Predicting the strength properties of slurry infiltrated fibrous concrete using artificial neural network

Abstract

This paper is aimed at adapting Artificial Neural Networks (ANN) to predict the strength properties of SIFCON containing different minerals admixture. The investigations were done on 84 SIFCON mixes, and specimens were cast and tested after 28 days curing. The obtained experimental data are trained using ANN which consists of 4 input parameters like Percentage of fiber (PF), Aspect Ratio (AR), Type of admixture (TA) and Percentage of admixture (PA). The corresponding output parameters are compressive strength, tensile strength and flexural strength. The predicted values obtained using ANN show a good correlation between the experimental data. The performance of the 4-14-3 architecture was better than other architectures. It is concluded that ANN is a highly powerful tool suitable for assessing the strength characteristics of SIFCON.

Keywords

artificial neural networks / root mean square error / SIFCON / silica fume / metakaolin / steel fiber

References

[1] Lankard D R. Properties application slurry infiltrated fiber concrete (SIFCON). Concrete International, 1984, 6: 44–47

[2] Tayfur G, Erdem T, Kırca Ö. Strength Prediction of High-Strength Concrete by Fuzzy Logic and Artificial Neural Networks. Journal of Materials in Civil Engineering, 2014, 26(11): 04014079

[3] Topçu I B, Saridemir M. Prediction of rubberized mortar properties using artificial neural network and fuzzy logic. Journal of Materials Processing Technology, 2008, 199(1-3): 108–118

[4] Zhang X, Wang H, Wang D, Li C. Prediction of Concrete Strength based on Self organizing Fuzzy Neural Network. In: Proceeding of the 11th World Congress on Intelligent Control and Automation Shenyang, China, June-July, 2014

[5] Abdalla A J, Hawileh R, Al-Tamimi A. Prediction of FRP-concrete ultimate bond strength using Artificial Neural Network. In: International Conference on Modeling, Simulation and Applied Optimization (ICMSAO), Kuala Lumpur, April, 2011

[6] Amani J, Moeini R. Prediction of shear strength of reinforced concrete beams using adaptive neuro-fuzzy inference system and artificial neural network. Scientia Iranica, 2012, 19(2): 242–248

[7] Ghaboussi J, Garrett J H Jr, Wu X. Knowledge-Based Modeling of Material Behavior with Neural Networks. Journal of Engineering Mechanics, 1991, 117(1): 132–153

[8] Mukherjee A, Schemauder S, Ruhle M. Artificial neural network for the prediction of the mechanical behaviour of metal matrix composite. Acta Metallurgica et Materialia, 1995, 43(11): 4083–4091

[9] Chopra P, Sharma R K, Kumar M. Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming. Advances in Materials Science and Engineering, 2016, (2): 1-10

[10] Akkurt S, Tayfur G, Can S. Fuzzy logic model for the prediction of cement compressive strength. Cement and Concrete Research, 2004, 34(8): 1429–1433

[11] Demir A. Prediction of Hybrid fibre-added concrete strength using artificial neural networks. Computers and Concrete, 2015, 15(4): 503–514

[12] Hamdia K M, Lahmer T, Nguyen-Thoi T, Rabczuk T. Predicting the fracture toughness of PNCs: A stochastic approach based on ANN and ANFIS. Computational Materials Science, 2015, 102: 304–313

[13] Bal L, Buyle-Bodin F. Artificial neural network for predicting drying shrinkage of concrete. Construction & Building Materials, 2013, 38: 248–254

[14] Başyigit C, Akkurt I, Kilincarslan S, Beycioglu A. Prediction of compressive strength of heavyweight concrete by ANN and FL models. Neural Computation, 2010, 19(4): 507–513

[15] Dias W P S, Pooliyadda S P. Neural networks for predicting properties of concretes with admixtures. Construction & Building Materials, 2001, 15(7): 371–379

[16] Adeli H. Neural networks in civil engineering: 1989-2000. Comput-Aided. Civ. Inf., 2001, 16: 126–142

[17] Aiyer B G, Kim D, Karingattikkal N, Samui P, Rao P R.Prediction of Compressive Strength of Self- Compacting Concrete using Least Square Support Vector Machine and Relevance Vector Machine. KSCE Journal of Civil Engineering, 2014, 18(6): 1753–1758

[18] Boukhatem B, Kenai S, Hamou A T, Ziou D, Ghrici M. Predicting concrete properties using neural networks (NN) with principal component analysis (PCA) technique. Computers and Concrete, 2012, 10(6): 557–573

