the improved proposed model using the ABC algorithm was more capable and more accurate in reducing the error rate in providing computational relations compared to the default models examined in the prediction of the compressive strength of RCC and also tried in simplifying computational relations.


1.     Ni, H. G., and Wang, J. Z. “Prediction of compressive strength of concrete by neural networks.” Cement and Concrete Research, Vol. 30, No. 8, (2000), 1245–1250. https://doi.org/10.1016/S0008-8846(00)00345-8
2.     Ashrafian, A., Gandomi, A. H., Rezaie-Balf, M., and Emadi, M. “An evolutionary approach to formulate the compressive strength of roller compacted concrete pavement.” Measurement: Journal of the International Measurement Confederation, Vol. 152,  (2020), 107309. https://doi.org/10.1016/j.measurement.2019.107309
3.     Amlashi, A. T., Alidoust, P., Ghanizadeh, A. R., Khabiri, S., Pazhouhi, M., and Monabati, M. S. “Application of computational intelligence and statistical approaches for auto-estimating the compressive strength of plastic concrete.” European Journal of Environmental and Civil Engineering, (2020). https://doi.org/10.1080/19648189.2020.1803144
4.     Ayaz, Y., Kocamaz, A. F., and Karakoç, M. B. “Modeling of compressive strength and UPV of high-volume mineral-admixtured concrete using rule-based M5 rule and tree model M5P classifiers.” Construction and Building Materials, Vol. 94, (2015), 235–240. https://doi.org/10.1016/j.conbuildmat.2015.06.029
5.     Ashrafian, A., Taheri Amiri, M. J., Rezaie-Balf, M., Ozbakkaloglu, T., and Lotfi-Omran, O. “Prediction of compressive strength and ultrasonic pulse velocity of fiber reinforced concrete incorporating nano silica using heuristic regression methods.” Construction and Building Materials, Vol. 190, (2018), 479–494. https://doi.org/10.1016/j.conbuildmat.2018.09.047
6.     Abdulelah Al-Sudani, Z., Salih, S. Q., sharafati, A., and Yaseen, Z. M. “Development of multivariate adaptive regression spline integrated with differential evolution model for streamflow simulation.” Journal of Hydrology, Vol. 573, (2019), 1–12. https://doi.org/10.1016/j.jhydrol.2019.03.004
7.     Mansouri, I., Kisi, O., Sadeghian, P., Lee, C.-H., and Hu, J. “Prediction of Ultimate Strain and Strength of FRP-Confined Concrete Cylinders Using Soft Computing Methods.” Applied Sciences, Vol. 7, No. 751, (2017), 1–14. https://doi.org/10.3390/app7080751
8.     Kaveh, A., Bakhshpoori, T., and Hamze-Ziabari, S. M. “M5’ and mars based prediction models for properties of selfcompacting concrete containing fly ash.” Periodica Polytechnica Civil Engineering, Vol. 62, No. 2, (2018), 281–294. https://doi.org/10.3311/PPci.10799
9.     Asteris, P. G., Ashrafian, A., and Rezaie-Balf, M. “Prediction of the compressive strength of self-compacting concrete using surrogate models.” Computers and Concrete, Vol. 24, No. 2, (2019), 137–150. https://doi.org/10.12989/cac.2019.24.2.137
10.   Ashrafian, A., Shokri, F., Taheri Amiri, M. J., Yaseen, Z. M., and Rezaie-Balf, M. “Compressive strength of Foamed Cellular Lightweight Concrete simulation: New development of hybrid artificial intelligence model.” Construction and Building Materials, Vol. 230, (2020), 117048. https://doi.org/10.1016/j.conbuildmat.2019.117048
11.   Rao, S. K., and Sravana, P. “Experimental Investigation On Pozzolanic Effect Of Fly Ash In Roller Compacted Concrete Pavement Using Manufactured Sand As Fine Aggregate.” International Journal of Applied Engineering Research, Vol. 10, No. 8, (2015), 20669–20682. Retrieved from http://www.ripublication.com
12.   Rao, S. K., Sravana, P., and Rao, T. C. “Experimental studies in Ultrasonic Pulse Velocity of roller compacted concrete pavement containing fly ash and M-sand Studies in Ultrasonic Pulse Velocity of Roller compacted concrete pavement.” International Journal of Pavement Research and Technology, Vol. 9, No. 4, (2016), 289–301. https://doi.org/10.1016/j.ijprt.2016.08.003
13.   Tangtermsirikul, S., Kaewkhluab, T., and Jitvutikrai, P. “A compressive strength model for roller-compacted concrete with fly ash.” Magazine of Concrete Research, Vol. 56, No. 1, (2004), 35–44. https://doi.org/10.1680/macr.2004.56.1.35
