As concerns on environmental issues are consistently escalating in perspective of establishing sustainable and environmental-friendly construction in the construction industry, one prominent approach to address this agenda is the application of Recycled Aggregate Concrete (RAC) to concrete production. However, this particular concrete, possesses different physical and mechanical properties from those of normal concrete, raising the difficulty of proper understanding and implementation in real-world practice. Accordingly, these shortcomings have drawn the inspiration in this research aim to devote efforts in deriving an accurate formulation of RAC elastic modulus which is contemplated as one crucial problem relevant to RAC properties. Furthermore, this research proposed Symbiotic Polyhedron Operation Tree (SPOT) which incorporated Symbiotic Organisms Search 2.0 and Polyhedron Operation Tree together to develop the prediction model. From result findings and several analyses conducted in further discussions of this research, proposed model has shown superiority among other applied methods in terms of stability and robustness. SPOT model provides the best RMSE, MAE, MAPE, R and R2 values in testing data with significant difference to the other applied models. Hence, this model conclusively demonstrated an auspicious capability in prediction performance, especially within this research scope of RAC.

As concerns on environmental issues are consistently escalating in perspective of establishing sustainable and environmental-friendly construction in the construction industry, one prominent approach to address this agenda is the application of Recycled Aggregate Concrete (RAC) to concrete production. However, this particular concrete, possesses different physical and mechanical properties from those of normal concrete, raising the difficulty of proper understanding and implementation in real-world practice. Accordingly, these shortcomings have drawn the inspiration in this research aim to devote efforts in deriving an accurate formulation of RAC elastic modulus which is contemplated as one crucial problem relevant to RAC properties. Furthermore, this research proposed Symbiotic Polyhedron Operation Tree (SPOT) which incorporated Symbiotic Organisms Search 2.0 and Polyhedron Operation Tree together to develop the prediction model. From result findings and several analyses conducted in further discussions of this research, proposed model has shown superiority among other applied methods in terms of stability and robustness. SPOT model provides the best RMSE, MAE, MAPE, R and R2 values in testing data with significant difference to the other applied models. Hence, this model conclusively demonstrated an auspicious capability in prediction performance, especially within this research scope of RAC.

American Concrete Pavement Association (ACPA). (2009). Recycling Concrete Pavement.
Arezoumandi, M. (2017). Mechanical Properties and Structural Performance of Recycled Aggregate Concrete: An Overview. Civil Engineering Research Journal, 1(5). https://doi.org/10.19080/cerj.2017.01.555571
Behnood, A., Olek, J., & Glinicki, M. A. (2015). Predicting Compressive Strength of Recycled Aggregate. Institute of Fundamental Technological Research, 381–391.
Bhardwaj, S., Aggrawal, S., Gupta, V., & Pandey, M. (2016). Use of Recycled Concrete Aggregate in Costruction. International Journal of Emerging Technologies in Engineering Research (IJETER), 4(10), 81–87. https://doi.org/10.1007/bf02480538
Bhattacharya, A., & Chatoopadhyay, P. (2010). Hybrid differential evolution with biogeography-based optimization for solution of economic load dispatch. IEEE Transactions on Power System, 25, 1995–1964.
Bui, D. K., Nguyen, T., Chou, J. S., Nguyen-Xuan, H., & Ngo, T. D. (2018). A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete. Construction and Building Materials, 180, 320–333. https://doi.org/10.1016/j.conbuildmat.2018.05.201
Cheng, M.-Y., & Lien, L.-C. (2012). Hybrid Artificial Intelligence–Based PBA for Benchmark Functions and Facility Layout Design Optimization. Journal of Computing in Civil Engineering, 26(5), 612–624. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000163
Cheng, M. Y., Firdausi, P. M., & Prayogo, D. (2014). High-performance concrete compressive strength prediction using Genetic Weighted Pyramid Operation Tree (GWPOT). Engineering Applications of Artificial Intelligence, 29, 104–113. https://doi.org/10.1016/j.engappai.2013.11.014
Cheng, M. Y., & Prayogo, D. (2014). Symbiotic Organisms Search: A new metaheuristic optimization algorithm. Computers and Structures, 139(July), 98–112. https://doi.org/10.1016/j.compstruc.2014.03.007
Choong, W. K., Leong, L. T., Sin, C. S., & Goh, B. H. (2013). Elastic Modulus of Concrete Cast with Recycled Aggregates. Applied Mechanics and Materials, 423–426, 1006–1009. https://doi.org/10.4028/www.scientific.net/amm.423-426.1006
Chopra, P., Sharma, R. K., & Kumar, M. (2016). Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming. Advances in Materials Science and Engineering, 2016(1), 32–34. https://doi.org/10.1155/2016/7648467
Chou, J. S., Chiu, C.-K., Farfoura, M., & Al-Taharwa, I. (2011). Optimizing the Prediction Accuracy of Concrete Compressive Strength Based on a Comparison of Data-Mining Techniques. Journal of Computating in Civil Engineering, 25(3), 242–253.
