The Efficiency of Hybrid BNN-DWT for Predicting the Construction and Demolition Waste Concrete Strength

Document Type: Original Article


1 Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran

2 Department of Civil Engineering, Shahrekord University, Shahrekord, Iran


The current study focuses on two main goals. First, with the use of construction and demolition (C&D) of building materials, a new aggregate was produced and it was utilized for green concrete production. The compressive strength test confirmed the good function of C&DW aggregate concrete. This concrete did not show significant differences with natural sand concrete. Second, Backpropagation neural network (BNN) was adjusted for C&DW concrete strength prediction at different curing times. Although BNN has good accuracy for strength prediction, due to the importance of 28th day of concrete strength the need to improve the accuracy was felt. So discrete wavelet transform (DWT) was used on BNN and a hybrid network was produced. DWT by filtering the noises can improve the homogeneity of the dataset. The results of DWT-BNN showed that the regression can increase to 98% and the MSE index reduces to 0.001. Continued research has shown that increasing the number of filters to four steps leads to reduced accuracy and increased computational cost. So using DWT-BNN as a hybrid network with one filter can improve prediction ability to the desired level but adding up the number of filters not recommended.


1.     Tavakoli, D., Hashempour, M. and Heidari, A., "Use of waste materials in concrete: A review", Pertanika Journal of Science and Technology,  Vol. 26, No. 2, (2018), 499-522.

2.     Heidari, A., Hashempour, M. and Chermahini, M.D., "Influence of reactive mgo hydration and cement content on c&dw aggregate concrete characteristics", International Journal of Civil Engineering,  Vol. 17, No. 7, (2019), 1095-1106. doi:

3.     Arunraj, E., Vincent Sam Jebadurai, S., Daniel, C., Joel Shelton, J. and Hemalatha, G., "Experimental study on compressive strength of brick using natural fibres", International Journal of Engineering, Transactions C: Aspects,  Vol. 32, No. 6, (2019), 799-804. doi: 10.5829/ije.2019.32.06c.01

4.     Ehsani, M.R., Rajaie, H., ramezanianpour, A. and Momayez, A., "Experimental investigation of the methods of evaluating the bond strength between concrete substrate and repair materials", International Journal of Engineering, Transactions B: Applications  Vol. 15, No. 4, (2002), 319-332.

5.     Adejuyigbe, I., Chiadighikaobi, P. and Okpara, D., "Sustainability comparison for steel and basalt fiber reinforcement, landfills, leachate reservoirs and multi-functional structure", Civil Engineering Journal,  Vol. 5, No. 1, (2019), 172-180. doi:

6.     Emeka, A., Chukwuemeka, A. and Benjamin.Okwudili, M., "Deformation behaviour of erodible soil stabilized with cement and quarry dust", Emerging Science Journal,  Vol. 2, (2018), 383. doi:

7.     Heidari, A., Hashempour, M., Javdanian, H. and Karimian, M., "Investigation of mechanical properties of mortar with mixed recycled aggregates", Asian Journal of Civil Engineering,  Vol. 19, No. 5, (2018), 583-593. doi:

8.     Verian, K.P., Ashraf, W. and Cao, Y., "Properties of recycled concrete aggregate and their influence in new concrete production", Resources, Conservation and Recycling,  Vol. 133, (2018), 30-49. doi:

9.     Khaloo, A.R., "Crushed tile coarse aggregate concrete", Cement, Concrete and Aggregates,  Vol. 17, No. 2, (1995), 119-125.

10. Vejmelková, E., Keppert, M., Rovnaníková, P., Ondráček, M., Keršner, Z. and Černý, R., "Properties of high performance concrete containing fine-ground ceramics as supplementary cementitious material", Cement and Concrete Composites,  Vol. 34, No. 1, (2012), 55-61. doi:

11.   Ay, N. and Ünal, M., "The use of waste ceramic tile in cement production", Cement and Concrete Research,  Vol. 30, No. 3, (2000), 497-499. doi:

12.   Wongsa, A., Sata, V., Nuaklong, P. and Chindaprasirt, P., "Use of crushed clay brick and pumice aggregates in lightweight geopolymer concrete", Construction and Building Materials,  Vol. 188, (2018), 1025-1034. doi:

13.   Heidari, A. and Hashempour, M., "Investigation of mechanical properties of self compacting polymeric concrete with backpropagation network", International Journal of Engineering, Transactions C: Aspects,  Vol. 31, No. 6, (2018), 903-909. doi: 10.5829/ije.2018.31.06c.06

14.   Naderpour, H., Poursaeidi, O. and Ahmadi, M., "Shear resistance prediction of concrete beams reinforced by frp bars using artificial neural networks", Measurement,  Vol. 126, (2018), 299-308. doi:

