Self-compacting concrete strength prediction using surrogate models
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Despite the extensive use of self-compacting concrete in constructions over the last decades, there is not yet a robust quantitative method, available in the literature, which can reliably predict its strength based on its mix components. Τhis limitation is due to the highly nonlinear relation between the self-compacting concrete’s compressive strength and the mixed components. In this paper, the application of artificial neural networks for predicting the mechanical characteristics of self-compacting concrete has been investigated. Specifically, surrogate models (such as artificial neural network models and a new proposed normalization method) have been used for predicting the 28-day compressive strength of admixture-based self-compacting concrete (based on experimental data available in the literature). The comparison of the derived results with the experimental findings demonstrates the ability of artificial neural networks to approximate the compressive strength of self-compacting concrete in a reliable and robust manner. Furthermore, the proposed formula for the normalization of data has been proven effective and robust compared to available ones.
KeywordsArtificial neural networks Back propagation neural networks Compressive strength Self-compacting concrete
The research was performed within the framework of the Master’s Program in Applied Computational Structural Engineering (ACSE), which has been partially financed by the Research Committee of the School of Pedagogical and Technological Education, Athens, Greece. The authors would like to express their gratitude to MSc students Mrs. M.G. Douvika, Mr. K. Roinos and Mr. A.K. Tsaris and to the undergraduate students Mr. N. Margaris and Mr. D. Georgakopoulos for their assistance on the computational implementation of the ANN models.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- 5.Asteris, P.G., Plevris, V. (2013). Neural network approximation of the masonry failure under biaxial compressive stress, ECCOMAS Special Interest Conference—SEECCM 2013: 3rd South-East European Conference on Computational Mechanics, Proceedings—an IACM Special Interest Conference, pp. 584–598Google Scholar
- 10.Baskar I, Ramanathan P, Venkatasubramani R (2012) Influence of silica fume on properties of self-compacting concrete. Int J Emerg Trends Eng Dev 4:757–767Google Scholar
- 14.Berry MJA, Linoff G (1997) Data mining techniques. Wiley, NYGoogle Scholar
- 15.Blum A (1992) Neural networks in C++. Wiley, NYGoogle Scholar
- 16.Boger, Z, Guterman, H (1997) Knowledge extraction from artificial neural network models, IEEE Systems, Man, and Cybernetics Conference, Orlando, FL, USAGoogle Scholar
- 18.Boukendakdji O, Kadri EH, Kenai S (2012) Effects of granulated blast furnace slag and superplasticizer type on the fresh properties and compressive strength of self-compacting concrete. Constr Build Mater 34:583–590Google Scholar
- 24.Fathi A, Shafiq N, Nuruddin MF, Elheber A (2013) Study the effectiveness of the different pozzolanic material on self-compacting concrete. ARPN J Eng Applied Sci 8:229–305Google Scholar
- 26.Gandage, AS, Ram, VV, Sivakumar, MVN, Vasan, A, Venu, M, Yaswanth, AB (2013) Optimization of class C flyash dosage in self-compacting concrete for pavement applications, Proceedings of the International Conference on Innovations in Concrete for Meeting Infrastructure Challenge, October 23–26, 2013, Hyderabad, Andhra Pradesh, India, pp: 213–226Google Scholar
- 29.Gettu, R., Izquierdo, J., Gomes, P.C.C., Josa, A. (2002). Development of high-strength self-compacting concrete with fly ash: a four-step experimental methodology, Proceedings of the 27th Conference on Our World in Concrete and Structures, August 29–30, 2002, Singapore, pp: 217–224Google Scholar
- 31.Grdic Z, Despotovic I, Curcic GT (2008) Properties of self-compacting concrete with different types of additives. Facta Universitatis-Ser: Archit Civil Eng 6:173–177Google Scholar
- 34.Iruansi, O, Guadagnini, M, Pilakoutas, K, Neocleous, K (2010) Predicting the shear strength of RC beams without stirrups using Bayesian neural network, in 4th International Workshop on Reliable Engineering Computing (REC 2010)Google Scholar
- 36.Karlik B, Olgac AV (2011) Performance analysis of various activation functions in generalized MLP architectures of neural networks. International Journal of Artificial Intelligence And Expert Systems (IJAE) 1(4):111–122Google Scholar
- 38.Lamanna J, Malgaroli A, Cerutti S, Signorini MG (2012) Detection of fractal behavior in temporal series of synaptic quantal release events: a feasibility study, Computational Intelligence and Neuroscience, volume 2012, 2012. Article number 704673Google Scholar
- 40.Lourakis MIA (2005). A brief description of the Levenberg-Marquardt algorithm implemened by levmar. Institute of Computer Science Foundation for Research and Technology - Hellas (FORTH), available at: http://www.ics.forth.gr/~lourakis/levmar/levmar.pdf.
- 43.Mansouri, I., Gholampour, A., Kisi, O., Ozbakkaloglu, T. (2016). Evaluation of peak and residual conditions of actively confined concrete using neuro-fuzzy and neural computing techniques, neural computing and applications, pp. 1-16Google Scholar
- 48.Pattnaik S, Karunakar DB, Jha PK (2014) A prediction model for the lost wax process through fuzzy-based artificial neural network, Proceedings of the Institution of Mechanical Engineers. Part C: Journal of Mechanical Engineering Science 228(7):1259–1271Google Scholar
- 50.Phani SS, Sekhar ST, Rao S, Sravana P (2013) High strength self-compacting concrete using mineral admixtures. Indian Concr J 87:42–47Google Scholar
- 51.Plevris, V, Asteris, PG (2014a) Modeling of masonry compressive failure using Neural Networks, OPT-i 2014—1st International Conference on Engineering and Applied Sciences Optimization, Proceedings, pp. 2843–2861Google Scholar
- 53.Plevris, V, Asteris, P (2015) Anisotropic failure criterion for brittle materials using Artificial Neural Networks, COMPDYN 2015—5th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, pp. 2259–2272Google Scholar
- 55.Rao, NVR, Rao, PS, Sravana, P, Sekhar, TS (2009). Studies on relationship of water-powder ratio and compressive strength of self-compacted concrete, Proceedings of the 34th Conference on Our World in Concrete and Structures, August 16–18, 2009, Singapore, pp: 1–8Google Scholar