Predicting the compressive strength of self-compacting concrete using artificial intelligence techniques: A review

Yıl 2024, Cilt: 8 Sayı: 3, 537 – 550, 28.07.2024

Terlumun Sesugh , Michael Onyia , Okafor Fidelis

https://doi.org/10.31127/tuje.1422225

Öz

Concrete is one of the most common construction materials used all over the word. In estimating the strength properties of concrete, laboratory works need to be carried out. However, researchers have adopted predictive models in order to minimize the rigorous laboratory works in estimating the compressive strength and other properties of concrete. Self-compacting concrete which is an advanced form of construction is adopted mainly in areas where vibrations may not be possible due to complexity of the form work or reinforcement. This work is targeted at predicting the compressive strength of self-compacting concrete using artificial intelligence techniques. A comparative performance analysis of all techniques is presented. The outcomes demonstrated that training in a Deep Neural Network model with several hidden layers could enhance the performance of the suggested model. The artificial neural network (ANN) model, possesses a high degree of steadiness when compared to experimental results of concrete compressive strength. ANN was observed to be a strong predictive tool, as such is recommended for formulation of many civil engineering properties that requires predictions. Much time and resources are saved with artificial intelligence models as it eliminates the need for experimental test which sometimes delay construction works.

Anahtar Kelimeler

Compression strength prediction, Self-compacting concrete, Artificial intelligence

Etik Beyan

THE AUTHORS DECLARE THAT THE WORK IS ORIGINAL AND THERE IS NO CONFLICT OF INTEREST

Destekleyen Kurum

NILL

Proje Numarası

NILL

Teşekkür

THANKS TO THE DEPARTMENT OF CIVIL ENGINEERING, UNIVERSITY OF NIGERIA NSUKKA FOR THE OPPOTURNITY TO CARRY OUT THIS RESEARCH. OUR APPRECIATION TO THE TURKISH JOURNAL OF ENGINEERING FOR THE OPPORTUNITY TO PUBLISH OUR RESEARCH WORK IN THIS REPUTABLE JOURNAL. THANKS

Kaynakça

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• Tufail, R. F., Naeem, M. H., Ahmad, J., Waheed, H., Majdi, A., Farooq, D., … & Butt, F. (2022). Evaluation of the fresh and mechanical properties of nano-engineered self compacting concrete containing graphite nano/micro platelets. Case Studies in Construction Materials, 17, e01165. https://doi.org/10.1016/j.cscm.2022.e01165
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Yıl 2024, Cilt: 8 Sayı: 3, 537 – 550, 28.07.2024

