Steel and Composite Structures

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Predicting tensile strength of reinforced concrete composited with geopolymer using several machine learning algorithms

  • Ibrahim Albaijan (Mechanical Engineering Department, College of Engineering at Al-Kharj, Prince Sattam Bin Abdulaziz University) ;
  • Hanan Samadi (IRO, Civil Engineering Department, University of Halabja) ;
  • Arsalan Mahmoodzadeh (IRO, Civil Engineering Department, University of Halabja) ;
  • Danial Fakhri (Department of Applied Sciences, Universite du Quebec a Chicoutimi) ;
  • Mehdi Hosseinzadeh (Institute of Research and Development, Duy Tan University) ;
  • Nejib Ghazouani (Department of Civil Engineering, College of Engineering, Northern Border University) ;
  • Khaled Mohamed Elhadi (Civil Engineering Department, College of Engineering, King Khalid University)
  • Received : 2024.11.15
  • Accepted : 2024.07.22
  • Published : 2024.08.10
⟨ Previous Next ⟩

Abstract

Researchers are actively investigating the potential for utilizing alternative materials in construction to tackle the environmental and economic challenges linked to traditional concrete-based materials. Nevertheless, conventional laboratory methods for testing the mechanical properties of concrete are both costly and time-consuming. The limitations of traditional models in predicting the tensile strength of concrete composited with geopolymer have created a demand for more advanced models. Fortunately, the increasing availability of data has facilitated the use of machine learning methods, which offer powerful and cost-effective models. This paper aims to explore the potential of several machine learning methods in predicting the tensile strength of geopolymer concrete under different curing conditions. The study utilizes a dataset of 221 tensile strength test results for geopolymer concrete with varying mix ratios and curing conditions. The effectiveness of the machine learning models is evaluated using additional unseen datasets. Based on the values of loss functions and evaluation metrics, the results indicate that most models have the potential to estimate the tensile strength of geopolymer concrete satisfactorily. However, the Takagi Sugeno fuzzy model (TSF) and gene expression programming (GEP) models demonstrate the highest robustness. Both the laboratory tests and machine learning outcomes indicate that geopolymer concrete composed of 50% fly ash and 40% ground granulated blast slag, mixed with 10 mol of NaOH, and cured in an oven at 190°F for 28 days has superior tensile strength.

Keywords

Acknowledgement

The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/213/45. The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA for funding this research work through the project number "NBU-FFR-2024-2105-07".

References

  1. Abdalla, A. and Salih, A. (2022), "Implementation of multi-expression programming (MEP), artificial neural network (ANN), and M5P-tree to forecast the compression strength cement-based mortar modified by calcium hydroxide at different mix proportions and curing ages", Innov. Infra. Sol., 7(2), 153. https://doi.org/10.1007/s41062-022-00761-8. 
  2. Addou, F.Y., Bourada, F., Meradjah, M., Bousahla, A.A., Tounsi, A., Ghazwani, M.H. and Alnujaie A. (2023), "Impact of porosity distribution on static behavior of functionally graded plates using a simple quasi-3D HSDT", Comput. Concrete, 32(1), 87-97. https://doi.org/10.12989/cac.2023.32.1.087. 
  3. Ahmad, A., Ahmad, W., Aslam, F. and Joyklad, P. (2022), "Compressive strength prediction of fly ash-based geopolymer concrete via advanced machine learning techniques", Case Study. Cons. Mater., 16, e00840. https://doi.org/10.1016/j.cscm.2021.e00840. 
  4. Ahmad, M.W., Reynolds, J. and Rezgui, Y. (2018), "Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees", J. Clean. Product., 203, 810-821. https://doi.org/10.1016/j.jclepro.2018.08.207. 
