Anticipating the Compressive Strength of Hydrated Lime Cement Concrete Using Artificial Neural Network Model

Chioma Temitope Gloria Awodiji, Davis Ogbonnaya Onwuka, Chinenye Okere, Owus Ibearugbulem


In this research work, the levernberg Marquardt back propagation neural network was adequately trained to understand the relationship between the 28th day compressive strength values of hydrated lime cement concrete and their corresponding mix ratios with respect to curing age. Data used for the study were generated experimentally. A total of a hundred and fourteen (114) training data set were presented to the network. Eighty (80) of these were used for training the network, seventeen (17) were used for validation, and another seventeen (17) were used for testing the network's performance. Six (6) data set were left out and later used to test the adequacy of the network predictions. The outcome of results of the created network was close to that of the experimental efforts. The lowest and highest correlation coefficient recorded for all data samples used for developing the network were 0.901 and 0.984 for the test and training samples respectively. These values were close to 1. T-value obtained from the adequacy test carried out between experimental and model generated data was 1.437. This is less than 2.064, which is the T values from statistical table at 95% confidence limit. These results proved that the network made reliable predictions. Maximum compressive strength achieved from experimental works was 30.83N/mm2 at a water-cement ratio of 0.562 and a percentage replacement of ordinary portland cement with hydrated lime of 18.75%. Generally, for hydrated lime to be used in making structural concrete, ordinary portland cement percentage replacement with hydrated lime must not be up to 30%. With the use of the developed artificial neural network model, mix design procedure for hydrated lime cement concrete can be carried out with lesser time and energy requirements, when compared to the traditional method. This is because, the need to prepare trial mixes that will be cured, and tested in the laboratory, will no longer be required.


Hydrated Lime; Compressive Strength; Artificial Neural Network; Ordinary Portland Cement.


Awodiji, Chioma, Onwuka, Davis and Awodiji, Olayinka. “Flexural and Split Tensile Strength Properties of Lime Cement Concrete”. Civil and Environmental Research. 9, no. 3 (2017): 10-16.

Shakeb Afsah “CDM Potential in the Cement Sector: The challenge of demonstrating additionality.” Performeks LLC. May 2004.

Welch Craig. Global Carbon-dioxide emissions are rising again. National Geographic. 2017.

Yate, T., and Ferguson, A. “The use of lime-based mortars in new buildings”. IHS BRE. June.…/mortars/NHBC%20lime%20mortar%20guide.pdf.

American Standard Test Measurement International. Standard Specification for Hydrated Lime for Masonry Purposes. ASTM C207. ASTM International. 2006.

Yang, Sarah. “To improve today’s Concrete, Do as the Romans Did.” Berkeley News. June 4, 2013. Accessed January 16, 2018.

“Preservation of our built heritage: St. Astier NHL mortars.” SAINT-ASTIER. Last modified March 31, 2009.

Rizwan, Syed, Toor Shamas, and Ahmad Husnain “Exploring huge natural resources of lime in Pakistan for construction industry.” In Proceedings of the 69th Annual Pakistan Engineering Congress. 2013. Pakistan: 2013.

Holmes, Stafford. “An introduction to building lime”. In Proceedings of the Manchester University Foresight Lime Research Conference, Nov. 19th, 2002. Manchester: 2002.

Gupta, B. L., & Gupta, A.. Concrete Technology (3rd Ed.). Standard Publishers Distributors. New Delhi, India, 2004.

Neville, Adam. Properties of Concrete, 4th edition, London, Pearson Education Inc, 2006.

Shetty, M. S. Properties of Concrete. Multicolour Revised edition. New Delhi. S. Chad &Company Ltd. 2006.

Yousif, Salim and Abdullah, Salwa. “Artificial neural networks model for predicting compressive strength of concrete.” Tikrit Journal of Engineering. Sciences 6, no. 3 (2009): 55-63.

Stegiou, Christos & Siganos, Dimitrios. “Neural Networks” Google. April 23, 2017.

Sathyabalan, P., Selladurai, V. and Sakthivel, P. “ANN Based Prediction of Effect of Reinforcements on Abrasive Wear Loss and Hardness in a Hybrid MMC.” American Journal of Engineering and Applied Sciences 2, no. 1 (January 1, 2009): 50–53. doi:10.3844/ajeassp.2009.50.53.

Fausett, Laurene. Fundamentals of Neural Network. New York. Prentice- Hall, 1994.

Zhang, Jisong, Yinghua Zhao, and Haijiang Li. “Experimental Investigation and Prediction of Compressive Strength of Ultra-High Performance Concrete Containing Supplementary Cementitious Materials.” Advances in Materials Science and Engineering 2017 (2017): 1–8. doi:10.1155/2017/4563164.

Chen, Huaicheng, Chunxiang Qian, Chengyao Liang, and Wence Kang. “An Approach for Predicting the Compressive Strength of Cement-Based Materials Exposed to Sulfate Attack.” Edited by Varenyam Achal. PLOS ONE 13, no. 1 (January 18, 2018): e0191370. doi:10.1371/journal.pone.0191370.