14.   Rao, S. K., Sravana, P., and Rao, T. C. “Experimental studies in ultrasonic pulse velocity of roller compacted concrete containing GGBS and M-sand.” ARPN Journal of Engineering and Applied Sciences, Vol. 11, No. 3, (2016), 2016–1819. Retrieved from www.arpnjournals.com
15.   Ghahari, S. A., Mohammadi, A., and Ramezanianpour, A. A. “Performance assessment of natural pozzolan roller compacted concrete pavements.” Case Studies in Construction Materials, Vol. 7, (2017), 82–90. https://doi.org/10.1016/j.cscm.2017.03.004
16.   Hesami, S., Modarres, A., Soltaninejad, M., and Madani, H. “Mechanical properties of roller compacted concrete pavement containing coal waste and limestone powder as partial replacements of cement.” Construction and Building Materials, Vol. 111, (2016), 625–636. https://doi.org/10.1016/j.conbuildmat.2016.02.116
17.   Atiş, C. D., Sevim, U. K., Özcan, F., Bilim, C., Karahan, O., Tanrikulu, A. H., and Ekşi, A. “Strength properties of roller compacted concrete containing a non-standard high calcium fly ash.” Materials Letters, Vol. 58, No. 9, (2004), 1446–1450. https://doi.org/10.1016/j.matlet.2003.10.007
18.   Mardani-Aghabaglou, A., and Ramyar, K. “Mechanical properties of high-volume fly ash roller compacted concrete designed by maximum density method.” Construction and Building Materials, Vol. 38, (2013), 356–364. https://doi.org/10.1016/j.conbuildmat.2012.07.109
19.   Modarres, A., and Hosseini, Z. “Mechanical properties of roller compacted concrete containing rice husk ash with original and recycled asphalt pavement material.” Materials and Design, Vol. 64, (2014), 227–236. https://doi.org/10.1016/j.matdes.2014.07.072
20.   Pavan, S., and Rao, S. K. “Effect of Fly ash on Strength Characteristics of Roller Compacted Concrete Pavement.” IOSR Journal of Mechanical and Civil Engineering, Vol. 11, No. 6, (2014), 8. Retrieved from www.iosrjournals.org
21.   Rashad, A. M. “A preliminary study on the effect of fine aggregate replacement with metakaolin on strength and abrasion resistance of concrete.” Construction and Building Materials, Vol. 44, (2013), 487–495. https://doi.org/10.1016/j.conbuildmat.2013.03.038
22.   Tropsha, A., Gramatica, P., and Gombar, V. K. “The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models.” In QSAR and Combinatorial Science (Vol. 22, pp. 69–77). Wiley-VCH Verlag. https://doi.org/10.1002/qsar.200390007
23.   Binder, K., and Heermann, D. W. Monte Carlo Simulation in Statistical Physics. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-10758-1
24.   Tung, Y., and Yen, B. Hydrosystems engineering uncertainty analysis. American Society of Civil Engineers, and  McGraw-Hill Book Company. Retrieved from https://cedb.asce.org/CEDBsearch/record.jsp?dockey=0150035
25.   Verbeeck, H., Samson, R., Verdonck, F., and Lemeur, R. “Parameter sensitivity and uncertainty of the forest carbon flux model FORUG: A Monte Carlo analysis.” In Tree Physiology (Vol. 26, pp. 807–817). Oxford University Press. https://doi.org/10.1093/treephys/26.6.807
26.   Chi, M., and Huang, R. “Effect of circulating fluidized bed combustion ash on the properties of roller compacted concrete.” Cement and Concrete Composites, Vol. 45, (2014), 148–156. https://doi.org/10.1016/j.cemconcomp.2013.10.001
27.   Kuddus, M. A., and Dey, P. P. “Cost Analysis of RCC, Steel and Composite Multi-Storied Car Parking Subjected to High Wind Exposure in Bangladesh.” Civil Engineering Journal, Vol. 3, No. 2, (2017), 95–104. https://doi.org/10.28991/cej-2017-00000076
28.   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. https://doi.org/10.28991/esj-2018-01137
29.   Karimnader-Shalkouhi, S., Karimpour Fard, M., and Machado, S. “An ANN Based Sensitivity Analysis of Factors Affecting Stability of Gravity Hunched Back Quay Walls.” Civil Engineering Journal, Vol. 3, No. 5, (2017), 301–318. https://doi.org/10.28991/cej-2017-00000092
30.           Jalali, M., Pasbani Khiavi, M., and Ghorbani, M. A. “Investigation of to the Effect of Bedrock Stiffness on Seismic Behaviour of Roller Compacted Concrete Dam.” Civil Engineering Journal, Vol. 3, No. 8, (2017), 626–639. https://doi.org/10.28991/cej-2017-00000117