15.   Heidari, A., Hashempour, M., Javdanian, H. and Nilforoushan, M.R., "The effects of reactive mgo on the mechanical properties of rock flour mortar", Iranian Journal of Science and Technology, Transactions of Civil Engineering,  Vol. 43, No. 3, (2019), 589-598. doi:

16.   Heidari, A., Hashempour, M. and Tavakoli, D., "Using of backpropagation neural network in estimating of compressive strength of waste concrete", Journal of Soft Computing in Civil Engineering,  Vol. 1, No. 1, (2017), 54-64. doi:

17.   Golafshani, E.M. and Behnood, A., "Application of soft computing methods for predicting the elastic modulus of recycled aggregate concrete", Journal of Cleaner Production,  Vol. 176, (2018), 1163-1176. doi:

18.   Xu, Y. and Jin, R., "Measurement of reinforcement corrosion in concrete adopting ultrasonic tests and artificial neural network", Construction and Building Materials,  Vol. 177, (2018), 125-133. doi:

19.   Kalman Šipoš, T., Miličević, I. and Siddique, R., "Model for mix design of brick aggregate concrete based on neural network modelling", Construction and Building Materials,  Vol. 148, No. Supplement C, (2017), 757-769. doi:

20.   Reuter, U., Sultan, A. and Reischl, D.S., "A comparative study of machine learning approaches for modeling concrete failure surfaces", Advances in Engineering Software,  Vol. 116, (2018), 67-79. doi:

21.   Paul, S.C., Panda, B., Huang, Y., Garg, A. and Peng, X., "An empirical model design for evaluation and estimation of carbonation depth in concrete", Measurement,  Vol. 124, (2018), 205-210. doi:

22.   Patel, S.S., Chourasia, A.P., Panigrahi, S.K., Parashar, J., Parvez, N. and Kumar, M., "Damage identification of rc structures using wavelet transformation", Procedia Engineering,  Vol. 144, (2016), 336-342. doi:

23.   Montes-García, P., Castellanos, F. and Vásquez-Feijoo, J.A., "Assessing corrosion risk in reinforced concrete using wavelets", Corrosion Science,  Vol. 52, No. 2, (2010), 555-561. doi:

24.   Heidari, A. and Raeisi, J., "Optimum design of structures against earthquake by simulated annealing using wavelet transform", Journal of Soft Computing in Civil Engineering,  Vol. 2, No. 4, (2018), 23-33. doi:

25.   Huang, L. and Wang, J., "Forecasting energy fluctuation model by wavelet decomposition and stochastic recurrent wavelet neural network", Neurocomputing,  Vol. 309, (2018), 70-82. doi:

26.   Hamidian, D., Salajegheh, J. and Salajegheh, E., "Damage detection of irregular plates and regular dams by wavelet transform combined adoptive neuro fuzzy inference system", Civil Engineering Journal,  Vol. 4, (2018), 305-319. doi:

27.   Hashempour, M., Heidari, A. and Jounaghani, M.S., "The evaluation of the stress-strain characteristics of mcc concrete", Materials Today Communications,  Vol. 23, (2020), 101133. doi:

28.   Belayneh, A., Adamowski, J., Khalil, B. and Quilty, J., "Coupling machine learning methods with wavelet transforms and the bootstrap and boosting ensemble approaches for drought prediction", Atmospheric Research,  Vol. 172-173, (2016), 37-47. doi:

29.   Aghajani, A., Kazemzadeh, R. and Ebrahimi, A., "A novel hybrid approach for predicting wind farm power production based on wavelet transform, hybrid neural networks and imperialist competitive algorithm", Energy Conversion and Management,  Vol. 121, (2016), 232-240. doi:

30.   Golafshani, E.M., Behnood, A. and Arashpour, M., "Predicting the compressive strength of normal and high-performance concretes using ann and anfis hybridized with grey wolf optimizer", Construction and Building Materials,  Vol. 232, (2020), 117266. doi:

31.   Alilou, V. and Teshnehlab, M., "Prediction of 28-day compressive strength of concrete on the third day using artificial neural networks", International Journal of Engineering, Vol. 3, No. 6, (2010), 565-670.

32.   Khademi, F., Jamal, S.M., Deshpande, N. and Londhe, S., "Predicting strength of recycled aggregate concrete using artificial neural network, adaptive neuro-fuzzy inference system and multiple linear regression", International Journal of Sustainable Built Environment,  Vol. 5, No. 2, (2016), 355-369. doi:

33.   Altunkaynak, A. and Wang, K.-H., "Estimation of significant wave height in shallow lakes using the expert system techniques", Expert Systems with Applications,  Vol. 39, No. 3, (2012), 2549-2559. doi: