Water Resources Engineering

Xiao Wu1, Daoyong Zhu1,2This email address is being protected from spambots. You need JavaScript enabled to view it., and Qin Yan1

1College of Water & Architectural Engineering, Shihezi University, Shihezi, Xinjiang , 832000, China

2State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining & Technology-Beijing, Beijing, 100083, China

 

Received: December 16, 2024
Accepted: March 10, 2025
Publication Date: May 10, 2025

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202601_29(1).0018  


To address the limited availability of literature on the splitting tensile strength (STS) of basalt fiber reinforced concrete (BFRC), it is necessary to create and evaluate approaches for predicting STS. The current research examined two methods, namely least square support vector regression (LSSVR) (RFOLS) and random forest (RF) (RFORF), for assessing goals. This simulation is very dependent on its hyperparameters; to find the optimal set of hyperparameters, the red fox optimization (RFO) is linked to both LSSVR and RF. The data collection includes 267 individuals who were compiled from the available literature and divided into training, validation, and testing phases in suitable percentages. This study aimed to evaluate the predictive ability of multiple methods based on machine learning (ML) for the STS of BFRC and to clarify their fundamental concepts. The results depict that RFORF outperformed RFOLS in terms of R2 values, with 0.9806, 0.9828, and 0.9676, compared to 0.9676, 0.9686, and 0.9476 for train, validation, and test stages, respectively. Also, the models’ results indicate that RFORF outperforms another model, with −34% lower values in symmetric mean absolute percentage error (SMAPE) and a significant −80% reduction in mean squared logarithmic error (MSLE). Although RFOLS exhibits notable accuracy, RFORF surpasses it in terms of reliability and effectiveness.


Keywords: Eco-friendly Concrete; Fiber; Basalt; Splitting Tensile Strength; Red Fox optimization


