ANN modelling approach for predicting SCC properties - Research considering Algerian experience. Part I. Development and analysis of models

  • Mohamed Sahraoui
  • Tayeb Bouziani
Keywords: Artificial neural networks, self compacting concrete, Algerian materials, fresh and hardened properties, prediction


This paper presents research on the use of artificial neural networks (ANNs) to predict fresh and hardened properties of self compacting concrete (SCC) made with Algerian materials. A multi-layer perceptron network with 5 nodes, 12 inputs, and 5 outputs is trained and optimized using a database of 167 mixtures collected from literature. The inputs for the ANN models are ordinary Portland cement (Cm), polycarboxylate ether superplasticizer (Sp), river sand (RS), crushed sand (CS), dune sand (DS), Gravel 3/8 (G1), Gravel 8/15 (G2), Water (W), Limestone filler (Lim), Marble powder (MP), blast furnace slag (Slag) and natural pozzolan (Pz). Instead, Slump flow (Slump), V-funnel, L-Box, static stability (Pi) and 28 days compressive strength (Rc28) were the outputs of the study. Results indicate that ANN models for data sets collected from literature have a strong potential for predicting 28 days compressive strength. Slump flow, V-funnel time and L-Box ratio could be moderately identified while an acceptable prediction has been obtained for static stability. Results have also confirmed by statistical parameters, Regression plots and residual analysis.


Ahmadi, M., Naderpour, H., & Kheyroddin, A. (2017). ANN model for predicting the compressive strength of circular steel-confined concrete. International Journal of Civil Engineering, 15(2), 213–221.

Asteris, P. G., Kolovos, K. G., Douvika, M. G., & Roinos, K. (2016). Prediction of self-compacting concrete strength using artificial neural networks. European Journal of Environmental and Civil Engineering, 20(sup1), s102–s122.

Belaidi, A. S. E., Azzouz, L., Kadri, E., & Kenai, S. (2012). Effect of natural pozzolana and marble powder on the properties of self-compacting concrete. Construction and Building Materials, 31, 251–257.

Benabed, B. (2014). Influence de la qualité et de la nature des sables sur les performances et la durabilité des bétons autoplaçants. Université de Laghouat-Amar Telidji.

Benyamina, S., Menadi, B., Bernard, S. K., & Kenai, S. (2019). Performance of self-compacting concrete with manufactured crushed sand. Advances in Concrete Construction, 7(2), 87.

Boukendakdji, O., Kenai, S., Kadri, E. H., & Rouis, F. (2009). Effect of slag on the rheology of fresh self-compacted concrete. Construction and Building Materials, 23(7), 2593–2598.

Boukhelkhal, A., Azzouz, L., Belaïdi, A. S. E., & Benabed, B. (2016). Effects of marble powder as a partial replacement of cement on some engineering properties of self-compacting concrete. Journal of Adhesion Science and Technology, 30(22), 2405–2419.

Boukhelkhal, D., Boukendakdji, O., Kenai, S., & Bachene, S. (2015). Effect of mineral admixture type on stability and rheological properties of self-compacting concrete.

Bouziani, T. (2013). Assessment of fresh properties and compressive strength of self-compacting concrete made with different sand types by mixture design modelling approach. Construction and Building Materials, 49, 308–314.

Douma, O. B., Boukhatem, B., Ghrici, M., & Tagnit-Hamou, A. (2017). Prediction of properties of self-compacting concrete containing fly ash using artificial neural network. Neural Computing and Applications, 28(1), 707–718.

Getahun, M. A., Shitote, S. M., & Gariy, Z. C. A. (2018). Artificial neural network based modelling approach for strength prediction of concrete incorporating agricultural and construction wastes. Construction and Building Materials, 190, 517–525.

JMP. (2020). JMP Documentation Library, 1–5609. SAS Institute Inc. 2020. Discovering JMP® 15. Cary, NC: SAS Institute Inc.

Laidani, Z. E.-A., Benabed, B., Abousnina, R., Gueddouda, M. K., & Kadri, E.-H. (2020). Experimental investigation on effects of calcined bentonite on fresh, strength and durability properties of sustainable self-compacting concrete. Construction and Building Materials, 230, 117062.

Malagavelli, V., & Manalel, P. A. (2014). Modeling of compressive strength of admixture-based self compacting concrete using fuzzy logic and artificial neural networks. Asian Journal of Applied Sciences.

Nécira, B. (2018). Développement des bétons autoplaçants à hautes performances: influence de la composition. Université Mohamed Khider Biskra.

Nécira, B., Guettala, A., & Guettala, S. (2017). Study of the combined effect of different types of sand on the characteristics of high performance self-compacting concrete. Journal of Adhesion Science and Technology, 31(17), 1912–1928.

Okamura, H., & Ouchi, M. (2003). Self-compacting concrete. Journal of Advanced Concrete Technology, 1(1), 5–15.

Ouldkhaoua, Y., Benabed, B., Abousnina, R., & Kadri, E.-H. (2019). Rheological properties of blended metakaolin self-compacting concrete containing recycled CRT funnel glass aggregate. Epitoanyag-Journal of Silicate Based & Composite Materials, 71(5).

Saha, P., Prasad, M. L. V, & RathishKumar, P. (2017). Predicting strength of SCC using artificial neural network and multivariable regression analysis. Comput. Concrete, 20(1), 31–38.

Sahraoui, M., & Bouziani, T. (2019a). Effect of coarse aggregates and sand contents on workability and static stability of self-compacting concrete. Advances in Concrete Construction, 7(2), 97.

Sahraoui, M., & Bouziani, T. (2019b). Effects of fine aggregates types and contents on rheological and fresh properties of SCC. Journal of Building Engineering, 26, 100890.

Skender, Z., Bali, A., & Kettab, R. (2019). Self-compacting concrete (SCC) behaviour incorporating limestone fines as cement and sand replacement. European Journal of Environmental and Civil Engineering, 1–22.

Sonebi, M., Grünewald, S., Cevik, A., & Walraven, J. (2016). Modelling fresh properties of self-compacting concrete using neural network technique. Computers and Concrete, 18(4), 903–920.

Thakre, N., Mangrulkar, D., Janbandhu, M., & Saxena, J. (2017). Self-Compacting Concrete-Robustness of SCC. International Journal of Advanced Engineering Research and Science, 4(3), 237086.

Yaman, M. A., Abd Elaty, M., & Taman, M. (2017). Predicting the ingredients of self compacting concrete using artificial neural network. Alexandria Engineering Journal, 56(4), 523–532.

YH aissa, Y., Idriss, G., & Benabed, B. (2019). Mix-design and properties of self-compacting concrete made with calcareous tuff. Journal of Building Engineering, 27, 100997.

How to Cite
Sahraoui, M., & Bouziani, T. (2020). ANN modelling approach for predicting SCC properties - Research considering Algerian experience. Part I. Development and analysis of models. Journal of Building Materials and Structures, 7(2), 188-198.
Original Articles