Artificial Neural Network for forecasting the Initial Setting Time of Cement Pastes
Citation
MLA Style :M. A. Abubakar, A.S. Maihula, M.B. Jibril, A. Bashir "Artificial Neural Network for forecasting the Initial Setting Time of Cement Pastes" International Journal of Recent Engineering Science 6.4(2019):13-17.
APA Style :M. A. Abubakar, A.S. Maihula, M.B. Jibril, A. Bashir, Artificial Neural Network for forecasting the Initial Setting Time of Cement Pastes. International Journal of Recent Engineering Science, 6(4),13-17.
Abstract
The most crucial element in cement and concrete behavior is the setting time of the cement paste; it states essential information in producing the final concrete products. In this study, Neural Network (N.N.) was applied to predict the initial setting time of the cement paste. 206 cases were collected from 14 published works of literature. The inputs selected are based on their significant effect on the setting time. The inputs are cementitious materials (slag, fly ash, and silica fume), cement`s oxide (CaO, Al2O3, SiO2& Fe2O3), water-to-cement ratio, environmental condition (Temperature), fineness of cement, superplasticizer, and cement content. The performances of the model were assessed from R2 and RMSE, and the results show a higher accuracy of 0.8949 (%).
Reference
[1] A. M. Neville and J. J. Brooks, "Concrete technology," Building and Environment, vol. 11. pp. 1–9, 2010.
[2] M. A. A. and S. I. A. Abba Bashir, Chhavi Gupta, "Comparison of Properties of Coarse Aggregate Obtained from Recycled Concrete with that of Conventional Coarse Aggregates," Eur. J. Adv. Eng. Technol., vol. 5, no. 8, p. 628–637., 2018.
[3] A. M. Neville, "Properties of concrete, 5th ed," p. 872, 2011.
[4] A. M. Neville, Properties of Concrete. Essex: Pearson Education Limited, 2005.
[5] E. Güneyisi, M. Gesoglu, and E. Özbay, "Evaluating and forecasting the initial and final setting times of selfcompacting concretes containing mineral admixtures by neural network," Mater. Struct. Constr., vol. 42, no. 4, pp. 469–484, 2009.
[6] F. Khademi, S. M. Jamal, N. Deshpande, and S. Londhe, "Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System and Multiple Linear Regression," Int. J. Sustain. Built Environ., vol. 5, no. 2, pp. 355–369, 2016.
[7] H. Baseri, S. M. Rabiee, F. Moztarzadeh, and M. SolatiHashjin, "Mechanical strength and setting times estimation of hydroxyapatite cement by using neural network," Mater. Des., vol. 31, no. 5, pp. 2585–2591, 2010.
[8] A. Raheman and P. P. O. Modani, "Prediction of Properties of Self Compacting Concrete Using Artificial Neural Network," vol. 3, no. 4, pp. 333–339, 2013.
[9] J. J. Brooks, M. A. Megat Johari, and M. Mazloom, "Effect of admixtures on the setting times of high-strength concrete," Cem. Concr. Compos., vol. 22, no. 4, pp. 293– 301, 2000.
[10] P. Ghoddousi, A. Akbar, and S. Javid, "A new method to determine initial setting time of cement and concrete using plate test," Mater. Struct., vol. 49, no. 8, pp. 3135–3142, 2016.
[11] E. Tkaczewska, "Effect of the superplasticizer type on the properties of the fly ash blended cement," Constr. Build. Mater., vol. 70, pp. 388–393, 2014.
[12] ?. B. Topçu and Ö. Ate?in, "Effect of high dosage lignosulphonate and naphthalene sulphonate based plasticizer usage on micro concrete properties," Constr. Build. Mater., vol. 120, pp. 189–197, 2016.
[13] K. Wang and Z. Ge, "Evaluating Properties of Blended Cements for Concrete Pavements," Dep. Civil, Constr. Environ. Eng., no. December, pp. 1–59, 2003.
[14] M. J. Webster, W. Xiaozheng, and Z. Aisha, "Rheology and Setting Time of Cement Paste," vol. 3, no. 6, pp. 208–211, 2015.
[15] E. Yurdakul, "Optimizing concrete mixtures with minimum cement content for performance and sustainability," Optim. Concr. Mix. with Minim. Cem. content Perform. Sustain., p. 112, 2010.
[16] M. Zhang, K. Sisomphon, T. S. Ng, and D. J. Sun, "Effect of superplasticizers on workability retention and initial setting time of cement pastes," Constr. Build. Mater., vol. 24, no. 9, pp. 1700–1707, 2010.
[17] A. M. N. and J. J. Brooks, Properties of Concrete. 2003.
[18] E. Gulbandilar and Y. Kocak, "Prediction of the effects of fly ash and silica fume on the setting time of Portland cement with fuzzy logic," Neural Comput. Appl., vol. 22, no. 7–8, pp. 1485–1491, 2013.
[19] B. Khan and M. Ullah, "Effect of a Retarding Admixture on the Setting Time.of Cement Pastes in Hot Weather," JKAU Eng.Sci., vol. 15, no. 1, pp. 63–79, 2004.
[20] M. Olivia, R. Oktaviani, and Ismeddiyanto, "Properties of Concrete Containing Ground Waste Cockle and Clam Seashells," Procedia Eng., vol. 171, pp. 658–663, 2017.
[21] K. A. Olonade, M. B. Jaji, S. A. Rasak, and B. . Ojo, "Comparative Quality Evaluation of Cement Brands used in South-west Nigeria," Acad. J. Sci. Eng., vol. 9, no. 1, pp. 53–63, 2015.
[22] Y. N. Sheen, D. H. Le, and T. H. Sun, "Innovative usages of stainless steel slags in developing self-compacting concrete," Constr. Build. Mater., vol. 101, pp. 268–276, 2015.
[23] N. B. Singh, R. Sarvahi, N. P. Singh, and A. K. Shukla, "thermochimica acta Effect of temperature on the hydration of ordinary Portland cement in the presence of a superplasticizer," vol. 247, pp. 381–388, 1994.
[24] F. D. Tamas, "Acceleration and Retardation of Portland Cement Hydration by Additives," no. 8, pp. 392–397, 1960.
[25] G. Abba, S. I., & Elkiran, "Effluent prediction of chemical oxygen demand from the wastewater treatment plant using artificial neural network application," in Procedia computer science, 2017, p. 156–163.
[26] S. sorooshian Kuo-lin Hsu, Hoshin Vijai Gupta, "Artificial Neural Networks Modelling of the rainfall - runoff process," J. Hydrol. Eng., vol. 31, no. 10, pp. 2517–2530, 1995.
[27] E. B. DoganE., Asude A., Yilmaz E.C, "Application of Artificial Neural Networks to Estimate Wastewater Treatment Plant Inlet Biochemical Oxygen Demand," Wiley Intersci., vol. 27(4), pp. 439–446, 2015.
[28] F. D. Nourani, V., Alami, M. T., and Vousoughi, "Waveletentropy data pre-processing approach for ANN-based groundwater level modeling," J. Hydrol., vol. 524, pp. 255– 269, 2015.
[29] M. A. S. S.I. Abba, A.S. Maihula, M.B. Jibril, A.M. Sunusi, M.A. Ahmad, "Application of data driven algorithms for the forecasting of nonlinear parameter.," Int. J. Recent Eng. Sci., vol. 6, no. 2, 2019.
[30] Nourani, V., Elkiran, G., & Abba, S. I. (2018). Wastewater treatment plant performance analysis using artificial intelligence–an ensemble approach. Water Science and Technology, 78(10), 2064-2076.
[31] Ms.R.Sangavi, Mrs.J.Umanambi, "Comparative Study on Strength Behaviour of Falg Concrete with Conventional Concrete" SSRG International Journal of Civil Engineering 6.4 (2019): 1-5.
[32] Abba, S. I., Hadi, S. J., & Abdullahi, J. (2017). River water modeling prediction using multi-linear regression, artificial neural network, and adaptive neuro-fuzzy inference system techniques. Procedia computer science, 120, 75-82.
[33] V. N. G. Elkiran, S. I. Abba, and J. Abdullahi, "Artificial intelligence-based approaches for multi-station modeling of dissolved oxygen in the river," vol. 4, no. 4, 2018.
[34] E. G., Nourani, V., & Abba, S. I. Multi-step ahead modeling of river water quality parameters using ensemble artificial-intelligence-based approach. Journal of Hydrology, 123962.(2019).
Keywords
NN, cement paste setting, R2 ,RMSE.