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

Yıl 2024, Cilt: 8 Sayı: 3, 537 - 550

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

• 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
• Ashish, D. K., & Verma, S. K. (2019). An overview on mixture design of self‐compacting concrete. Structural Concrete, 20(1), 371-395. https://doi.org/10.1002/suco.201700279
• Bayer, İ. R., Turanlı, L., & Mehta, P. K. (2019). Mass concrete construction using self-compacting mortar. Turkish Journal of Engineering, 3(3), 110-119. https://doi.org/10.31127/tuje.462548
• Kandiri, A., Golafshani, E. M., & Behnood, A. (2020). Estimation of the compressive strength of concretes containing ground granulated blast furnace slag using hybridized multi-objective ANN and salp swarm algorithm. Construction and Building Materials, 248, 118676. https://doi.org/10.1016/j.conbuildmat.2020.118676
• Golafshani, E. M., Rahai, A., & Sebt, M. H. (2015). Artificial neural network and genetic programming for predicting the bond strength of GFRP bars in concrete. Materials and Structures, 48, 1581-1602. https://doi.org/10.1617/s11527-014-0256-0
• Gesoğlu, M., Güneyisi, E., Özturan, T., & Özbay, E. (2010). Modeling the mechanical properties of rubberized concretes by neural network and genetic programming. Materials and Structures, 43, 31-45. https://doi.org/10.1617/s11527-009-9468-0
• 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
• Siddique, R., Aggarwal, P., & Aggarwal, Y. (2011). Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks. Advances in Engineering software, 42(10), 780-786. https://doi.org/10.1016/j.advengsoft.2011.05.016
• Behnood, A., & Golafshani, E. M. (2018). Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves. Journal of Cleaner Production, 202, 54-64. https://doi.org/10.1016/j.jclepro.2018.08.065
• Chakravarthy HG, N., Seenappa, K. M., Naganna, S. R., & Pruthviraja, D. (2023). Machine Learning Models for the Prediction of the Compressive Strength of Self-Compacting Concrete Incorporating Incinerated Bio-Medical Waste Ash. Sustainability, 15(18), 13621. https://doi.org/10.3390/su151813621
• Behnood, A., Behnood, V., Gharehveran, M. M., & Alyamac, K. E. (2017). Prediction of the compressive strength of normal and high-performance concretes using M5P model tree algorithm. Construction and Building Materials, 142, 199-207. https://doi.org/10.1016/j.conbuildmat.2017.03.061
• Kovačević, M., Lozančić, S., Nyarko, E. K., & Hadzima-Nyarko, M. (2021). Modeling of compressive strength of self-compacting rubberized concrete using machine learning. Materials, 14(15), 4346. https://doi.org/10.3390/ma14154346
• Aiyer, B. G., Kim, D., Karingattikkal, N., Samui, P., & Rao, P. R. (2014). Prediction of compressive strength of self-compacting concrete using least square support vector machine and relevance vector machine. KSCE Journal of Civil Engineering, 18, 1753-1758. https://doi.org/10.1007/s12205-014-0524-0
• 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
• Ikeagwuani, C. C. (2021). Estimation of modified expansive soil CBR with multivariate adaptive regression splines, random forest and gradient boosting machine. Innovative Infrastructure Solutions, 6(4), 199. https://doi.org/10.1007/s41062-021-00568-z
• Onyia, M. E., Ambrose, E. E., Okafor, F. O., & Udo, J. J. (2023). Mathematical modelling of compressive strength of recycled ceramic tile aggregate concrete using modified regression theory. Journal of Applied Sciences and Environmental Management, 27(1), 33-42. https://doi.org/10.4314/jasem.v27i1.6
• Aicha, M. B., Al Asri, Y., Zaher, M., Alaoui, A. H., & Burtschell, Y. (2022). Prediction of rheological behavior of self-compacting concrete by multi-variable regression and artificial neural networks. Powder Technology, 401, 117345. https://doi.org/10.1016/j.powtec.2022.117345
• Patel, R., Hossain, K. M. A., Shehata, M., Bouzoubaa, N., & Lachemi, M. (2004). Development of statistical models for mixture design of high-volume fly ash self-consolidating concrete. Materials Journal, 101(4), 294-302. https://doi.org/10.14359/13363
• Razavi Tosee, S. V., & Nikoo, M. (2019). Neuro-fuzzy systems in determining light weight concrete strength. Journal of Central South University, 26(10), 2906-2914. https://doi.org/10.1007/s11771-019-4223-3
