Advances in nano research

  • 제15권6호
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  • Pages.533-539
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  • 2023
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  • 2287-237X(pISSN)
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  • 2287-2388(eISSN)
테크노프레스 (Techno-Press)
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Machine learning-based techniques to facilitate the production of stone nano powder-reinforced manufactured-sand concrete

  • Zanyu Huang (College of Economics and Management Engineering, Beijing Institute of Civil Engineering and Architecture) ;
  • Qiuyue Han (College of Economics and Management Engineering, Beijing Institute of Civil Engineering and Architecture) ;
  • Adil Hussein Mohammed (Department of Communication and Computer Engineering, Faculty of Engineering, Cihan University-Erbil) ;
  • Arsalan Mahmoodzadeh (IRO, Civil Engineering Department, University of Halabja) ;
  • Nejib Ghazouani (Department of Civil Engineering, College of Engineering, Northern Border University) ;
  • Shtwai Alsubai (Department of Computer Science, College of Computer Engineering and Sciences in Al-Kharj, Prince Sattam bin Abdulaziz University) ;
  • Abed Alanazi (Department of Computer Science, College of Computer Engineering and Sciences in Al-Kharj, Prince Sattam bin Abdulaziz University) ;
  • Abdullah Alqahtani (Software Engineering Department, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University)
  • 투고 : 2023.05.18
  • 심사 : 2023.09.13
  • 발행 : 2023.12.25
⟨ 이전 논문 다음 논문 ⟩

초록

This study aims to examine four machine learning (ML)-based models for their potential to estimate the splitting tensile strength (STS) of manufactured sand concrete (MSC). The ML models were trained and tested based on 310 experimental data points. Stone nanopowder content (SNPC), curing age (CA), and water-to-cement (W/C) ratio were also studied for their impacts on the STS of MSC. According to the results, the support vector regression (SVR) model had the highest correlation with experimental data. Still, all of the optimized ML models showed promise in estimating the STS of MSC. Both ML and laboratory results showed that MSC with 10% SNPC improved the STS of MSC.

키워드

과제정보

A. 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-2023-0109". B. This study is supported via funding from Prince Satam bin Abdulaziz University project number (PSAU/2023/R/1444).

참고문헌

  1. Amlashi, A.T., Abdollahi, S.M., Goodarzi, S. and Ghanizadeh, A. R. (2019), "Soft computing based formulations for slump, compressive strength, and elastic modulus of bentonite plastic concrete", J. Clean. Prod., 230, 1197-1216. https://doi.org/10.1016/j.jclepro.2019.05.168
  2. Ashrafian, A., Taheri Amiri, M. J., Rezaie-Balf, M., Ozbakkaloglu, T. and Lotfi-Omran, O. (2018), "Prediction of compressive strength and ultrasonic pulse velocity of fiber reinforced concrete incorporating nano silica using heuristic regression methods", Constr. Build. Mater., 190, 479-494. https://doi.org/10.1016/j.conbuildmat.2018.09.047
  3. Cai, J., Pan, J., Li, G. and Elchalakani, M. (2023), "Behaviors of eccentrically loaded ECC-encased CFST columns after fire exposure", Eng. Struct., 289, 116258. https://doi.org/10.1016/j.engstruct.2023.116258
  4. Dao, D., Ly, H.B., Trinh, S., Le, T.T. and Pham, B. (2019), "Artificial intelligence approaches for prediction of compressive strength of geopolymer concrete", Materials, 12(6), 983. https://doi.org/10.3390/ma12060983
  5. Dutta, D., Barai, S.V. (2019), Prediction of Compressive Strength of Concrete: Machine Learning Approaches, Recent Advances in Structural Engineering, 1. Lecture Notes in Civil Engineering, 11, Springer, Singapore.
  6. Gandomi, A.H., Sajedi, S., Kiani, B. and Huang, Q. (2016), "Genetic programming for experimental big data mining: A case study on concrete creep formulation", Auto. Constr., 70, 89-97. https://doi.org/10.1016/j.autcon.2016.06.010
  7. He, H.E.S., Ai, L., Wang, X., Yao, J., He, C. and Cheng, B. (2023), "Exploiting machine learning for controlled synthesis of carbon dots-based corrosion inhibitors", J. Clean. Prod., 419, 138210. https://doi.org/10.1016/j.jclepro.2023.138210
  8. Huang, H., Guo, M., Zhang, W. and Huang, M. (2022), "Seismic behavior of strengthened rc columns under combined loadings", J. Bridge Eng., 27(6). https://doi.org/10.1061/(ASCE)BE.1943-5592.0001871
  9. Huang, H., Guo, M., Zhang, W., Zeng, J., Yang, K. and Bai, H. (2021), "Numerical investigation on the bearing capacity of RC columns strengthened by HPFL-BSP under combined loadings", J. Build. Eng., 39, 102266. https://doi.org/10.1016/j.jobe.2021.102266
  10. Huang, H., Huang, M., Zhang, W., Pospisil, S. and Wu, T. (2020), "Experimental investigation on rehabilitation of corroded RC columns with BSP and HPFL under combined loadings", J. Struct. Eng., 146(8). https://doi.org/10.1061/(ASCE)ST.1943-541X.0002725
  11. Jin, M., Ma, Y., Li, W., Huang, J., Yan, Y., Zeng, H., Lu, C. and Liu, J. (2023), "Multi-scale investigation on composition-structure of C-(A)-S-H with different Al/Si ratios under attack of decalcification action", Cement Concr. Res., 172, 107251. https://doi.org/10.1016/j.cemconres.2023.107251
  12. Liu, C., Peng, Z., Cui, J., Huang, X., Li, Y. and Chen, W. (2023), "Development of crack and damage in shield tunnel lining under seismic loading: Refined 3D finite element modeling and analyses", Thin Wall. Struct., 185, 110647. https://doi.org/10.1016/j.tws.2023.110647
  13. Li, B., Ke, G. and Zhou, M. (2011), "Influence of manufactured sand characteristics on strength and abrasion resistance of pavement cement concrete", Constr. Build. Mater., 25(10), 3849-3853. https://doi.org/10.1016/j.conbuildmat.2011.04.004
  14. Ling, H., Qian, C., Kang, W., Liang, C. and Chen, H. (2019), "Combination of Support Vector Machine and K-Fold cross validation to predict compressive strength of concrete in marine environment", Constr. