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Comparative Analysis of Machine Learning Techniques for Prediction of the Compressive Strength of Field Concrete

Year 2024, , 127 - 137, 31.08.2024

Ajibola Oyedejı , Adekunle David , Ositola Osifeko , Abisola Olayiwola , Omobolaji Opafola

https://doi.org/10.35377/saucis...1415583

Abstract

The determination of the concrete compressive strength remains a challenging task in the concrete industry. Machine learning (ML) algorithms offer an alternative and this study presents a comparative analysis of five ML regression models; Gradient Boosting (GB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), and Linear Regression (LR) on a dataset of 1030 concrete samples. The findings indicate that the GB model achieved the best performance. The developed GB model achieved R-squared values of 91.60%, 91.43%, and 90.18% for the 10-fold, 5-fold, and 3-fold cross-validations, respectively, with mean absolute error, root mean squared error, and mean absolute percentage error values of 2.6776, 4.3523, and 9.19%, respectively. The GB model trained and evaluated was deployed to a web application using Streamlit for real-time prediction of the concrete compressive strength. The results of this research offer a precise and practical method for judging the quality of concrete constructions.

Keywords

Machine learning Concrete compressive strength Prediction Regression models Web application

References

  • ACI, AMERICAN CONCRETE INSTITUTE. ACI 318: Building Code Requirements for Structural Concrete. American Concrete Institute, 2008.
  • J. Zhang, Y. Zhao, and H. Li, “Experimental Investigation and Prediction of Compressive Strength of Ultra-High Performance Concrete Containing Supplementary Cementitious Materials,” Adv. Mater. Sci. Eng., vol. 2017, 2017, doi: 10.1155/2017/4563164.
  • B. G. Aiyer, D. Kim, N. Karingattikkal, P. Samui, and P. R. Rao, “Prediction of compressive strength of self-compacting concrete using least square support vector machine and relevance vector machine,” KSCE J. Civ. Eng., vol. 18, no. 6, pp. 1753–1758, 2014, doi: 10.1007/s12205-014-0524-0.
  • C. Bilim, C. D. Atiş, H. Tanyildizi, and O. Karahan, “Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network,” Adv. Eng. Softw., vol. 40, no. 5, pp. 334–340, 2009, doi: 10.1016/j.advengsoft.2008.05.005.
  • R. Mustapha and M. EL Aroussi, “High-Performance Concrete Compressive Strength Prediction Based Weighted Support Vector Machines,” Int. J. Eng. Res. Appl., vol. 07, no. 01, pp. 68–75, 2017, doi: 10.9790/9622-0701016875.
  • S. Popovics, “Analysis of the concrete strength versus water-cement ratio relationship,” ACI Mater. J., vol. 87, no. 5, pp. 517–529, 1990, doi: 10.14359/1944.
  • J. P. Zaniewski, MATERIALS FOR CIVIL AND 3 rd Edition Michael S . Mamlouk Arizona State University. 2011.
  • R. Kozul and D. Darwin, “Effects of Aggregate Type, Size and Content on Concrete Strength and Fracture Energy,” 1997.
  • A. Fernández-Jiménez and A. Palomo, “Characterisation of fly ashes. Potential reactivity as alkaline cements,” Fuel, vol. 82, no. 18, pp. 2259–2265, 2003, doi: 10.1016/S0016-2361(03)00194-7.
  • J. M. Fox, “Fly Ash Classification-Old and New Ideas,” in Fly Ash Classification – Old and New Ideas, 2017, pp. 1–15.
  • A. M. Zeyad, “Effect of curing methods in hot weather on the properties of high-strength concretes,” J. King Saud Univ. - Eng. Sci., vol. 31, no. 3, pp. 218–223, 2019, doi: 10.1016/j.jksues.2017.04.004.
  • B. A. Young, A. Hall, L. Pilon, P. Gupta, and G. Sant, “Can the compressive strength of concrete be estimated from knowledge of the mixture proportions?: New insights from statistical analysis and machine learning methods,” Cem. Concr. Res., vol. 115, no. July, pp. 379–388, 2019, doi: 10.1016/j.cemconres.2018.09.006.
  • M. A. DeRousseau, J. R. Kasprzyk, and W. V. Srubar, “Computational design optimization of concrete mixtures: A review,” Cem. Concr. Res., vol. 109, pp. 42–53, 2018, doi: 10.1016/j.cemconres.2018.04.007.
  • M. Z. Naser, “An engineer’s guide to eXplainable Artificial Intelligence and Interpretable Machine Learning: Navigating causality, forced goodness, and the false perception of inference,” Autom. Constr., vol. 129, no. September, 2021, doi: 10.1016/j.autcon.2021.103821.
  • K. Khan, W. Ahmad, M. N. Amin, F. Aslam, A. Ahmad, and M. A. Al-Faiad, “Comparison of Prediction Models Based on Machine Learning for the Compressive Strength Estimation of Recycled Aggregate Concrete,” Materials (Basel)., vol. 15, no. 10, pp. 1–36, 2022, doi: 10.3390/ma15103430.
  • L. Chi et al., “Machine learning prediction of compressive strength of concrete with resistivity modification,” Mater. Today Commun., vol. 36, p. 106470, Aug. 2023, doi: 10.1016/j.mtcomm.2023.106470.
  • P. G. Asteris, A. D. Skentou, A. Bardhan, P. Samui, and K. Pilakoutas, “Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models,” Cem. Concr. Res., vol. 145, no. October 2020, p. 106449, 2021, doi: 10.1016/j.cemconres.2021.106449.
  • D.-C. Feng et al., “Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach,” Constr. Build. Mater., vol. 230, p. 117000, Jan. 2020, doi: 10.1016/j.conbuildmat.2019.117000.
  • H. N. Muliauwan, D. Prayogo, G. Gaby, and K. Harsono, “Prediction of Concrete Compressive Strength Using Artificial Intelligence Methods,” J. Phys. Conf. Ser., vol. 1625, no. 1, p. 012018, Sep. 2020, doi: 10.1088/1742-6596/1625/1/012018.
  • Z. Zeng et al., “Accurate prediction of concrete compressive strength based on explainable features using deep learning,” Constr. Build. Mater., vol. 329, 2022, doi: 10.1016/j.conbuildmat.2022.127082.
  • I.-C. Yeh, “Modeling of Strength of High-Performance Concrete Using Artificial Neural Networks,” Cem. Concr. Res., vol. 28, no. 12, pp. 1797–1808, 1998.
  • I.-C. Yeh, “Concrete Compressive Strength.” UCI Machine Learning Repository, 2007, doi: 10.24432/C5PK67.
  • D. Maulud and A. M. Abdulazeez, “A Review on Linear Regression Comprehensive in Machine Learning,” J. Appl. Sci. Technol. Trends, vol. 1, no. 4, pp. 140–147, 2020, doi: 10.38094/jastt1457.
  • E. Pekel, “Estimation of soil moisture using decision tree regression,” Theor. Appl. Climatol., vol. 139, no. 3–4, pp. 1111–1119, 2020, doi: 10.1007/s00704-019-03048-8.
  • M. Rakhra et al., “Crop Price Prediction Using Random Forest and Decision Tree Regression:-A Review,” Mater. Today Proc., no. xxxx, 2021, doi: 10.1016/j.matpr.2021.03.261.
  • V. Rodriguez-Galiano, M. Sanchez-Castillo, M. Chica-Olmo, and M. Chica-Rivas, “Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines,” Ore Geol. Rev., vol. 71, pp. 804–818, 2015, doi: 10.1016/j.oregeorev.2015.01.001.
  • B. Singh, P. Sihag, and K. Singh, “Modelling of impact of water quality on infiltration rate of soil by random forest regression,” Model. Earth Syst. Environ., vol. 3, no. 3, pp. 999–1004, 2017, doi: 10.1007/s40808-017-0347-3.
  • J. Cai, K. Xu, Y. Zhu, F. Hu, and L. Li, “Prediction and analysis of net ecosystem carbon exchange based on gradient boosting regression and random forest,” Appl. Energy, vol. 262, no. 114566, pp. 1–14, 2020, doi: 10.1016/j.apenergy.2020.114566.
  • U. Singh, M. Rizwan, M. Alaraj, and I. Alsaidan, “A machine learning-based gradient boosting regression approach for wind power production forecasting: A step towards smart grid environments,” Energies, vol. 14, no. 16, pp. 1–21, 2021, doi: 10.3390/en14165196.
  • L. E. de Oliveira Aparecido, G. de Souza Rolim, J. R. da Silva Cabral De Moraes, C. T. S. Costa, and P. S. de Souza, “Machine learning algorithms for forecasting the incidence of Coffea arabica pests and diseases,” Int. J. Biometeorol., vol. 64, no. 4, pp. 671–688, 2020, doi: 10.1007/s00484-019-01856-1.
  • C. Araújo, C. Soares, I. Pereira, D. Coelho, M. Â. Rebelo, and A. Madureira, “A Novel Approach for Send Time Prediction on Email Marketing,” Appl. Sci., vol. 12, no. 8310, pp. 1–13, 2022, doi: 10.3390/app12168310.
  • A. O. Oyedeji, A. M. Salami, O. Folorunsho, and O. R. Abolade, “Analysis and Prediction of Student Academic Performance Using Machine Learning,” JITCE (Journal Inf. Technol. Comput. Eng., vol. 4, no. 01, pp. 10–15, 2020, doi: 10.25077/jitce.4.01.10-15.2020.

Year 2024, , 127 - 137, 31.08.2024

Ajibola Oyedejı , Adekunle David , Ositola Osifeko , Abisola Olayiwola , Omobolaji Opafola

https://doi.org/10.35377/saucis...1415583

Abstract

References

  • ACI, AMERICAN CONCRETE INSTITUTE. ACI 318: Building Code Requirements for Structural Concrete. American Concrete Institute, 2008.
  • J. Zhang, Y. Zhao, and H. Li, “Experimental Investigation and Prediction of Compressive Strength of Ultra-High Performance Concrete Containing Supplementary Cementitious Materials,” Adv. Mater. Sci. Eng., vol. 2017, 2017, doi: 10.1155/2017/4563164.
  • B. G. Aiyer, D. Kim, N. Karingattikkal, P. Samui, and P. R. Rao, “Prediction of compressive strength of self-compacting concrete using least square support vector machine and relevance vector machine,” KSCE J. Civ. Eng., vol. 18, no. 6, pp. 1753–1758, 2014, doi: 10.1007/s12205-014-0524-0.
  • C. Bilim, C. D. Atiş, H. Tanyildizi, and O. Karahan, “Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network,” Adv. Eng. Softw., vol. 40, no. 5, pp. 334–340, 2009, doi: 10.1016/j.advengsoft.2008.05.005.
  • R. Mustapha and M. EL Aroussi, “High-Performance Concrete Compressive Strength Prediction Based Weighted Support Vector Machines,” Int. J. Eng. Res. Appl., vol. 07, no. 01, pp. 68–75, 2017, doi: 10.9790/9622-0701016875.
  • S. Popovics, “Analysis of the concrete strength versus water-cement ratio relationship,” ACI Mater. J., vol. 87, no. 5, pp. 517–529, 1990, doi: 10.14359/1944.
  • J. P. Zaniewski, MATERIALS FOR CIVIL AND 3 rd Edition Michael S . Mamlouk Arizona State University. 2011.
  • R. Kozul and D. Darwin, “Effects of Aggregate Type, Size and Content on Concrete Strength and Fracture Energy,” 1997.
  • A. Fernández-Jiménez and A. Palomo, “Characterisation of fly ashes. Potential reactivity as alkaline cements,” Fuel, vol. 82, no. 18, pp. 2259–2265, 2003, doi: 10.1016/S0016-2361(03)00194-7.
  • J. M. Fox, “Fly Ash Classification-Old and New Ideas,” in Fly Ash Classification – Old and New Ideas, 2017, pp. 1–15.
  • A. M. Zeyad, “Effect of curing methods in hot weather on the properties of high-strength concretes,” J. King Saud Univ. - Eng. Sci., vol. 31, no. 3, pp. 218–223, 2019, doi: 10.1016/j.jksues.2017.04.004.
  • B. A. Young, A. Hall, L. Pilon, P. Gupta, and G. Sant, “Can the compressive strength of concrete be estimated from knowledge of the mixture proportions?: New insights from statistical analysis and machine learning methods,” Cem. Concr. Res., vol. 115, no. July, pp. 379–388, 2019, doi: 10.1016/j.cemconres.2018.09.006.
  • M. A. DeRousseau, J. R. Kasprzyk, and W. V. Srubar, “Computational design optimization of concrete mixtures: A review,” Cem. Concr. Res., vol. 109, pp. 42–53, 2018, doi: 10.1016/j.cemconres.2018.04.007.
  • M. Z. Naser, “An engineer’s guide to eXplainable Artificial Intelligence and Interpretable Machine Learning: Navigating causality, forced goodness, and the false perception of inference,” Autom. Constr., vol. 129, no. September, 2021, doi: 10.1016/j.autcon.2021.103821.
  • K. Khan, W. Ahmad, M. N. Amin, F. Aslam, A. Ahmad, and M. A. Al-Faiad, “Comparison of Prediction Models Based on Machine Learning for the Compressive Strength Estimation of Recycled Aggregate Concrete,” Materials (Basel)., vol. 15, no. 10, pp. 1–36, 2022, doi: 10.3390/ma15103430.
  • L. Chi et al., “Machine learning prediction of compressive strength of concrete with resistivity modification,” Mater. Today Commun., vol. 36, p. 106470, Aug. 2023, doi: 10.1016/j.mtcomm.2023.106470.
  • P. G. Asteris, A. D. Skentou, A. Bardhan, P. Samui, and K. Pilakoutas, “Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models,” Cem. Concr. Res., vol. 145, no. October 2020, p. 106449, 2021, doi: 10.1016/j.cemconres.2021.106449.
  • D.-C. Feng et al., “Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach,” Constr. Build. Mater., vol. 230, p. 117000, Jan. 2020, doi: 10.1016/j.conbuildmat.2019.117000.