Khademi, Faezehossadat, Sayed Mohammadmehdi Jamal, Neela Deshpande, and Shreenivas Londhe. “Predicting Strength of Recycled Aggregate Concrete Using Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System and Multiple Linear Regression.” International Journal of Sustainable Built Environment 5, no. 2 (December 2016): 355–369. doi:10.1016/j.ijsbe.2016.09.003.

Lorenzi, Alexandre and Silva Filho, Luiz. “Artificial Neural Network Methods to Analysis of Ultrasonic Testing on Concrete.” The E-journal of Non-destructive Testing 20, no. 11 (2015): 1-12.

Dahou, Zohra, Z. Mehdi Sbartaï, Arnaud Castel, and Fouad Ghomari. “Artificial Neural Network Model for Steel–concrete Bond Prediction.” Engineering Structures 31, no. 8 (August 2009): 1724–1733. doi:10.1016/j.engstruct.2009.02.010.

Asteris, P.G., K.G. Kolovos, M.G. Douvika, and K. Roinos. “Prediction of Self-Compacting Concrete Strength Using Artificial Neural Networks.” European Journal of Environmental and Civil Engineering 20, no. sup1 (November 10, 2016): s102–s122. doi:10.1080/19648189.2016.1246693.

Muthpriya, P. Subramanian, K. and Vishnuran, B. G. “Prediction of Compressive strength and durability of High Performance Concrete by Artificial Neural Network.” International Journal of Optimization in Civil Engineering 1, (2011): 189-209.

Hawkins, Peter, Tennis Paul, and Detwiler Rachel. The use of limestone Portland cement: A state-of-the-art-review. Portland Cement Association EB 227.01. Skokie, PCA. 2003.

Dhir, R. K., M. C. Limbachiya, M. J. McCarthy, and A. Chaipanich. “Evaluation of Portland Limestone Cements for Use in Concrete Construction.” Materials and Structures 40, no. 5 (January 25, 2007): 459–473. doi:10.1617/s11527-006-9143-7.

laxmi,, C.Dhana, and Dr.K.Nirmal kumar. “Study on the Properties of Concrete Incorporated With Various Mineral Admixtures – Limestone Powder and Marble Powder (Review Paper).” International Journal of Innovative Research in Science, Engineering and Technology 04, no. 01 (January 15, 2015): 18511–18515. doi:10.15680/ijirset.2015.0401014.

Ravasan Farshad, Azardoust Ardalan, & Arash Osgouei. “Reuse of Sedimentary Lime and Incinerator Ash for the Production of Structural Concretes.” Life Science Journal 10, no. 5s (2013): 248-252.

Blair, Bruce. “Building Green with Blended Cement” Architects Magazine. August 11, 2010. Accessed July, 2018.

Acharya, Prasanna Kumar, Sanjaya Kumar Patro, and Narayana C. Moharana. “Effect of Lime on Mechanical and Durability Properties of Blended Cement Based Concrete.” Journal of The Institution of Engineers (India): Series A 97, no. 2 (May 27, 2016): 71–79. doi:10.1007/s40030-016-0158-y.

Almerich-Chulia, Ana, E. Fenollosa, and Pedro Martin. “Reinforced Lime Concrete with FRP: An Alternative in the Restoration of Architectural Heritage.” Applied Mechanics and Materials 851 (August 2016): 751–756. doi:10.4028/

Salman Mohammed and Mutter Ammar. “Mechanical properties of Lime concrete.” Journal of Engineering and Sustainable Development 21, no. 02 (2017): 180-191.

British Standard Institute. Specification for Ordinary and Rapid Hardening Portland Cement, Composition, Manufacture and Chemical and Physical Properties. BS 12. BSI- London. 1978.

Nigerian Industrial Standard. Composition, specification and conformity criteria for common cements NIS 444-1. Standards Organization of Nigeria. 2003.

British Standard Institute. Testing Concrete: Method for Determination of Compressive Strength of Concrete Cubes. BS 1881:116. BSI- London. 1983.

Anyanwu, Timothy. Mathematical Models for the Optimization of the Compressive Strength of Palm-Bunch Ash-Cement Concrete. M.Eng. thesis, Federal University of Technology, Owerri, Imo State, Nigeria, 2011.

Onwuka, Davis. & Awodiji, Chioma. “Artificial neural network for the modulus of rupture of concrete.” Advances in Applied Science Research 4, no. 4 (2013): 214–223.

Lourakis, Manolis. “A Brief Description of the Levenberg-Marquardt Algorithm implemented by Levmar.” Foundation of Research Technology 4, no. 1 (2005): 1-6.

Sounthararajan, V. M. & Sivakumar, A. “Effects of the Lime Content in Marble Powder for producing Concrete.” ARPN Journal of Engineering and Applied Science 8, no. 4 (2013): 260-264.

Beale, Mark, Hagan Martin, and Demuth Howard. “The Neural Network ToolboxTMR2014a User Guide.” The Mathworks, Inc. 2014.

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DOI: 10.28991/cej-03091216


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