[19] Alexandridis A, Triantis D, Stavrakas I, Stergiopoulos C. A neural network approach for compressive strength prediction in cement-based materials through the study of pressure-stimulated electrical signals. Construction & Building Materials, 2012, 30: 294–300

[20] Muhammad K, Mohammad N, Rehman F. Modeling shotcrete mix design using artificial neural network. Computers and Concrete, 2015, 15(2): 167–181

[21] Bilim C, Atis C D, Tanyildizi H, Karahan O. Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network. Advances in Engineering Software, 2009, 40(5): 334–340

[22] Erdem H. Prediction of the moment capacity of reinforced concrete slabs in fire using artificial neural networks. Advances in Engineering Software, 2010, 41(2): 270–276

[23] Erdal H I, Karakurt O, Namli E. High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform. Engineering Applications of Artificial Intelligence, 2013, 26(4): 1246–1254

[24] Cheng M, Firdausi P M, Prayogo D. High-performance concrete compressive strength prediction using Genetic Weighted Pyramid Operation Tree (GWPOT). Engineering Applications of Artificial Intelligence, 2014, 29: 104–113

[25] Ghafari E, Bandarabadi M, Costa H, Júlio E. Prediction of Fresh and Hardened State Properties of UHPC: Comparative Study of Statistical Mixture Design and an Artificial Neural Network Model. Journal of Materials in Civil Engineering, 2015, 27(11): 04015017

[26] Gupta S. Using Artificial Neural Network to Predict the Compressive Strength of Concrete containing Nano-silica. Civil Engineering and Architecture, 2013, 1: 96–102

[27] Hush D R, Horne B G. Progress in supervised Neural Network: What is New since Lippman. IEEE Signal Processing Magazine, 1993, 10: 8–39

[28] Najigivi A, Khaloo A. A. Iraji zad, and S.A. Rashid, An Artificial Neural Networks Model for Predicting Permeability Properties of Nano Silica–Rice Husk Ash Ternary Blended Concrete. IJCSM, 2013, 7: 225–238

[29] Pham A, Hoang N, Nguyen Q. Predicting Compressive Strength of High-Performance Concrete Using Metaheuristic-Optimized Least Squares Support Vector Regression. Journal of Computing in Civil Engineering, 2016, 30(3): 06015002

[30] A.Sincero. Predicting Mixing Power Using Artificial Neural Network. In: Proceedings of World Water & Environmental Resources Congress, Philadelphia, Pennsylvania, United States, June, 2003

[31] Słoński M. A comparison of model selection methods for compressive strength prediction of high performance concrete using neural networks. Computers & Structures, 2010, 88(21-22): 1248–1253

[32] Sarıdemir M. Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic. Advances in Engineering Software, 2009, 40(9): 920–927

[33] Yeh I C. Analysis of Strength of Concrete Using Design of Experiments and Neural Networks. Journal of Materials in Civil Engineering, 2006, 18(4): 597–604

[34] Yeh I C. Design of High-Performance Concrete Mixture Using Neural Networks and Nonlinear Programming. Journal of Computing in Civil Engineering, 1999, 13(1): 36–42

[35] Hou T H, Su C H, Chang H Z. Using neural networks and immune algorithms to find the optimal parameters for an IC wire bonding process. Expert Systems with Applications, 2008, 34(1): 427–436

[36] Kostić S, Vasović D. Prediction model for compressive strength of basic concrete mixture using artificial neural networks. Neural Computing & Applications, 2015, 26(5): 1005–1024

[37] Lee S C. Prediction of concrete strength using artificial neural networks. Engineering Structures, 2003, 25(7): 849–857

[38] Öztaş A, Pala M, Özbay E, Kanca E, Çagˇlar N, Bhatti M A. Predicting the compressive strength and slump of high strength concrete using neural network. Construction & Building Materials, 2006, 20(9): 769–775

[39] Parichatprecha R, Nimityongskul P. Analysis of durability of high performance concrete using artificial neural networks. Construction & Building Materials, 2009, 23(2): 910–917

[40] Morova N, Karahancer S, Terzi S, Serin S. Modeling Marshall Stability of Light Asphalt Concretes Fabricated using Expanded Clay Aggregate with Artificial Neural Networks. International Symposium on Innovations in Intelligent Systems and Applications, Turkey, 2012

[41] Vu-Bac N, Lahmer T, Zhuang X, Rabczuk T. A software framework for probabilistic sensitivity analysis for computationally expensive models. Advances in Engineering Software, 2016, 100: 19–31

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag GmbH Germany