Corinaldesi, V. (2010). Mechanical and Elastic Behaviour of Concretes Made of Recycled-Concrete Coarse Aggregates. Construction and Building Materials, 24(9), 1616–1620. Retrieved from doi: 10.1016/j.%0Aconbuildmat.2010.02.031
Dhir, R. K., Limbachiya, M. C., & Leelawat, T. (1999). Suitability of Recycled Aggregate for Use in BS 5328 Designated Mixes. Proceedings of the Institution of Civil Engineers. Structures and Buildings, 134(3), 257–274.
Dillmann, R. (1998). Concrete with Recycled Concrete Aggregate. Proceedings of International Symposium on Sustainable Construction: Use of Recycled Concrete Aggregate, 239–253.
Du, D., Simon, D., & Ergezer, M. (2009). Biogeography-based Optimization Combined with Evolutionary Strategy and Immigration Refusal. In Proceedings of the 2009 IEEE International Conference on Systems, Man and Cybernetics (pp. 997–1002). Piscataway, NJ, USA: IEEE Press. Retrieved from http://dl.acm.org/citation.cfm?id=1732323.1732493
Duan, Z. H., Kou, S. C., & Poon, C. S. (2013). Using artificial neural networks for predicting the elastic modulus of recycled aggregate concrete. Construction and Building Materials, 44, 524–532. Retrieved from http://dx.doi.org/10.1016/j.conbuildmat.2013.02.064
Fazel Zarandi, M. H., Türksen, I. B., Sobhani, J., & Ramezanianpour, A. A. (2008). Fuzzy polynomial neural networks for approximation of the compressive strength of concrete. Applied Soft Computing Journal, 8(1), 488–498. https://doi.org/10.1016/j.asoc.2007.02.010
Golafshani, E. M., & Behnood, A. (2018). Automatic regression methods for formulation of elastic modulus of recycled aggregate concrete. Applied Soft Computing Journal, 64, 377–400. https://doi.org/10.1016/j.asoc.2017.12.030
Gong, W., Cai, Z., & Ling, C. X. (2011). DE/BBO: A hybrid differential evolution with biogeography-based optimization for global numerical optimization. Soft Computing, 15(4), 645–665. https://doi.org/10.1007/s00500-010-0591-1
Heniegal, A. M. (2012). Numerical Analysis for Predicting of Self Compacting Concrete Mixtures Using Artificial Neural Networks. Journal of Engineering Sciences, 40(6), 1575–1597.
Hong, S., Lee, S., & Kim, S. (2018). Flexural Behavior of High-Strength Reinforced Concrete Beam with Recycled Aggregate Strengthened by FRP Plate, 22(4), 126–132.
Hsie, M., Ho, Y. F., Lin, C. T., & Yeh, I. C. (2012). Modeling asphalt pavement overlay transverse cracks using the genetic operation tree and Levenberg-Marquardt Method. Expert Systems with Applications, 39(5), 4874–4881. https://doi.org/10.1016/j.eswa.2011.10.005
Jothiprakash, V., & Arunkumar, R. (2013). Optimization of Hydropower Reservoir Using Evolutionary Algorithms Coupled with Chaos. Water Resources Management, 27(7), 1963–1979. https://doi.org/10.1007/s11269-013-0265-8
Kakizaki, M., Harada, M., Soshiroda, T., Kubota, S., Ikeda, T., & Kasai, Y. (1988). Strength and Elastic Modulus of Recycled Aggregate Concrete. Proceedings of the Second International RILEM Symposium on Demolition and Reuse of Concrete and Masonry, 565–574.