  5. Albaijan, I., Samadi, H., Mahmood, F.M.Z., Mahmoodzadeh, A., Fakhri, D., Ibrahim, H.H. and El Ouni, M.H. (2024), "Evaluation of concrete's fracture toughness under an acidic environment condition using advanced machine learning algorithms", Eng. Frac. Mech., 298, 109948. https://doi.org/10.1016/j.engfracmech.2024.109948. 
  6. Alsubaie, A.M., Alfaqih, I., Al-Osta, M.A., Tounsi, A., Chikh, A., Mudhaffar, I.M. and Tahir, S. (2023), "Porosity-dependent vibration investigation of functionally graded carbon nanotube-reinforced composite beam", Comput. Concrete., 32(1), 75-85. https://doi.org/10.12989/cac.2023.32.1.075. 
  7. Andrew, R.M. (2018), "Global CO2 emissions from cement production", Earth Syst. Sci. Data., 10(1), 195-217. https://doi.org/10.5194/essd-10-195-2018. 
  8. Armaghani, D.J. and Asteris, P.G. (2021), "A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength", Neural Comput. Appl., 33(9), 4501-4532. https://doi.org/10.1007/s00521-020-05244-4. 
  9. Asteris, P.G., Cavaleri, L., Ly, H.B. and Pham, B.T. (2021), "Surrogate models for the compressive strength mapping of cement mortar materials", Soft. Comput., 25(8), 6347-6372. https://doi.org/10.1007/s00500-021-05626-3. 
  10. Awoyera, P.O., Kirgiz, M.S., Viloria, A. and Ovallos-Gazabon, D. (2020), "Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques", J. Mater. Res. Techno., 9(4), 9016-9028. https://doi.org/https://doi.org/10.1016/j.jmrt.2020.06.008. 
  11. Bae, H.J. and Koumoutsakos, P. (2022), "Scientific multi-agent reinforcement learning for wall-models of turbulent flows", Nat. Commun., 13(1), 1443. https://doi.org/10.1038/s41467-022-28957-7. 
  12. Bakharev, T. (2005), "Geopolymeric materials prepared using Class F fly ash and elevated temperature curing", Cement. Conc. Research., 35(6), 1224-1232. https://doi.org/https://doi.org/10.1016/j.cemconres.2004.06.031. 
  13. Bellum, R.R., Nerella, R., Madduru, S.R. and Indukuri, C.S. (2019), "Mix Design and mechanical properties of fly ash and GGBFS-synthesized alkali-activated concrete (AAC)", Infra., 4(2). https://doi.org/10.3390/infrastructures4020020. 
  14. Beskopylny, A.N., Stel'makh, S.A., Shcherban, E.M., Mailyan, L. R., Meskhi, B., Razveeva, I., Chernil'nik, A. and Beskopylny, N. (2022), "Concrete strength prediction using machine learning methods catboost, k-nearest neighbors, support vector regression", Appl. Sci., 12(21). https://www.scilit.net/article/ea5f104868ec18d367ad7ce0c949831d.  104868ec18d367ad7ce0c949831d
  15. Bishop, C.M. (2006), Pattern Recognition and Machine Learning, Springer New York, NY. 
  16. Bui, D.K., Nguyen, T., Chou, J.S., Nguyen-Xuan, H. and Ngo, T.D. (2018), "A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete", Cons. Build. Mater., 180, 320-333. https://doi.org/https://doi.org/10.1016/j.conbuildmat.2018.05.201. 
  17. Caliskan, A., Demirhan, S. and Tekin, R. (2022), "Comparison of different machine learning methods for estimating compressive strength of mortars", Cons. Build. Mater., 335, 127490. https://doi.org/https://doi.org/10.1016/j.conbuildmat.2022.127490. 
  18. Chen, T. and Guestrin, C. (2016), "XGBoost: A scalable tree boosting system", Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, USA. 
  19. Cortes, C. and Vapnik, V. (1995), "Support-vector networks", Machine Learning, 20(3), 273-297. https://doi.org/10.1007/BF00994018. 
  20. Cover, T. and Hart, P. (1967), "Nearest neighbor pattern classification", IEEE Trans. Infor. Theo., 13(1), 21-27. https://doi.org/10.1109/TIT.1967.1053964. 
  21. Dai, B., Xu, Z., Zeng, J., Zandi, Y., Rahimi, A., Pourkhorshidi, S., Khadimallah, M.A., Zhao X. and El-Arab, I.E. (2021), "Moment-rotational analysis of soil during mining induced ground movements by hybrid machine learning assisted quantification models of ELM-SVM", Steel Compos. Struct., 41(6), 831-850. https://doi.org/10.12989/scs.2021.5.41.831. 
  22. Dai, Y., Roy, K., Fang, Z., Chen, B., Raftery, G.M. and Lim, J.B.P. (2022), "A novel machine learning model to predict the moment capacity of cold-formed steel channel beams with edge-stiffened and un-stiffened web holes", J. Build. Eng., 53, 104592. https://doi.org/https://doi.org/10.1016/j.jobe.2022.104592. 
  23. Dao, D.V., Adeli, H., Ly, H.-B., Le, L.M., Le, V.M., Le, T.T. and Pham, B.T. (2020), "A Sensitivity and robustness analysis of gpr and ann for high-performance concrete compressive strength prediction using a monte carlo simulation", Sustain., 12(3). https://doi.org/10.3390/su12030830. 
  24. Davidovits, J. (2013), "Geopolymer cement", Geopoly. Sci. Tech., 21.
  25. de-Prado-Gil, J., Palencia, C., Jagadesh, P. and Martinez-Garcia, R. (2022), "A Comparison of machine learning tools that model the splitting tensile strength of self-compacting recycled aggregate concrete", Mater., 15(12). 
  26. Degtyarev, V.V. and Tsavdaridis, K.D. (2022), "Buckling and ultimate load prediction models for perforated steel beams using machine learning algorithms", J. Build. Eng., 51, 104316. https://doi.org/https://doi.org/10.1016/j.jobe.2022.104316. 
  27. Dehestani, A., Kazemi, F., Abdi, R. and Nitka, M. (2022), "Prediction of fracture toughness in fibre-reinforced concrete, mortar, and rocks using various machine learning techniques", Eng. Frac. Mech., 276, 108914. https://doi.org/https://doi.org/10.1016/j.engfracmech.2022.108914 
  28. Eidgahee, D.R., Soleymani, A., Hasani, H., Kontoni, D.P.N. and Jahangir, H. (2023), "Flexural capacity estimation of FRP reinforced T-shaped concrete beams via soft computing techniques", Comput. Concrete, 32(1), 1. https://doi.org/10.12989/cac.2023.32.1.001. 
  29. Erdebilli, B. and Devrim-Ictenbas, B. (2022), "Ensemble voting regression based on machine learning for predicting medical waste: A case from Turkey", Math., 10(14). https://doi.org/10.3390/math10142466. 
  30. Fakhri, D., Khodayari, A., Mahmoodzadeh, A., Hosseini, M., Hashim Ibrahim, H. and Hussein Mohammed, A. (2022), "Prediction of Mixed-mode I and II effective fracture toughness of several types of concrete using the extreme gradient boosting method and metaheuristic optimization algorithms", Eng. Frac. Mech., 276, 108916. https://doi.org/https://doi.org/10.1016/j.engfracmech.2022.108916. 
  31. Fakhri, D., Mahmoodzadeh, A., Hussein Mohammed, A., Khodayari, A., Hashim Ibrahim, H., Rashidi, S. and Taher Karim, S.H. (2023), "Forecasting failure load of andstone under different freezing-thawing cycles using Gaussian process regression method and grey wolf optimization algorithm", Theo. Appl. Frac. Mech., 125, 103876. https://doi.org/https://doi.org/10.1016/j.tafmec.2023.103876. 
  32. Farina, I., Modano, M., Zuccaro, G., Goodall, R. and Colangelo, F. (2018), "Improving flexural strength and toughness of geopolymer mortars through additively manufactured metallic rebars", Compos. Part B: Eng., 145, 155-161. https://doi.org/https://doi.org/10.1016/j.compositesb.2018.03.017. 
  33. Gayathri, R., Rani, S.U., Cepova, L., Rajesh, M. and Kalita, K. (2022), "A comparative analysis of machine learning models in prediction of mortar compressive strength", Process., 10(7). https://doi.org/10.3390/pr10071387. 
  34. Geurts, P., Ernst, D. and Wehenkel, L. (2006), "Extremely randomized trees", Mach. Learn., 63(1), 3-42. https://doi.org/10.1007/s10994-006-6226-1. 
  35. Ghosh, A. and Ransinchung, G.D. (2022), "Application of machine learning algorithm to assess the efficacy of varying industrial wastes and curing methods on strength development of geopolymer concrete", Const. Build. Mater., 341, 127828. https://doi.org/10.1016/j.conbuildmat.2022.127828. 
  36. Kamran, M., Chaudhry, W., Taiwo, B.O., Hosseini, S. and Rehman, H. (2024), "Decision intelligence-based predictive modelling of hard rock pillar stability using K-nearest neighbor coupled with grey wolf optimization algorithm", Process., 12(4), 783. https://doi.org/10.3390/pr12040783. 
  37. Kamran, M., Ullah, B., Ahmad, M. and Sabri, M.M.S. (2022), "Application of KNN-based isometric mapping and fuzzy c-means algorithm to predict short-term rock burst risk in deep underground projects", Front. Public Health, 10, 1023890. https://doi.org/10.3389/fpubh.2022.1023890. 
  38. Kewalramani, M., Samadi, H., Mohammed, A., Mahmoodzadeh, A., Albaijan, I., Ibrahim, H. and Alsulamy, S. (2024), "Predicting the splitting tensile strength of manufactured-sand concrete containing stone nano-powder through advanced machine learning techniques", Adv. Nano Res., 16(4), 375-394. https://doi.org/10.12989/anr.2024.16.4.375. 
  39. Khatri, K.L. and Markad, K. (2023), "Stochastic fracture behavior analysis of infinite plates with a separate crack and a hole under tensile loading", Comput. Concrete., 32(1), 99-117. https://doi.org/10.12989/cac.2023.32.1.099. 
  40. Khodayari, A., Fakhri, D., Mohammed, A., Albaijan, I., Mahmoodzadeh, A., Ibrahim, H., Elhag, A.B. and Rashidi, S. (2023), "The Gene Expression Programming Method to Generate an Equation to Estimate Fracture Toughness of Reinforced Concrete", Steel Compos. Struct., 48(2), 163-177. https://doi.org/10.12989/scs.2023.48.2.163. 
  41. Kim, S.E., Vu, Q.V., Papazafeiropoulos, G., Kong, Z. and Truong, V.H. (2022). "Comparison of machine learning algorithms for regression and classification of ultimate load-carrying capacity of steel frames", Steel Compos. Struct., 37(2), 193-209. https://doi.org/10.12989/scs.2020.37.2.193. 
  42. Komnitsas, K.A. (2011), "Potential of geopolymer technology towards green buildings and sustainable cities", Procedia Eng., 21, 1023-1032. https://doi.org/10.1016/j.proeng.2011.11.2108. 
  43. Lao, J.C., Huang, B.T., Fang, Y., Xu, L.Y., Dai, J.G. and Shah, S.P. (2023), "Strain-hardening alkali-activated fly ash/slag composites with ultra-high compressive strength and ultra-high tensile ductility", Cement Concrete Res., 165, 107075. https://doi.org/10.1016/j.cemconres.2022.107075. 
  44. Lao, J.C., Huang, B.T., Xu, L.Y., Khan, M., Fang, Y. and Dai, J.G. (2023), "Seawater sea-sand Engineered Geopolymer Composites (EGC) with high strength and high ductility", Cement Concrete Res., 138, 104998. https://doi.org/10.1016/j.cemconcomp.2023.104998. 
  45. Lao, J.C., Ma, R.Y., Xu, L.Y., Li, Y., Shen, Y.N., Yao, J., Wang, Y.S., Xie, T.Y. and Huang, B.T. (2024), "Fly ash-dominated high-strength engineered/strain-hardening geopolymer composites (HS-EGC/SHGC): influence of alkalinity and environmental assessment", J. Clean. Prod., 447, 141182. https://doi.org/10.1016/j.jclepro.2024.141182. 
  46. LeCun, Y., Bengio, Y. and Hinton, G. (2015), "Deep learning", Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539. 
  47. Li, N., Asteris, P.G., Tran, T.T., Pradhan, B. and Nguyen, H. (2022), "Modelling the deflection of reinforced concrete beams using the improved artificial neural network by imperialist competitive optimization", Steel Compos. Struct., 42(6), 733-745. https://doi.org/10.12989/scs.2022.42.6.733. 
  48. Li, X.N., Zuo, X.B., Zou, Y.X. and Zhou, G.P. (2023), "Numerical simulation on integrated curing-leaching process of slag-blended cement pastes", Comput. Concrete, 32(1), 45. https://doi.org/10.12989/cac.2023.32.1.045. 
  49. Liu, X., Athanasiou, C.E., Padture, N.P., Sheldon, B.W. and Gao, H. (2020), "A machine learning approach to fracture mechanics problems", Acta. Mater., 190, 105-112. https://doi.org/https://doi.org/10.1016/j.actamat.2020.03.016. 
  50. Loh, W.Y. (2011), "Classification and regression trees", WIREs Data Min. Know. Dis., 1(1), 14-23. https://doi.org/https://doi.org/10.1002/widm.8. 
  51. Marani, A. and Nehdi, M.L. (2020), "Machine learning prediction of compressive strength for phase change materials integrated cementitious composites", Const. Build. Mater., 265, 120286. https://doi.org/https://doi.org/10.1016/j.conbuildmat.2020.120286. 
  52. Mazumder, E.A. and Prasad, Meesaraganda, L.V. (2023), "Probabilistic estimation for mechanical properties of self-compacting geopolymer concrete using machine learning technique", Arab. J. Sci. Eng., 48(10), 1-14. https://doi.org/10.1007/s13369-023-07866-x. 
  53. Mohammed, A., Rafiq, S., Sihag, P., Kurda, R., Mahmood, W., Ghafor, K. and Sarwar, W. (2020), "ANN, M5P-tree and nonlinear regression approaches with statistical evaluations to predict the compressive strength of cement-based mortar modified with fly ash", J. Mater. Res. Techno., 9(6), 12416-12427. https://doi.org/https://doi.org/10.1016/j.jmrt.2020.08.083. 
  54. Nguyen, H., Vu, T., Vo, T.P. and Thai, H.T. (2021), "Efficient machine learning models for prediction of concrete strengths", Construct. Build. Mater., 266, 120950. https://doi.org/https://doi.org/10.1016/j.conbuildmat.2020.120950. 
  55. Nguyen, H.D., Kim, J.H. and Shin, M. (2022). "Development of ensemble machine learning models for evaluating seismic demands of steel moment frames", Steel Compos. Struct., 44(1), 49-63. https://doi.org/10.12989/scs.2022.44.1.049. 
  56. Ozcan, G., Kocak, Y. and Gulbandilar, E. (2017), "Estimation of compressive strength of BFS and WTRP blended cement mortars with machine learning models", Comput. Concrete, 19(3), 275-282. https://doi.org/10.12989/CAC.2017.19.3.275. 
