Development of A Machine Learning-Based Model for Predicting Compressive Strength of Ordinary Portland Cement
| International Journal of Civil Engineering |
| © 2024 by SSRG - IJCE Journal |
| Volume 11 Issue 3 |
| Year of Publication : 2024 |
| Authors : Tesfahiwet Kesete, Christopher Kanali, Victoria Okumu, Gidewon Tekeste |
10.14445/23488352/IJCE-V11I3P107
How to Cite?
Tesfahiwet Kesete, Christopher Kanali, Victoria Okumu, Gidewon Tekeste, "Development of A Machine Learning-Based Model for Predicting Compressive Strength of Ordinary Portland Cement," SSRG International Journal of Civil Engineering, vol. 11, no. 3, pp. 80-93, 2024. Crossref, https://doi.org/10.14445/23488352/IJCE-V11I3P107
Abstract:
Cement is a vital construction material with widespread use in the construction industry, acting as a binding agent for various construction materials. The compressive strength of cement, which measures its binding force and ability to withstand compression, is a crucial factor in manufacturing cement and constructing concrete-based structures. Traditionally, costly laboratory tests have been employed to determine cement’s compressive strength. However, with the complexity of material engineering, this approach has become inefficient, leading to resource and time losses. Establishing a logical connection between cement’s chemical composition, physical characteristics, and compressive strength is also challenging due to its heterogeneous properties and nonlinear behaviour. However, to address these issues, with the evolution of machine learning and its efficient modelling techniques, different modelling techniques are prepared to study its behaviour and satisfy the desired performance. This paper aims to demonstrate the effectiveness of different shallow supervised machine learning techniques such as Multivariant linear regressions, Decision Tree (DT), Nonlinear regression, and ensemble Random Forests (RF) and apply Principal Component Analysis (PCA) to develop a compressive strength prediction model to overcome the disadvantages of traditional approaches (experimental analysis) in estimating the compressive strength of cement. Finally, the study has compared the results obtained by these different Machine Learning (ML) techniques and provided a general conclusion.
Keywords:
Cement, Cement compressive strength, Machine Learning model, Nonlinear regression, Principal Component Analysis.
References:
[1] Mohammed S. Imbabi, Collette Carrigan, and Sean McKenna, “Trends and Developments in Green Cement and Concrete Technology,” International Journal of Sustainable Built Environment, vol. 1, no. 2, pp. 194-216, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[2] S.N. Ghosh, Advances in Cement Technology: Chemistry, Manufacture and Testing, Taylor and Francis, pp. 1-828, 2002.
[Google Scholar] [Publisher Link]
[3] Farzad Naseri et al., “Experimental Observations and SVM-based Prediction of Properties of Polypropylene Fibres Reinforced Self-Compacting Composites Incorporating Nano-CuO,” Construction and Building Materials, vol. 143, pp. 589-598, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Xiangyang Xu, Nasim Fallahi, and Hao Yang, “Efficient CUF-based FEM Analysis of Thin-Wall Structures with Lagrange Polynomial Expansion,” Mechanics of Advanced Materials and Structures, vol. 29, no. 9, pp. 1316-1337, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[5] E. Vintzileou, and E. Panagiotidou, “An Empirical Model for Predicting the Mechanical Properties of FRP-Confined Concrete,” Construction and Building Materials, vol. 22, no. 5, pp. 841-854, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Mostafa Jalal, “Soft Computing Techniques for Compressive Strength Prediction of Concrete Cylinders Strengthened by CFRP Composites,” Science and Engineering of Composite Materials, vol. 22, no. 1, pp. 97-112, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Ian Flood, “Towards the Next Generation of Artificial Neural Networks for Civil Engineering,” Advanced Engineering Informatics, vol. 22, no. 1, pp. 4-14, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Vanessa Nilsen et al., “Prediction of Concrete Coefficient of Thermal Expansion and Other Properties Using Machine Learning,” Construction and Building Materials, vol. 220, pp. 587-595, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Mohit Verma, A. Thirumalaiselvi, and J. Rajasankar, “Kernel-based Models for Prediction of Cement Compressive Strength,” Neural Computing and Applications, vol. 28, pp. 1083-1100, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Xibin Dong et al., “A Survey on Ensemble Learning,” Frontiers of Computer Science, vol. 14, pp. 241-258, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Cha Zhang, and Yunqian Ma, Ensemble Machine Learning: Methods and Applications, Springer, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Tzu-Tsung Wong, and Po-Yang Yeh, “Reliable Accuracy Estimates from K-Fold Cross Validation,” IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 8, pp. 1586-1594, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Isaac Kofi Nti, Owusu Nyarko-Boateng, and Justice Aning, “Performance of Machine Learning Algorithms with Different K Values in K-Fold Cross-Validation,” International Journal of Information Technology and Computer Science, vol. 13, no. 6, pp. 61-71, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Jacob Berlin, and Amihai Motro, “Database Schema Matching Using Machine Learning with Feature Selection,” International Conference on Advanced Information Systems Engineering, pp. 452-466, 2002.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Peshawa Jammal Muhammad Ali, and Rezhna Hassan Faraj, “Data Normalization and Standardization: A Technical Report,” Machine Learning Technical Reports, vol. 1, no. 1, pp. 1-6, 2014.
[CrossRef] [Publisher Link]
[16] Kelsy Cabello-Solorzano et al., “The Impact of Data Normalization on the Accuracy of Machine Learning Algorithms: A Comparative Analysis,” 18th International Conference on Soft Computing Models in Industrial and Environmental Applications, pp. 344-353, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Zheng Xiong et al., “Evaluating Explorative Prediction Power of Machine Learning Algorithms for Materials Discovery Using K-Fold Forward Cross-Validation,” Computational Materials Science, vol. 171, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Chris Albon, Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning, O’Reilly Media, pp. 1-366, 2018.
[Google Scholar] [Publisher Link]
[19] Michael Greenacre et al., “Principal Component Analysis,” Nature Reviews Methods Primers, vol. 3, no. 22, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Aurélien Géron, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, O’Reilly Media, pp. 1-856, 2022.
[Google Scholar] [Publisher Link]
[21] T. Chai, and R.R. Draxler, “Root Mean Square Error (RMSE) or Mean Absolute Error (MAE)?–Arguments against Avoiding RMSE in the Literature,” Geoscientific Model Development, vol. 7, no. 3, pp. 1247-1250, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Davide Chicco, Matthijs J. Warrens, and Giuseppe Jurman, “The Coefficient of Determination R-Squared is more Informative than SMAPE, MAE, MAPE, MSE and RMSE in Regression Analysis Evaluation,” PeerJ Computer Science, vol. 7, PP. 1-24, 2021.
[CrossRef] [Google Scholar] [Publisher Link]