Karaboga, D., & Akay, B. (2009). A comparative study of Artificial Bee Colony algorithm. Applied Mathematics and Computation, 214(1), 108–132. https://doi.org/10.1016/j.amc.2009.03.090
Khazaee, A., & Ghalehnovi, M. (2018). Bearing Stiffness of UHPC; An Experimental Investigation and A Comparative Study of Regression and SVR-ABC Models. Journal of Advanced Concrete Technology, 16(3), 145–158. https://doi.org/10.3151/jact.16.145
Konrad, Z., & Frank, R. (2001). Design of concrete structures made from recycled aggregate – a globe recommendation following the DIN 1045-1. Proceeding to the Fib-Symposium Concrete and Environment, Berlin.
Kou, S. C., Poon, C. S., & Chan, D. (2007). Influence of Fly Ash as Cement Replacement on the Properties of Recycled Aggregate Concrete. Journal of Materials in Civil Engineering, 19(9), 709–717.
Kumar, M. P., & Monteiro, P. J. (2014). Concrete – Microstructure, Properties, and Materials (4th ed.). McGraw Hill Education, New York, USA.
Liang, C., Qian, C., Chen, H., & Kang, W. (2018). Prediction of Compressive Strength of Concrete in Wet-Dry Environment by BP Artificial Neural Networks. Advances in Materials Science and Engineering, 2018. https://doi.org/10.1155/2018/6204942
Mcgovern, M. (2002). Going with the Flow proportioning have spawned the industry ’ s latest. Concrete Technology Today.
Mellmann, G. (1999). Processed Concrete Rubble for the Reuse as Aggregate. Proceedings of the International Seminar on Exploiting Waste in Concrete, 171–178.
Melo, V., & Banzhaf, W. (2016). Kaizen Programming for Feature Construction for Classification (pp. 39–57). https://doi.org/10.1007/978-3-319-34223-8_3
Mousavi, S. M., Gandomi, A. H., Alavi, A. H., & Vesalimahmood, M. (2010). Modeling of compressive strength of HPC mixes using a combined algorithm of genetic programming and orthogonal least squares. Structural Engineering and Mechanics, 36(2), 225–241. https://doi.org/10.12989/sem.2010.36.2.225
Park, W.-J., Noguchi, T., Shin, S.-H., & Oh, D.-Y. (2014). Modulus of elasticity of recycled aggregate concrete. Magazine of Concrete Research, 67(11), 585–591. https://doi.org/10.1680/macr.14.00213
Peng, C.-H., Yeh, I.-C., & Lien, L.-C. (2009). Building strength models for high-performance concrete at different ages using genetic operation trees, nonlinear regression and neural networks. Engineering Computations, 26, 61–73.
Peng, C.-K., Costa, M., & Goldberger, A. R. Y. L. (2009). Adaptive Data Analysis of Complex Fluctuations in Physiologic Time Series. Advances in Adaptive Data Analysis, 01(01), 61–70. https://doi.org/10.1142/S1793536909000035
Pham, A.-D., Hoang, N.-D., & Nguyen, Q.-T. (2015). Predicting Compressive Strength of High-Performance Concrete Using Metaheuristic-Optimized Least Squares Support Vector Regression. Journal of Computing in Civil Engineering, 30(3), 06015002. https://doi.org/10.1061/(asce)cp.1943-5487.0000506
Prasad, B. K. R., Eskandari, H., & Reddy, B. V. V. (2009). Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN. Construction and Building Materials, 23(1), 117–128. https://doi.org/10.1016/j.conbuildmat.2008.01.014
Prayogo, D., Cheng, M.-Y., Widjaja, J., Ongkowijoyo, H., & Prayogo, H. (2017). Prediction of concrete compressive strength from early age test result using an advanced metaheuristic-based machine learning technique. 34th International Symposium on Automation and Robotics in Construction, ISARC 2017, (Isarc), 856–863.
Prayogo, D., Wong, F. T., & Tjandra, D. (2018). Prediction of High-Performance Concrete Strength Using a Hybrid Artificial Intelligence Approach. MATEC Web of Conferences, 203, 06006. https://doi.org/10.1051/matecconf/201820306006
Rasheeduzzafar, K. A. (1984). Recycled concrete-a source of new aggregate. Cement, Concrete, and Aggregates. ASTM, 6(1), 17–27.
