Compressive Strength Prediction of High-Strength Concrete Using Regression and ANN Models

  • Sukomal Mandal
  • M. Shilpa
  • Ramachandra RajeshwariEmail author
Conference paper
Part of the Lecture Notes in Civil Engineering book series (LNCE, volume 25)


High-strength concrete (HSC) is one of the most popular terminologies used in the concrete technology, which is known for benefits like high workable, durable and high ultimate strength. The estimation of the compressive strength (CS) using experimental method is too expensive and time-consuming procedure and small error will lead to repetition of the work, and to overcome this, alternative methods are used for prediction of the CS of HSC. In the present study, the experimentally investigated HSC data pertaining to various mix proportions are collected from authenticated journal papers, which are used to predict the CS using regression analysis—multilinear regression (MLR) and artificial neural network (ANN) models. The collected data set is divided into two groups, one for training and other for testing. The input parameters used in regression and ANN models are cement content, super plasticizer, coarse aggregate, fly ash, fine aggregate, silica fume, blast furnace slag, water–cement ratio and the CS of HSC at 28 days is the output parameter. The models are developed using training data set and the developed model is validated using testing data set. The comparison is made between the CS obtained from the MLR and ANN models. The ANN model yields better correlation between predicted and actual values of the CS (test correlation for MLR—45.48% and ANN—95.03%) and the percentage of error also reduces as compared to that of MLR. From this investigation, it is observed that the ANN model can be used to predict the CS of HSC.


High-strength concrete Artificial neural network Multilinear regression 


  1. 1.
    Alves, M. F., Cremonini, R. A., & Dal Molin, D. C. C. (2004). A comparison of mix proportioning methods for high-strength concrete. Cement & Concrete Composites, 26, 613–621. (Elsevier).CrossRefGoogle Scholar
  2. 2.
    Behnood, A., & Ziari, H. (2008). Effects of silica fume addition and water to cement ratio on the properties of high-strength concrete after exposure to high temperatures. Cement & Concrete Composites, 30, 106–112. (Elsevier).CrossRefGoogle Scholar
  3. 3.
    Rashid, M. A., & Mansur, M. A. (2009). Considerations in producing high strength concrete. Journal of Civil Engineering (IEB), 37(1), 53–63.Google Scholar
  4. 4.
    Husem, M. (2006). The effect of high temperature on compressive and flexural strengths of ordinary and high performance concrete. Fire Safety Journal, 41, 155–163. (Elsevier).CrossRefGoogle Scholar
  5. 5.
    Ibrahim, A., El-Chabib, H., & Eisa, A. (2013). Ultra strength flowable concrete made with high volumes of supplementary cementitious materials. Journal of Material in Civil Engineering-ASCE, 25(12), 1830–1839. Scholar
  6. 6.
    Khademi, F., Akbari, M., Jamal, S. M., & Nikoo, M. (2017). Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete. Frontiers of Structural and Civil Engineering, 11(1), 90–99. Scholar
  7. 7.
    Altun, F., Kisi, Ö., & Aydin, K. (2008). Predicting the compressive strength of steel fiber added lightweight concrete using neural network. Computational Materials Science, 42, 259–265. (Elsevier).CrossRefGoogle Scholar
  8. 8.
    Özcana, F., Atis, C. D., Karahan, O., Uncuoğlu, E., & Tanyildizi, H. (2009). Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete. Advances in Engineering Software, 40, 856–863. (Elsevier).CrossRefzbMATHGoogle Scholar
  9. 9.
    Chou, J.-S., & Tsai, C.-F. (2012). Concrete compressive strength analysis using a combined classification and regression technique. Automation in Construction, 24, 52–60. (Elsevier).CrossRefGoogle Scholar
  10. 10.
    Słoński, M. (2010). A comparison of model selection methods for compressive strength prediction of high-performance concrete using neural networks. Computers and Structures, 88, 12–48. Scholar
  11. 11.
    Khan, M. I. (2012). Predicting properties of High Performance Concrete containing composite cementitious materials using Artificial Neural Networks. Automation in Construction, 22, 516–524. (Elsevier).CrossRefGoogle Scholar
  12. 12.
    Sarıdemir, M. (2009). Prediction of the compressive strength of mortars containing metkaolin by artificial neural networks and fuzzy logic. Advances in Engineering Software, 40, 920–927. (Elsevier).CrossRefzbMATHGoogle Scholar
  13. 13.
    Deshpande, N., Londhe, S., & Kulkarni, S. (2014). Modeling compressive strength of recycled aggregate concrete by Artificial Neural Network, Model Tree and Non-linear Regression. International Journal of Sustainable Built Environment, 3, 187–198. Scholar
  14. 14.
    Akpinara, P., & Khashmanb, A. (2017). Intelligent classification system for concrete compressive strength. Procedia Computer Science, 120, 712–718. (Elsevier).CrossRefGoogle Scholar
  15. 15.
    Parichatprecha, R., & Nimityongskul, P. (2009). Analysis of durability of high performance concrete using artificial neural networks. Construction and Building Materials, 23, 910–917. (Elsevier).CrossRefGoogle Scholar
  16. 16.
    Lee, S.-C. (2003). Prediction of concrete strength using artificial neural networks. Engineering Structures, 25, 849–857. Scholar
  17. 17.
    Duan, Z. H., Kou, S. C., & Poon, C. S. (2013). Prediction of compressive strength of recycled aggregate concrete using artificial neural networks. Construction and Building Materials, 40, 1200–1206. (Elsevier).CrossRefGoogle Scholar
  18. 18.
    Ouda, A. S. (2015). Development of high-performance heavy density concrete using different aggregates for gamma-rays shielding. Housing and Building National Research Center HBRC J, 11, 328–338. (Elsevier).CrossRefGoogle Scholar
  19. 19.
    Elahia, A., Basheerb, P. A. M., Nanukuttanb, S. V., & Khana, Q. U. Z. (2010). Mechanical and durability properties of high performance concretes containing supplementary cementitious materials. Construction and Building Materials, 24, 292–299. (Elsevier).CrossRefGoogle Scholar
  20. 20.
    Raghu Prasad, B. K., Eskandari, H., & Venkatarama Reddy, B. V. (2009). Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN. Construction and Building Materials, 23, 117–128. (Elsevier).CrossRefGoogle Scholar
  21. 21.
    Vinay Kumar, B. M., Ananthan, H., & Balaji, K. V. A. (2017). Experimental studies on utilization of recycled coarse and fine aggregates in high performance concrete mixes. Alexandria Engineering Journal Scholar
  22. 22.
    Hassan, K. E., Cabrera, J. G., & Maliehe, R. S. (2000). The effect of mineral admixtures on the properties of high-performance concrete. Cement & Concrete Composites, 22, 267–271. (Elsevier).CrossRefGoogle Scholar
  23. 23.
    Biskri, Y., Achoura, D., Chelghoum, N., & Mouret, M. (2017). Mechanical and durability characteristics of High Performance Concrete containing steel slag and crystalized slag as aggregates. Construction and Building Materials, 150, 167–178. (Elsevier).CrossRefGoogle Scholar
  24. 24.
    Camões, A., Aguiar, B., & Jalali, S. (2003). Durability of low cost high performance fly ash concrete. International Ash Utilization Symposium, Center for Applied Energy Research, University of Kentucky, Paper #43.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Sukomal Mandal
    • 1
  • M. Shilpa
    • 1
  • Ramachandra Rajeshwari
    • 1
    Email author
  1. 1.Department of Civil EngineeringPES UniversityBengaluruIndia

Personalised recommendations