• Günal, A. Y., & Mehdi, R. (2024). Application of a new fuzzy logic model known as" SMRGT" for estimating flow coefficient rate. Turkish Journal of Engineering, 8(1), 46-55. https://doi.org/10.31127/tuje.1225795
• Zhou, Q., Wang, F., & Zhu, F. (2016). Estimation of compressive strength of hollow concrete masonry prisms using artificial neural networks and adaptive neuro-fuzzy inference systems. Construction and Building Materials, 125, 417-426. https://doi.org/10.1016/j.conbuildmat.2016.08.064
• Behnood, A., & Golafshani, E. M. (2020). Machine learning study of the mechanical properties of concretes containing waste foundry sand. Construction and Building Materials, 243, 118152. https://doi.org/10.1016/j.conbuildmat.2020.118152
• Madani, H., Kooshafar, M., & Emadi, M. (2020). Compressive strength prediction of nanosilica-incorporated cement mixtures using adaptive neuro-fuzzy inference system and artificial neural network models. Practice Periodical on Structural Design and Construction, 25(3), 04020021. https://doi.org/10.1061/(ASCE)SC.1943-5576.0000499
• Chiew, F. H., Ng, C. K., Chai, K. C., & Tay, K. M. (2017). A fuzzy adaptive resonance theory‐based model for mix proportion estimation of high‐performance concrete. Computer‐Aided Civil and Infrastructure Engineering, 32(9), 772-786. https://doi.org/10.1111/mice.12288
• 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
• Lai, S., & Serra, M. (1997). Concrete strength prediction by means of neural network. Construction and Building Materials, 11(2), 93-98. https://doi.org/10.1016/S0950-0618(97)00007-X
• Ibrahim, S. M., Ansari, S. S., & Hasan, S. D. (2023). Towards white box modeling of compressive strength of sustainable ternary cement concrete using explainable artificial intelligence (XAI). Applied Soft Computing, 149, 110997. https://doi.org/10.1016/j.asoc.2023.110997
• Erzin, Y. (2007). Artificial neural networks approach for swell pressure versus soil suction behaviour. Canadian Geotechnical Journal, 44(10), 1215-1223. https://doi.org/10.1139/T07-052
• Silva, F. A., Delgado, J. M., Cavalcanti, R. S., Azevedo, A. C., Guimarães, A. S., & Lima, A. G. (2021). Use of nondestructive testing of ultrasound and artificial neural networks to estimate compressive strength of concrete. Buildings, 11(2), 44. https://doi.org/10.3390/buildings11020044
• Duan, Z. H., Kou, S. C., & Poon, C. S. (2013). Prediction of compressive strength of recycled aggregate concrete using artificial neural networks. Construction and Building Materials, 40, 1200-1206. https://doi.org/10.1016/j.conbuildmat.2012.04.063
• Alade, I. O., Bagudu, A., Oyehan, T. A., Abd Rahman, M. A., Saleh, T. A., & Olatunji, S. O. (2018). Estimating the refractive index of oxygenated and deoxygenated hemoglobin using genetic algorithm–support vector regression model. Computer Methods and Programs in Biomedicine, 163, 135-142. https://doi.org/10.1016/j.cmpb.2018.05.029
• Iqbal, M. F., Liu, Q. F., Azim, I., Zhu, X., Yang, J., Javed, M. F., & Rauf, M. (2020). Prediction of mechanical properties of green concrete incorporating waste foundry sand based on gene expression programming. Journal of Hazardous Materials, 384, 121322. https://doi.org/10.1016/j.jhazmat.2019.121322
• Sarıdemir, M. (2010). Genetic programming approach for prediction of compressive strength of concretes containing rice husk ash. Construction and Building Materials, 24(10), 1911-1919. https://doi.org/10.1016/j.conbuildmat.2010.04.011
• Gandomi, A. H., & Roke, D. A. (2015). Assessment of artificial neural network and genetic programming as predictive tools. Advances in Engineering Software, 88, 63-72. https://doi.org/10.1016/j.advengsoft.2015.05.007
• Khan, M. A., Zafar, A., Akbar, A., Javed, M. F., & Mosavi, A. (2021). Application of Gene Expression Programming (GEP) for the prediction of compressive strength of geopolymer concrete. Materials, 14(5), 1106. https://doi.org/10.3390/ma14051106
• Faradonbeh, R. S., Hasanipanah, M., Amnieh, H. B., Armaghani, D. J., & Monjezi, M. (2018). Development of GP and GEP models to estimate an environmental issue induced by blasting operation. Environmental Monitoring and Assessment, 190, 1-15. https://doi.org/10.1007/s10661-018-6719-y
• 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
• Ferreira, C. (2006). Gene expression programming: mathematical modeling by an artificial intelligence, 21. Springer.
• 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
• Mogaraju, J. K. (2024). Machine learning empowered prediction of geolocation using groundwater quality variables over YSR district of India. Turkish Journal of Engineering, 8(1), 31-45. https://doi.org/10.31127/tuje.1223779
• Acı, M., Acı, Ç. İ., & Avcı, M. (2018). Performance comparison of ANFIS, ANN, SVR, CART AND MLR techniques for geometry optimization of carbon nanotubes using CASTEP. Turkish Journal of Engineering, 2(3), 119-124. https://doi.org/10.31127/tuje.408976
• Kiani, B., Gandomi, A. H., Sajedi, S., & Liang, R. Y. (2016). New formulation of compressive strength of preformed-foam cellular concrete: an evolutionary approach. Journal of Materials in Civil Engineering, 28(10), 04016092. https://doi.org/10.1061/(ASCE)MT.1943-5533.0001602
• Nehdi, M., Djebbar, Y., & Khan, A. J. M. J. (2001). Neural network model for preformed-foam cellular concrete. Materials Journal, 98(5), 402-409.
• Othman, M. M. (2023). Modeling of daily groundwater level using deep learning neural networks. Turkish Journal of Engineering, 7(4), 331-337. https://doi.org/10.31127/tuje.1169908
• Tiza, M. T., Ogunleye, E., Jiya, V. H., Onuzulike, C., Akande, E. O., & Terlumun, S. (2023). Integrating Sustainability into Civil Engineering and the Construction Industry. Journal of Cement Based Composites, 4(1), 1-11. https://doi.org/10.36937/cebacom.2023.5756
• Algaifi, H. A., Bakar, S. A., Alyousef, R., Sam, A. R. M., Alqarni, A. S., Ibrahim, M., ... & Salami, B. A. (2021). Machine learning and RSM models for prediction of compressive strength of smart bio-concrete. Smart Structural System, 28, 535-551. https://doi.org/10.12989/sss.2021.28.4.535
• Chou, J. S., & Pham, A. D. (2013). Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength. Construction and Building Materials, 49, 554-563. https://doi.org/10.1016/j.conbuildmat.2013.08.078
• Imam, A., Salami, B. A., & Oyehan, T. A. (2021). Predicting the compressive strength of a quaternary blend concrete using Bayesian regularized neural network. Journal of Structural Integrity and Maintenance, 6(4), 237-246. https://doi.org/10.1080/24705314.2021.1892572
• Ly, H. B., Nguyen, M. H., & Pham, B. T. (2021). Metaheuristic optimization of Levenberg–Marquardt-based artificial neural network using particle swarm optimization for prediction of foamed concrete compressive strength. Neural Computing and Applications, 33(24), 17331-17351. https://doi.org/10.1007/s00521-021-06321-y
• Shariati, M., Mafipour, M. S., Mehrabi, P., Bahadori, A., Zandi, Y., Salih, M. N., ... & Poi-Ngian, S. (2019). Application of a hybrid artificial neural network-particle swarm optimization (ANN-PSO) model in behavior prediction of channel shear connectors embedded in normal and high-strength concrete. Applied Sciences, 9(24), 5534. https://doi.org/10.3390/app9245534
• Shariati, M., Mafipour, M. S., Mehrabi, P., Shariati, A., Toghroli, A., Trung, N. T., & Salih, M. N. (2021). A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques. Engineering with Computers, 37, 2089-2109. https://doi.org/10.1007/s00366-019-00930-x
• Shariati, M., Mafipour, M. S., Mehrabi, P., Ahmadi, M., Wakil, K., Trung, N. T., & Toghroli, A. (2020). Prediction of concrete strength in presence of furnace slag and fly ash using Hybrid ANN-GA (Artificial Neural Network-Genetic Algorithm). Smart Structures and Systems, An International Journal, 25(2), 183-195. https://doi.org/10.12989/sss.2020.25.2.183
• Pham, A. D., Ngo, N. T., Nguyen, Q. T., & Truong, N. S. (2020). Hybrid machine learning for predicting strength of sustainable concrete. Soft Computing, 24(19), 14965-14980. https://doi.org/10.1007/s00500-020-04848-1
• Babajanzadeh, M., & Azizifar, V. (2018). Compressive strength prediction of self-compacting concrete incorporating silica fume using artificial intelligence methods. Civil Engineering Journal, 4(7), 1542-1552. http://dx.doi.org/10.28991/cej-0309193
• Özcan, F., Atiş, C. D., Karahan, O., Uncuoğlu, E., & Tanyildizi, H. (2009). Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete. Advances in Engineering Software, 40(9), 856-863. https://doi.org/10.1016/j.advengsoft.2009.01.005