  57. Quinlan, J.R. (1986), "Induction of decision trees", Mach. Learn., 1(1), 81-106. https://doi.org/10.1007/BF00116251. 
  58. Rangan, B.V. (2008), "Design and manufacture of flyash-based geopolymer concrete", Concrete Aust., 34(2), 37-43. https://doi.org/http://hdl.handle.net/20.500.11937/17113. 
  59. Rangan, D.H.A.B.V. (2005), "Development and Properties of Low-calcium Fly Ash Based Geopolymer Concrete". 
  60. Rasmussen, C.E. (2004), Gaussian Processes in Machine Learning, Heidelberg: Springer Berlin Heidelberg, Berlin, Germany. 
  61. Roy, D.M., Jiang, W. and Silsbee, M.R. (2000), "Chloride diffusion in ordinary, blended, and alkali-activated cement pastes and its relation to other properties", Cement Concrete Res., 30(12), 1879-1884. https://doi.org/https://doi.org/10.1016/S00088846(00)00406-3. 
  62. Schmidhuber, J. (2015), "Deep learning in neural networks: An overview", Neural Netw., 61, 85-117. https://doi.org/https://doi.org/10.1016/j.neunet.2014.09.003. 
  63. Schneider, M., Romer, M., Tschudin, M. and Bolio, H. (2011), "Sustainable cement production-present and future", Cement Concrete Res., 41(7), 642-650. https://doi.org/https://doi.org/10.1016/j.cemconres.2011.03.019. 
  64. Seeger, M. (2004), "Gaussian processes for machine learning", Int. J. Neural Syst., 14(02), 69-106. https://doi.org/10.1142/S0129065704001899. 
  65. Sevakula, R.K., Au-Yeung, W.T.M., Singh, J.P., Heist, E.K., Isselbacher, E.M. and Armoundas, A.A. (2020), "State-of-the- art machine learning techniques aiming to improve patient outcomes pertaining to the cardiovascular system", J. Amer. Heart Asso., 9(4), e013924. https://doi.org/10.1161/JAHA.119.013924. 
  66. Sevim, U.K., Bilgic, H.H., Cansiz, O.F., Ozturk, M. and Atis, C.D. (2021), "Compressive strength prediction models for cementitious composites with fly ash using machine learning techniques", Const. Build. Mater., 271, 121584. https://doi.org/https://doi.org/10.1016/j.conbuildmat.2020.121584. 
  67. Silva, P.D., Sagoe-Crenstil, K. and Sirivivatnanon, V. (2007), "Kinetics of geopolymerization: Role of Al2O3 and SiO2", Cement Concrete Res., 37(4), 512-518. https://doi.org/https://doi.org/10.1016/j.cemconres.2007.01.003 
  68. Singh, N.B. (2018), "Fly ash-based geopolymer binder: A future construction material", Miner., 8(7), 299. https://doi.org/10.3390/min8070299. 
  69. Tin Kam, H. (1998), "The random subspace method for constructing decision forests", IEEE Trans. Pattern Anal. Mach. Intel., 20(8), 832-844. https://doi.org/10.1109/34.709601. 
  70. Wang, Y.T., Zhang, X. and Liu, X.-S. (2021), "Machine learning approaches to rock fracture mechanics problems: Mode-I fracture toughness determination", Eng. Frac. Mech., 253, 107890. https://doi.org/https://doi.org/10.1016/j.engfracmech.2021.107890. 
  71. Xu, H. and Van Deventer, J.S.J. (2000), "The geopolymerisation of alumino-silicate minerals", Inter. J. Miner. Process., 59(3), 247-266. https://doi.org/https://doi.org/10.1016/S0301-7516(99)00074-5. 
  72. Yip, C.K., Lukey, G.C. and van Deventer, J.S.J. (2005), "The coexistence of geopolymeric gel and calcium silicate hydrate at the early stage of alkaline activation", Cement Concrete Res., 35(9), 1688-1697.https://doi.org/https://doi.org/10.1016/j.cemconres.2004.10.042.