Prediction of Compressive Strength in High Strength Concrete with Steel Fiber Addition using Support Vector Machine Algorithm

  • Abdulhameed Umar Abubakar Civil Engineering Department, MAUTECH, Nigeria
  • Kachalla Alhaji Kau
  • Murtala Hassan
  • Maimuna Salisu Tabra
Keywords: High strength concrete, steel fiber-reinforced concrete, compressive strength prediction, algorithms, support vector machine

Abstract

In this study, a support vector machine model available in Weka Algorithms, was utilized to test the predictive capacity of compressive strength in high performance concrete (HSC) with steel fiber addition. To test the performance of the algorithm, a certain percentage were allocated for training of the algorithm, and the rest for test. This was done from 60-40 percent split up to 90-10 percent split for training and testing respectively. Results generated from the model include mean absolute error, root mean squared error, and relative absolute error for each model. It was observed that there was a good correlation between the actual and predicted values, and that errors were relatively low. Utilization of free algorithms in civil engineering construction will enhance the optimization of concrete mixtures.

 

References

[1] Jakkula, V. (N.D.) “Tutorial on Support Vector Machines” available online at course.ccs.edu./cs5100f11/resources/jakkula.pdf
[2] Wikipedia Online. http://en.wikipedia.org/wiki
[3] Tefas, A., Kotropoulous, C., Pitas, I. (1999) “Enhancing the performance of elastic graph matching for face authentications by using support vector machines”. In Proceedings of Advanced Course on Artificial Intelligence (ACAI 99), Chania, Greece.
[4] Fernandez, R. (1999) “Predicting time series with a local support vector regression machine”. In Proceedings of Advanced Course on Artificial Intelligence (ACAI 99), Chania, Greece.
[5] Veropoulos, K., Cristianni, N., Campbell, C. (1999) “The Application of Support Vector Machines to Medical Decision Support: A Case Study”. In Proceedings of Advanced Course on Artificial Intelligence (ACAI 99), Chania, Greece.
[6] Evgeniou, T., Pontil, M. (1999) “Workshop on Support Vector Machines: Theory and Applications”. In Proceedings of Advanced Course on Artificial Intelligence (ACAI 99), Chania, Greece. DOI:10.1007/3-540-44673-7_12
[7] Berwick, R. (N.D.) “An Idiot’s Guide to Support Vector Machines (SVM)” available at web.mit.edu/6.034/wwwbob/sum-notes-long-09.pdf
[8] Wahba, G. (1990) “Splines Models for Observational Data”. Series in Applied Mathematics, Vol. 59, SIAM.
[9] Vapnik, V. (1998) “Statistical Learning Theory”. Wiley, New York.
[10] Cristianni, N., Shawe-Taylor, J. (2000) “An Introduction to SVM and Other Kernel-based Learning Methods”. Cambridge University Press.
[11] Faruqi, M.A., Agarwala, R., Sai, J., Francisco, A. (2015) “Application of Artificial Intelligence to Predict Compressive Strength of Concrete from Mix Design Parameters: A Structural Engineering Application”, Journal of Civil Engineering Research, 5(6), 158-161.
[12] Chou, J.-S. C., Tsai, C.-F. (2012) “Concrete compressive strength analysis using a combined classification and regression technique”, Automation in Construction, 24, 52-60.
[13] Hong-Guang, N., Wang, J.-Z. (2000) “Prediction of compressive strength of concrete by neural networks”, Cement and Concrete Research, 30 (8), 1245-1250.
[14] Saridemir, M. (2009a) “Prediction of compressive strength of concretes containing metakaolin and silica fume by artificial neural networks”, Advances in Engineering Software, 40 (5), 350-355.
[15] Saridemir, M. (2009b) “Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic”, Advances in Engineering Software, 40 (9), 920-927.
[16] Duan, Z. H., Kou, S. C., Poon, C. S. (2012) “Prediction of compressive strength of recycled aggregate concrete using artificial neural networks”, Construction and Building Materials.
[17] Chithra, S., Senthil Kumar, S.R.R., Chinnaraju, K., Ashmita, F.A. (2016) “A Comparative Study on the Compressive Strength prediction models for High Performance Concrete containing nano silica and copper slag using regression analysis and Artificial Neural Networks”, Constr. Build. Mater., 114, 528-535.