Terlumun Sesugh , Michael Onyia , Okafor Fidelis

https://doi.org/10.31127/tuje.1422225

Öz

Proje Numarası

NILL

Kaynakça

• Gaimster, R., & Dixon, N. (2003). Self-compacting concrete. Advanced Concrete Technology, 3, 1-23. https://doi.org/10.1016/B978-075065686-3/50295-0
• Falliano, D., De Domenico, D., Ricciardi, G., & Gugliandolo, E. (2018). Experimental investigation on the compressive strength of foamed concrete: Effect of curing conditions, cement type, foaming agent and dry density. Construction and Building Materials, 165, 735-749. https://doi.org/10.1016/j.conbuildmat.2017.12.241
• Lee, S. C. (2003). Prediction of concrete strength using artificial neural networks. Engineering Structures, 25(7), 849-857. https://doi.org/10.1016/S0141-0296(03)00004-X
• Madandoust, R., & Mousavi, S. Y. (2012). Fresh and hardened properties of self-compacting concrete containing metakaolin. Construction and Building Materials, 35, 752-760. https://doi.org/10.1016/j.conbuildmat.2012.04.109
• Tufail, R. F., Naeem, M. H., Ahmad, J., Waheed, H., Majdi, A., Farooq, D., … & Butt, F. (2022). Evaluation of the fresh and mechanical properties of nano-engineered self compacting concrete containing graphite nano/micro platelets. Case Studies in Construction Materials, 17, e01165. https://doi.org/10.1016/j.cscm.2022.e01165
• Shi, C., Wu, Z., Lv, K., & Wu, L. (2015). A review on mixture design methods for self-compacting concrete. Construction and Building Materials, 84, 387-398. https://doi.org/10.1016/j.conbuildmat.2015.03.079
• Hamada, H., Alattar, A., Tayeh, B., Yahaya, F., & Thomas, B. (2022). Effect of recycled waste glass on the properties of high-performance concrete: A critical review. Case Studies in Construction Materials, 17, e01149. https://doi.org/10.1016/j.cscm.2022.e01149
• Efnarc, S. (2002). Guidelines for Self-Compacting Concrete, Rep. from EFNARC. 44, 32.
• Tejaswini, G. L. S., & Rao, A. V. (2020). A detailed report on various behavioral aspects of self-compacting concrete. Materials Today: Proceedings, 33, 839-844. https://doi.org/10.1016/j.matpr.2020.06.273
• Danish, P., & Ganesh, G. M. (2021). Self-compacting concrete—optimization of mix design procedure by the modifications of rational method. In 3rd International Conference on Innovative Technologies for Clean and Sustainable Development: ITCSD 2020 3, 369-396. https://doi.org/10.1007/978-3-030-51485-3_25
• 1Esmaeilkhanian, B., Khayat, K. H., Yahia, A., & Feys, D. (2014). Effects of mix design parameters and rheological properties on dynamic stability of self-consolidating concrete. Cement and Concrete Composites, 54, 21-28. https://doi.org/10.1016/j.cemconcomp.2014.03.001
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• Khandelwal, M., Shirani Faradonbeh, R., Monjezi, M., Armaghani, D. J., Majid, M. Z. B. A., & Yagiz, S. (2017). Function development for appraising brittleness of intact rocks using genetic programming and non-linear multiple regression models. Engineering with Computers, 33, 13-21. https://doi.org/10.1007/s00366-016-0452-3
• Ferreira, C. (2002). Gene expression programming in problem solving. Soft Computing and Industry: Recent Applications, 635-653. https://doi.org/10.1007/978-1-4471-0123-9_54
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• Gholampour, A., Gandomi, A. H., & Ozbakkaloglu, T. (2017). New formulations for mechanical properties of recycled aggregate concrete using gene expression programming. Construction and Building Materials, 130, 122-145. https://doi.org/10.1016/j.conbuildmat.2016.10.114
• Asteris, P. G., Skentou, A. D., Bardhan, A., Samui, P., & Pilakoutas, K. (2021). Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models. Cement and Concrete Research, 145, 106449. https://doi.org/10.1016/j.cemconres.2021.106449
• Neira, P., Bennun, L., Pradena, M., & Gomez, J. (2020). Predviđanje tlačne čvrstoće betona pomoću umjetnih neuronskih mreža. Građevinar, 72(07), 585-592. https://doi.org/10.14256/JCE.2438.2018
• Mehmannavaz, T., Khalilikhorram, V., Sajjadi, S. M., & Samadi, M. (2014). Presenting an Appropriate Neural Network for Optimal Mix Design of Roller Compacted Concrete Dams. Research Journal of Applied Sciences, Engineering and Technology, 7(9), 1872-1877. https://doi.org/10.19026/rjaset.7.475
• Khan, M. I. (2012). Predicting properties of high performance concrete containing composite cementitious materials using artificial neural networks. Automation in Construction, 22, 516-524. https://doi.org/10.1016/j.autcon.2011.11.011
• Onyelowe, K. C., Iqbal, M., Jalal, F. E., Onyia, M. E., & Onuoha, I. C. (2021). Application of 3-algorithm ANN programming to predict the strength performance of hydrated-lime activated rice husk ash treated soil. Multiscale and Multidisciplinary Modeling, Experiments and Design, 4, 259-274. https://doi.org/10.1007/s41939-021-00093-7
• Prasad, B. R., Eskandari, H., & Reddy, B. V. (2009). Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN. Construction and Building Materials, 23(1), 117-128. https://doi.org/10.1016/j.conbuildmat.2008.01.014
• Nguyen, T., Kashani, A., Ngo, T., & Bordas, S. (2019). Deep neural network with high‐order neuron for the prediction of foamed concrete strength. Computer‐Aided Civil and Infrastructure Engineering, 34(4), 316-332. https://doi.org/10.1111/mice.12422
• Dias, W. P. S., & Pooliyadda, S. P. (2001). Neural networks for predicting properties of concretes with admixtures. Construction and Building Materials, 15(7), 371-379. https://doi.org/10.1016/S0950-0618(01)00006-X
• Abunassar, N., Alas, M., & Ali, S. I. A. (2023). Prediction of compressive strength in self-compacting concrete containing fly ash and silica fume using ANN and SVM. Arabian Journal for Science and Engineering, 48(4), 5171-5184. https://doi.org/10.1007/s13369-022-07359-3
• Yaman, M. A., Abd Elaty, M., & Taman, M. (2017). Predicting the ingredients of self compacting concrete using artificial neural network. Alexandria Engineering Journal, 56(4), 523-532. https://doi.org/10.1016/j.aej.2017.04.007
• Belalia Douma, O., Boukhatem, B., Ghrici, M., & Tagnit-Hamou, A. (2017). Prediction of properties of self-compacting concrete containing fly ash using artificial neural network. Neural Computing and Applications, 28, 707-718. https://doi.org/10.1007/s00521-016-2368-7
• Golafshani, E. M., Behnood, A., & Arashpour, M. (2020). Predicting the compressive strength of normal and High-Performance Concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer. Construction and Building Materials, 232, 117266. https://doi.org/10.1016/j.conbuildmat.2019.117266
• Demir, V., & Doğu, R. (2024). Prediction of elevation points using three different heuristic regression techniques. Turkish Journal of Engineering, 8(1), 56-64. https://doi.org/10.31127/tuje.1257847
• Salami, B. A., Iqbal, M., Abdulraheem, A., Jalal, F. E., Alimi, W., Jamal, A., … & Bardhan, A. (2022). Estimating compressive strength of lightweight foamed concrete using neural, genetic and ensemble machine learning approaches. Cement and Concrete Composites, 133, 104721. https://doi.org/10.1016/j.cemconcomp.2022.104721
• Shariati, M., Armaghani, D. J., Khandelwal, M., Zhou, J., & Khorami, M. (2021). Assessment of longstanding effects of fly ash and silica fume on the compressive strength of concrete using extreme learning machine and artificial neural network. Journal of Advanced Engineering and Computation, 5(1), 50-74. http://dx.doi.org/10.25073/jaec.202151.308
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