Ravindrarajah, R. S., & Tam, C. T. (1985). Properties of concrete made with crushed concrete as coarse aggregate. Magazine of Concrete Research, 37(130), 29–38.
Reyes-sánchez, J. A., Tenza-abril, A. J., Verdu, F., & Reyes Perales, J. A. (2018). Predicting modulus of elasticity of recycled aggregate concrete using nonlinear mathematical models. International Journal of Computational Methods and Experimental Measurements, 6(4), 703–715. https://doi.org/10.2495/cmem-v6-n4-703-715
Sadati, S., Brito da Silva, L. E., Wunsch, D. C., & Khayat, K. H. (2019). Artificial intelligence to investigate modulus of elasticity of recycled aggregate concrete. ACI Materials Journal, 116(1), 51–62. https://doi.org/10.14359/5170694
Saremi, S., Mirjalili, S., & Lewis, A. (2014). Biogeography-based optimisation with chaos. Neural Computing and Applications, 25(5), 1077–1097. https://doi.org/10.1007/s00521-014-1597-x
Saremi, S., Mirjalili, S. M., & Mirjalili, S. (2014). Chaotic Krill Herd Optimization Algorithm. Procedia Technology, 12, 180–185. https://doi.org/10.1016/j.protcy.2013.12.473
Simon, D. (2008). Biogeography-based optimization. IEEE Transactions on Evolutionary Computation, 12(6), 702–713. https://doi.org/10.1109/TEVC.2008.919004
Sonawane, T. R., & Pimplikar, S. S. (2013). Use of recycled aggregate in concrete. Int J Eng Res Technol, 2, 1–9.
Surahyo, A. (2019). Concrete Construction: Practical Problems and Solutions. Springer International Publishing.
Tavakoli, M., & Soroushian, P. (1996). Strength of recycled aggregate concrete made using field-demolished concrete as aggregate. ACI Materials Journal, 93(2), 182–190.
Tsai, H. C. (2011). Weighted operation structures to program strengths of concrete-typed specimens using genetic algorithm. Expert Systems with Applications, 38(1), 161–168. https://doi.org/10.1016/j.eswa.2010.06.034
Vimmrová, A., & Výborný, J. (2001). Building Materials 10: Materials and Testing Methods, 79–86.
Wang, G. G., Guo, L., Gandomi, A. H., Hao, G. S., & Wang, H. (2014). Chaotic Krill Herd algorithm. Information Sciences, 274, 17–34. https://doi.org/10.1016/j.ins.2014.02.123
Wang, N., Liu, L., & Liu, L. (2001). Genetic Algorithm in Chaos. OR Trans, 5, 1–10.
World Business Council for Sustainable Development. (2012). Recycling Concrete.
Xiao, J. Z., Li, J. B., & Zhang, C. (2006). On relationships between the mechanical properties of recycled aggregate concrete: An overview. Materials and Structures/Materiaux et Constructions, 39(290), 655–664. https://doi.org/10.1617/s11527-006-9093-0
Yang, L. J., & Chen, T. L. (2002). Application of chaos in genetic algorithms. Communications in Theoretical Physics, 38, 167–172.
Yeh, I.-C. (1998). Modeling of strength of High-Performance Concrete using Artificial Neural Networks. Cement and Concrete Research, 28(12), 1797–1808.
Yeh, I. C., & Lien, L. C. (2009). Knowledge discovery of concrete material using Genetic Operation Trees. Expert Systems with Applications, 36(3 PART 2), 5807–5812. https://doi.org/10.1016/j.eswa.2008.07.004
Yu, Y., Li, W., Li, J., & Nguyen, T. N. (2018). A novel optimised self-learning method for compressive strength prediction of high performance concrete. Construction and Building Materials, 184, 229–247. https://doi.org/10.1016/j.conbuildmat.2018.06.219
Zhenyu, G., Bo, C., Min, Y., & Binggang, C. (2006). Self-adaptive chaos differential evolution. Advances in Natural Computation, (1), 972–975.
Zilch, K., & Roos, F. (2001). An Equation to Estimate the Modulus of Elasticity of Concrete with Recycled Aggregates. Civil Engineering, 76(4), 187–191.