  • H. N. Muliauwan, D. Prayogo, G. Gaby, and K. Harsono, “Prediction of Concrete Compressive Strength Using Artificial Intelligence Methods,” J. Phys. Conf. Ser., vol. 1625, no. 1, p. 012018, Sep. 2020, doi: 10.1088/1742-6596/1625/1/012018.
  • Z. Zeng et al., “Accurate prediction of concrete compressive strength based on explainable features using deep learning,” Constr. Build. Mater., vol. 329, 2022, doi: 10.1016/j.conbuildmat.2022.127082.
  • I.-C. Yeh, “Modeling of Strength of High-Performance Concrete Using Artificial Neural Networks,” Cem. Concr. Res., vol. 28, no. 12, pp. 1797–1808, 1998.
  • I.-C. Yeh, “Concrete Compressive Strength.” UCI Machine Learning Repository, 2007, doi: 10.24432/C5PK67.
  • D. Maulud and A. M. Abdulazeez, “A Review on Linear Regression Comprehensive in Machine Learning,” J. Appl. Sci. Technol. Trends, vol. 1, no. 4, pp. 140–147, 2020, doi: 10.38094/jastt1457.
  • E. Pekel, “Estimation of soil moisture using decision tree regression,” Theor. Appl. Climatol., vol. 139, no. 3–4, pp. 1111–1119, 2020, doi: 10.1007/s00704-019-03048-8.
  • M. Rakhra et al., “Crop Price Prediction Using Random Forest and Decision Tree Regression:-A Review,” Mater. Today Proc., no. xxxx, 2021, doi: 10.1016/j.matpr.2021.03.261.
  • V. Rodriguez-Galiano, M. Sanchez-Castillo, M. Chica-Olmo, and M. Chica-Rivas, “Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines,” Ore Geol. Rev., vol. 71, pp. 804–818, 2015, doi: 10.1016/j.oregeorev.2015.01.001.
  • B. Singh, P. Sihag, and K. Singh, “Modelling of impact of water quality on infiltration rate of soil by random forest regression,” Model. Earth Syst. Environ., vol. 3, no. 3, pp. 999–1004, 2017, doi: 10.1007/s40808-017-0347-3.
  • J. Cai, K. Xu, Y. Zhu, F. Hu, and L. Li, “Prediction and analysis of net ecosystem carbon exchange based on gradient boosting regression and random forest,” Appl. Energy, vol. 262, no. 114566, pp. 1–14, 2020, doi: 10.1016/j.apenergy.2020.114566.
  • U. Singh, M. Rizwan, M. Alaraj, and I. Alsaidan, “A machine learning-based gradient boosting regression approach for wind power production forecasting: A step towards smart grid environments,” Energies, vol. 14, no. 16, pp. 1–21, 2021, doi: 10.3390/en14165196.
  • L. E. de Oliveira Aparecido, G. de Souza Rolim, J. R. da Silva Cabral De Moraes, C. T. S. Costa, and P. S. de Souza, “Machine learning algorithms for forecasting the incidence of Coffea arabica pests and diseases,” Int. J. Biometeorol., vol. 64, no. 4, pp. 671–688, 2020, doi: 10.1007/s00484-019-01856-1.
  • C. Araújo, C. Soares, I. Pereira, D. Coelho, M. Â. Rebelo, and A. Madureira, “A Novel Approach for Send Time Prediction on Email Marketing,” Appl. Sci., vol. 12, no. 8310, pp. 1–13, 2022, doi: 10.3390/app12168310.
  • A. O. Oyedeji, A. M. Salami, O. Folorunsho, and O. R. Abolade, “Analysis and Prediction of Student Academic Performance Using Machine Learning,” JITCE (Journal Inf. Technol. Comput. Eng., vol. 4, no. 01, pp. 10–15, 2020, doi: 10.25077/jitce.4.01.10-15.2020.
There are 32 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Articles
Authors

Ajibola Oyedejı 0000-0002-0180-492X

Adekunle David 0000-0002-5803-708X

Ositola Osifeko 0000-0002-9350-6056

Abisola Olayiwola 0000-0002-1585-0863

Omobolaji Opafola 0000-0003-4896-512X

Early Pub Date August 23, 2024
Publication Date August 31, 2024
Submission Date January 18, 2024
Acceptance Date July 10, 2024
Published in Issue Year 2024

Cite

IEEE A. Oyedejı, A. David, O. Osifeko, A. Olayiwola, and O. Opafola, “Comparative Analysis of Machine Learning Techniques for Prediction of the Compressive Strength of Field Concrete”, SAUCIS, vol. 7, no. 2, pp. 127–137, 2024, doi: 10.35377/saucis...1415583.

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