[18] Sadrmomtazi, A., Sobhani, J., Mirgozar, M.A. (2013) “Modeling compressive strength of EPS lightweight concrete”, Constr. Build. Mater., 42, 205-216.
[19] Oh, J., Lee, I., Kim, J., & Lee, G. (1999). Applications of neural networks for proportioning of concrete mixes. ACI Material Journal, 96 (1), 51–59.
[20] Kasperkiewicz, J., Dubrawski, A. (1995) “HPC strength prediction using artificial neural network”, Journal of Computing in Civil Engineering, 9 (4), 279–284.
[21] Fazel Zarandi, M.H., Türksen, I.B., Sobhani, J., Ramezanianpour, A.A. (2008) “Fuzzy polynomial neural networks for approximation of the compressive strength of concrete”, Applied Soft Computing 8 (1), 488–498.
[22] Platt, J.C. (1998). Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. Available at www.microsoft.com MSR-TR-98-14
[23] Chu, F., Jin, G., Wang, L. (2005) “Cancer Diagnosis and Protein Secondary Structure Prediction using Support Vector Machines”, StudFuzz, Vol. 177, pp. 343-363.
[24] Ackgenc, M., Ulas, M. & Alyamac, K. E. (2015). Using Artificial Neural Network to Predict Mix Compositions of Steel Fiber – Reinforced Concrete. Arab J. Sci. Eng. Vol. 40, 407 – 419.
[25] Abubakar, A.U. (2018) “Influence of Steel Fiber Addition on Workability & Mechanical Behavior of High Performance Concrete” PhD Thesis, EMU North Cyprus.
[26] Eren, O. & Marar, K. (2009). Effects of limestone crusher dust and steel fibers on concrete. Constr. Build. Mater., 23, 981-988. doi:10.1016/j.conbuildmat.2008.05.014
[27] Eren, O., Marar, K. & Celik, T. (1999). Effects of Silica Fume and Steel Fibers on Some Mechanical Properties of High-Strength Fiber – Reinforced Concrete. Journal of Testing & Evaluations, JTEVA Vol. 27, No. 6, 380 – 387.
[28] Ibrahim, I.S., & Che Bakar, M.B. (2011). Effects on mechanical properties of industrialised steel fibers addition to normal weight concrete. In: Proceedings of The Twelfth East Asia-Pacific Conference on Structural Engineering and Construction. Procedia Engineering. doi:10.1016/j.proeng.2011.07.329
[30] Marar, K., Eren, O. & Yitmen, I. (2011). Compression Specific Toughness of Normal Strength Steel Fiber Reinforced Concrete (NSSFRC) and High Strength Steel Fiber Reinforced Concrete (HSSFRC). Materials Research, 14(2), 239-247.
[31] Nguyen-Minh, L., Rovnak, M., Tran-Quoc, T., & Nguyenkim, K. (2011). Punching Shear Resistance of Steel Fiber Reinforced Concrete Flat Slabs. In: Proceedings of The Twelfth East Asia-Pacific Conference on Structural Engineering and Construction. Procedia Engineering. doi:10.1016/j.proeng.2011.07.230
[32] Nili, M. & Afroughsabet, V. (2010). Combined effect of silica fume and steel fibers on the impact resistance and mechanical properties of concrete. Int. J. Impact Eng., 32, 879-886. doi:10.1016/j.ijimpeng.2010.03.004
[33] Pigeon, M. & Cantin, R. (1998). Flexural properties of steel fiber-reinforced concretes at low temperatures. Cem. Concr. Compos., 20, 365-375.
doi:10.1016/S0958-9465(98)00017-1
[34] Sahin, Y. & Koksal, F.(2011). The influences of matrix and steel fiber tensile strengths on the fracture energy of high-strength concrete. Constr. Build. Mater., 25, 1801-1806. doi:10.1016/j.conbuildmat.2010.11.084
[35] Unal, O., Demir, F. & Uygunoglu, T. (2007). Fuzzy logic approach to predict stress–strain curves of steel fiber-reinforced concretes in compression. Build. Environ. doi:10.1016/j.buildenv.2006.10.023
[36] Yalcin, M.(1994). Optimization and Performance Based Design of Steel Fiber Reinforced Concretes. Doctoral thesis, Istanbul Technic University, Civil Engineering Faculty, Turkey.
Published
2020-06-20
How to Cite
Abubakar, A., Kau, K., Hassan, M., & Tabra, M. (2020, June 20). Prediction of Compressive Strength in High Strength Concrete with Steel Fiber Addition using Support Vector Machine Algorithm. Sustainable Structures and Materials, An International Journal, 3(1), 25-36. https://doi.org/https://doi.org/10.26392/SSM.2020.03.01.025