  1. [1] B. Da, Y. Chen, H. Yu, H. Ma, D. Chen, Z. Wu, J. Liu, and Y. Li, (2022) “Preparation technology, mechanical properties and durability of coral aggregate seawater concrete in the island-reef environment" Journal of Cleaner Production 339: 130572. DOI: https: //doi.org/10.1016/j.jclepro.2022.130572.
  2. [2] Q. Fu, Z. Zhang, and D. Niu, (2023) “Understanding the acceleration impact of load and flowing water on the chloride ion transport properties of fly ash-based geopolymer concrete" Cement and Concrete Composites 141: 105146. DOI: https://doi.org/10.1016/j.cemconcomp. 2023.105146.
  3. [3] B.Zhang, F. Xu, H. Zhu, Z. Yang, and H. Peng, (2024) “Deterioration of bond performance between BFRP bars and coral aggregate concrete incorporating slag-based geopolymers under seawater corrosion environments" Construction and Building Materials 411: 134518. DOI: https: //doi.org/10.1016/j.conbuildmat.2023.134518. 
  4. [4] E. C. Peters, (2015) “Diseases of coral reef organisms" Coral reefs intheAnthropocene:147–178. DOI: https: //doi.org/10.1007/978-94-017-7249-5_8. 
  5. [5] S. A. Hoor and M. Esmaeili-Falak, (2024) “Innovative Approaches for Mitigating Soil Liquefaction: A State-of the-Art Review of Techniques and Bibliometric Analysis" Indian Geotechnical Journal: 1–28. DOI: https: //doi.org/10.1007/s40098-024-01120-3.
  6. [6] Z. Sun, Y. Li, L. Su, D. Niu, D. Luo, W. He, and S. Xie, (2024) “Investigation of electrical resistivity for fiber reinforced coral aggregate concrete" Construction and Building Materials 414: 135011. DOI: https: //doi.org/10.1016/j.conbuildmat.2024.135011. 
  7. [7] R. S. Benemaran, M. Esmaeili-Falak, and M. S. Kord lar, (2024) “Improvement of recycled aggregate concrete using glass fiber and silica fume" Multiscale and Multidisciplinary Modeling, Experiments and Design 7: 1895–1914. DOI: https: //doi.org/10.1007/s41939-023-00313-2. 
  8. [8] Y. Zhu, L. Huang, Z. Zhang, and B. Bayrami, (2022) “Estimation of splitting tensile strength of modified recycled aggregate concrete using hybrid algorithms" Steel and Composite Structures, An International Journal 44: 389–406.
  9. [9] L. Zhou, S. Guo, Z. Zhang, C. Shi, Z. Jin, and D. Zhu, (2021) “Mechanical behavior and durability of coral aggregate concrete and bonding performance with fiber reinforced polymer (FRP) bars: A critical review" Journal of Cleaner Production 289: 125652. DOI: https: //doi.org/10.1016/j.jclepro.2020.125652. 
  10. [10] H.Yu, B. Da, H. Ma, X. Dou, and Z. Wu, (2020) “Ser vice life prediction of coral aggregate concrete structure under island reef environment" Construction and Build ing Materials 246: 118390. DOI: https://doi.org/10.1016/j.conbuildmat.2020.118390.
  11. [11] M.Esmaeili-Falak and R. S. Benemaran, (2024) “Ap plication of optimization-based regression analysis for evaluation of frost durability of recycled aggregate concrete" Structural Concrete 25: 716–737. DOI: https: //doi.org/10.1002/suco.202300566. 
  12. [12] Z.Sun, D.Niu, X.Wang, L.Zhang, and D.Luo,(2022) “Bond behavior of coral aggregate concrete and corroded Cr alloy steel bar" Journal of Building Engineering 61: 105294. DOI: https: //doi.org/10.1016/j.jobe.2022.105294. 
  13. [13] Z. Sun, L. Zhang, D. Niu, B. Wen, and D. Luo, (2020) “Time-varying model for predicting the resistivity of coral aggregate concrete" Construction and Building Materials 265: 120588. DOI: https://doi.org/10.1016/j.conbuildmat.2020.120588.
  14. [14] E. Hassankhani and M. Esmaeili-Falak, (2024) “Soil–structure interaction for buried conduits influenced by the coupled effect of the protective layer and trench in stallation" Journal of Pipeline Systems Engineering and Practice 15: 04024012. DOI: https://doi.org/10.1061/JPSEA2.PSENG-1547.
  15. [15] Z. Wenwu, Z. Shaowu, G. Guilin, L. Qiangqiang, and L. Kexing, (2024) “Estimating the Torsional Capacity of Reinforced Concrete Beams Using ANFIS Models" Advances in Engineering and Intelligence Systems 3: 136–150. DOI: 10.22034/aeis.2024.475280.1222.
  16. [16] L. Chen and W. Jiang, (2023) “Estimation of the Com pressive Strength of Self-Compacting Concrete (SCC) by a Machine Learning Technique Coupling with Novel Optimization Algorithms" Advances in Engineering and Intelligence Systems 2: 38–49. DOI: 10.22034/aeis.2023.383263.1069.
