Forecasting the Shear Strength of Binary Blended Concrete Containing Hydrated Lime Using Artificial Intelligence

Document Type : Regular Article


1 Senior Lecturer, Department of Civil and Environmental Engineering, University of Port-Harcourt, Nigeria

2 Professor, Department of Civil Engineering, Federal University of Technology, Owerri, Nigeria


In this exploratory study, the shear strength of blended cement concrete made using the hydrated lime (HL) as an admixture was studied. 120 shear strength values were experimentally obtained for several mix ratios at 7, 14, 21, and 28 days. This concrete was put together from water, portland cement (PC), HL, river sand (RS), and granite chippings (GC). 96 of the results were utilized to formulate a Levernberg-Marquardt backpropagation artificial neural network (ANN) for determining the shear strength of the concrete. The effectiveness of the forecast was tested using the unused 24 results. The model had 6 input variables namely; proportions of PC, HL, RS, GC, water, and curing age. While the output variable was the shear strength value. 1 hidden layer of 20 neurons was adopted. Uppermost 28 days shear strength value of 1.257N/mm2 was observed at 13.75% replacement of PC with HL for 0.58 water-cement ratio. The performance of the ANN proved that the model was acceptably executed. Root mean square errors (RMSE) obtained between network forecast and experimental values ranged from 0.0278 to 0.06536. These are close to 0. In addition, the factor of agreement (IA) determined were within the limits of 0.0475 and 0.1747. These are between the stipulated range of 0 to 1 for consistency between variables. The highest average percentage error recorded between model predictions and experimental values was 2.5066%. Lastly, the ANN created can be convincingly used to predict the shear strength of hydrated lime cement concrete and eliminate the need for try-out laboratory research.


Main Subjects

[1]     Afsarian F, Saber A, Pourzangbar A, Olabi AG, Khanmohammadi MA. Analysis of recycled aggregates effect on energy conservation using M5′ model tree algorithm. Energy 2018;156:264–77. doi:10.1016/
[2]     Suryankanta P. What is blended Cement and what are its advantages?,; 2014. Retrieved April 7, 2020, from 2014.
[3]     Qadir W, Ghafor K, Mohammed A. Evaluation the effect of lime on the plastic and hardened properties of cement mortar and quantified using Vipulanandan model. Open Eng 2019;9:468–80. doi:10.1515/eng-2019-0055.
[4]     ACI CT-13 (2013). ACI Concrete Terminology. ACI Standard. n.d.
[5]     Wikipedia. Shear strength. Accessed on 23rd April, 2020 2020.
[6]     Thorhallsson ER, Birgisson SR. Experiment on Concrete Beams without Shear Reinforcement. Nordic Concrete Research 2014;50:145–8.
[7]     Nogueira CL. Anti-plane shear strength of plain concrete. Mater Today Commun 2020;24:101051. doi:10.1016/j.mtcomm.2020.101051.
[8]     Rodger L. BBC News on “Climate Change: The massive CO2 emitter you may not know about” Available from 2018.
[9]     Awodiji CTG, Onwuka DO, Okere C, Ibearugbulem O. Anticipating the Compressive Strength of Hydrated Lime Cement Concrete Using Artificial Neural Network Model. Civ Eng J 2018;4:3005. doi:10.28991/cej-03091216.
[10]    Kartam N, Flood I, Garrett JH. Artificial neural networks for civil engineers: fundamentals and applications, American Society of Civil Engineers; 1997.
[11]    Mahanta J. Introduction to neural networks, advantages and applications. Towar Data Sci 2017;13.
[12]    Pourzangbar A, Saber A, Yeganeh-Bakhtiary A, Ahari LR. Predicting scour depth at seawalls using GP and ANNs. J Hydroinformatics 2017;19:349–63. doi:10.2166/hydro.2017.125.
[13]    Behnood A, Behnood V, Modiri Gharehveran M, Alyamac KE. Prediction of the compressive strength of normal and high-performance concretes using M5P model tree algorithm. Constr Build Mater 2017;142:199–207. doi:10.1016/j.conbuildmat.2017.03.061.
[14]    Amani J, Moeini R. Prediction of shear strength of reinforced concrete beams using adaptive neuro-fuzzy inference system and artificial neural network. Sci Iran 2012;19:242–8. doi:10.1016/j.scient.2012.02.009.
[15]    Sharifi Y, Hosainpoor M. A Predictive Model Based ANN for Compressive Strength Assessment of the Mortars Containing Metakaolin. J Soft Comput Civ Eng 2020;4:1–12.
[16]    Leiva-Villacorta F, Vargas-Nordcbeck A. Neural Network Based Model to Estimate Dynamic Modulus E* for Mixtures in Costa Rica. J Soft Comput Civ Eng 2019;3:1–15.
[17]    Priyadarshee A, Chandra S, Gupta D, Kumar V. Neural Models for Unconfined Compressive Strength of Kaolin clay mixed with pond ash, rice husk ash and cement. J Soft Comput Civ Eng 2020;4:85–102.
[18]    Collins MP, C. BE, Quach PT, Fisher AW, Y. PG. Predicting the shear strength of concrete structures. New Zeal Concr Ind Conf 2015 Rotorua Conv Cent n.d.
[19]    Neville AM. Properties of Concrete (4th Edition). Pearson Education Limited, Delhi 1995.
[20]    BS 1881, Part 118 (1983). Testing concrete: Method of Determination of Flexural Strength. British Standard Institute, London n.d.
[21]    Yu, L, Wang S, Lai KK. International Series in Operations Research and Management Science n.d.;138.
[22]    Sharma S, Sharma S. Activation functions in neural networks. Towar Data Sci 2017;6:310–6.