  17. [17] T. Zhou and D. Mozyrska, (2023) “Novel Optimization Algorithms Usage to Model the Compressive Strength of Ultra-High-Performance Concrete in Machine Learning Technique: Support Vector Regression" Advances in Engineering and Intelligence Systems 2: 51–63. DOI: 10.22034/aeis.2023.386402.1082.
  18. [18] Q. Fu, W. Xu, J. He, L.Su, H. Song, and D. Niu,(2021) “Dynamic strength criteria for basalt fibre-reinforced coral aggregate concrete" Composites Communications 28: 100983. DOI: https://doi.org/10.1016/j.coco.2021.100983.
  19. [19] J. Shi, X. Wang, Z. Wu, X. Wei, and X. Ma, (2022) “Long-term mechanical behaviors of uncracked concrete beams prestressed with external basalt fiber-reinforced polymer tendons" Engineering Structures 262: 114309. DOI: https: //doi.org/10.1016/j.engstruct.2022.114309.
  20. [20] D. Huang, D. Niu, L. Su, Y. Liu, B. Guo, Q. Xia, and G. Peng, (2022) “Diffusion behavior of chloride in coral aggregate concrete in marine salt-spray environment" Construction and Building Materials 316: 125878. DOI: https: //doi.org/10.1016/j.conbuildmat.2021.125878.
  21. [21] M. F. M. Zain, H. B. Mahmud, A. Ilham, and M. Faizal, (2002) “Prediction of splitting tensile strength of high-performance concrete" Cement and Concrete Research 32: 1251–1258. DOI: https: //doi.org/10.1016/S0008-8846(02)00768-8. 
  22. [22] Y. Zhang, S. Zhang, T. Li, and M. Deng, (2023) “Cyclic response and shear mechanisms of RC short walls strengthened with engineered cementitious composites thin layers" Archives of Civil and Mechanical Engineering 23: 148. DOI: https: //doi.org/10.1007/s43452-023-00683-x.  
  23. [23] Y. Zhang, S. Zhang, and M. Deng, (2022) “Four-point bending tests of ECC: Mechanical response and toughness evaluation" Case Studies in Construction Materials 17: e01573. DOI: https: //doi.org/10.1016/j.cscm.2022.e01573. 
  24. [24] Y. Wang, S. Zhang, D. Niu, L. Su, and D. Luo, (2020) “Strength and chloride ion distribution brought by aggregate of basalt fiber reinforced coral aggregate concrete" Construction and Building Materials 234: 117390. DOI: https://doi.org/10.1016/j.conbuildmat.2019.117390. 
  25. [25] Z. Deng, Y. Zhou, J. Jiang, X. Huang, and B. Liu, (2023) “Mechanical properties and uniaxial constitutive model of fiber-reinforced coral aggregate concrete" Structural Concrete 24: 4259–4275. DOI: https: //doi.org/10.1002/suco.202200271.
  26. [26] B. Liu, X. Zhang, J. Ye, X. Liu, and Z. Deng, (2022) “Mechanical properties of hybrid fiber reinforced coral concrete" Case Studies in Construction Materials 16: e00865. DOI: https: //doi.org/10.1016/j.cscm.2021.e00865. 
  27. [27] D. Niu, L. Su, Y. Luo, D. Huang, and D. Luo, (2020) “Experimental study on mechanical properties and durability of basalt fiber reinforced coral aggregate concrete" Construction and Building Materials 237: 117628. DOI: https: //doi.org/10.1016/j.conbuildmat.2019.117628.
  28. [28] D. Li, X. Zhang, Q. Kang, and E. Tavakkol, (2023) “Estimation of unconfined compressive strength of marine clay modified with recycled tiles using hybridized extreme gradient boosting method" Construction and Building Materials 393: 131992. DOI: https://doi.org/10.1016/j.conbuildmat.2023.131992.
  29. [29] R. Liang and B. Bayrami, (2023) “Estimation of frost durability of recycled aggregate concrete by hybridized Random Forests algorithms" Steel and Composite Structures 49: 91–107. DOI: https: //doi.org/10.12989/scs.2023.49.1.091.
  30. [30] Y. Dawei, Z. Bing, G. Bingbing, G. Xibo, and B. Razza ghzadeh, (2023) “Predicting the CPT-based pile set-up parameters using HHO-RF and PSO-RF hybrid models" Structural Engineering and Mechanics, An Int’l Journal 86: 673–686. DOI: 10.12989/sem.2023.86.5.673.
  31. [31] M. Esmaeili-Falak, H. Katebi, M. Vadiati, and J. Adamowski, (2019) “Predicting triaxial compressive strength and Young’s modulus of frozen sand using artificial intelligence methods" Journal of Cold Regions Engineering 33: 04019007. DOI: https: //doi.org/10.1061/(ASCE)CR.1943-5495.0000188. 
  32. [32] M.Esmaeili-Falak and R. S. Benemaran, (2024) “Ensemble extreme gradient boosting based models to predict the bearing capacity of micropile group" Applied Ocean Research 151: 104149. DOI: https: //doi.org/10.1016/j.apor.2024.104149. 
  33. [33] R. S. Benemaran, (2023) “Application of extreme gradient boosting method for evaluating the properties of episodic failure of borehole breakout" Geoenergy Science and Engineering 226: 211837. DOI: https://doi.org/10.1016/j.geoen.2023.211837. 
  34. [34] K. Zhang, Y. Zhang, and B. Razzaghzadeh, (2024) “Application of the optimal fuzzy-based system on bearing capacity of concrete pile" Steel and Composite Structures 51: 25. DOI: https: //doi.org/10.12989/scs.2024.51.1.025. 
  35. [35] B. M. Yaychi and M. Esmaeili-Falak, (2024) “Estimat ing axial bearing capacity of driven piles using tuned random forest frameworks" Geotechnical and Geological Engineering 42: 7813–7834. DOI: https: //doi.org/10.1007/s10706-024-02952-9. 
  36. [36] M. Wang, (2022) “Mechanical Properties Dataset of BFRC for strength prediction with machine learning" Mendeley Data 1: DOI: 10.17632/b5s8ywwgwr.1.
  37. [37] G. J. McRae, J. W. Tilden, and J. H. Seinfeld, (1982) “Global sensitivity analysis—a computational implementation of the Fourier amplitude sensitivity test (FAST)" Computers & Chemical Engineering 6: 15–25. DOI: https: //doi.org/10.1016/0098-1354(82)80003-3.
  38. [38] N.-D. Hoang, (2023) “Compressive strength estimation of rice husk ash-blended concrete using deep neural network regression with an asymmetric loss function" Iranian Journal of Science and Technology, Transactions of Civil Engineering 47: 1547–1565. DOI: https: //doi.org/10.1007/s40996-022-01015-4. 
  39. [39] J. Herman and W. Usher, (2017) “SALib: An open source Python library for sensitivity analysis" Journal of OpenSource Software 2: 97. DOI: 10.21105/joss. 00097.
  40. [40] S. Xu, X. An, X. Qiao, L. Zhu, and L. Li, (2013) “Multi output least-squares support vector regression machines" Pattern recognition letters 34: 1078–1084. DOI: https: //doi.org/10.1016/j.patrec.2013.01.015.
  41. [41] J. C. Y. Ngu and C. Yeo, (2022) “A comparative study of different kernel functions applied to LW-KPLS model for nonlinear processes" Biointerface Research in Ap plied Chemistry 13: DOI: 10.33263/BRIAC132.184.
  42. [42] L. Breiman, (2001) “Random forests" Machine learning 45: 5–32. DOI: https: //doi.org/10.1023/A:1010933404324. 
  43. [43] R. Díaz-Uriarte and S. A. de Andrés, (2006) “Gene selection and classification of microarray data using random forest" BMC bioinformatics 7: 1–13. DOI: https: //doi.org/10.1186/1471-2105-7-3. 
  44. [44] D. R. Cutler, T. C. E. Jr, K. H. Beard, A. Cutler, K. T. Hess, J. Gibson, and J. J. Lawler, (2007) “Random forests for classification in ecology" Ecology 88: 2783 2792. DOI: https: //doi.org/10.1890/07-0539.1.
  45. [45] V. F. Rodriguez-Galiano, B. Ghimire, J. Rogan, M. Chica-Olmo, and J. P. Rigol-Sanchez, (2012) “An assessment of the effectiveness of a random forest classifier for land-cover classification" ISPRS journal of photogrammetry and remote sensing 67: 93–104. DOI: https: //doi.org/10.1016/j.isprsjprs.2011.11.002.
  46. [46] H. Sun, D. Gui, B. Yan, Y. Liu, W. Liao, Y. Zhu, C. Lu, and N. Zhao, (2016) “Assessing the potential of random forest method for estimating solar radiation using air pollution index" Energy Conversion and Management 119: 121–129. DOI: https: //doi.org/10.1016/j.enconman.2016.04.051.
  47. [47] Z. Wang, Y. Wang, R. Zeng, R. S. Srinivasan, and S. Ahrentzen, (2018) “Random Forest based hourly building energy prediction" Energy and Buildings 171: 11 25. DOI: https: //doi.org/10.1016/j.enbuild.2018.04.008. 
  48. [48] D. Połap and M. Wo´ zniak,(2021)“Redfoxoptimization algorithm" Expert Systems with Applications 166: 114107. DOI: https: //doi.org/10.1016/j.eswa.2020.114107. 
  49. [49] W. Kulasooriya, R. S. S. Ranasinghe, U. S. Perera, P. Thisovithan, I. U. Ekanayake, and D. P. P. Meddage, (2023) “Modeling strength characteristics of basalt fiber reinforced concrete using multiple explainable machine learning with a graphical user interface" Scientific Reports 13: 13138. DOI: https: //doi.org/10.1038/s41598-023-40513-x. 

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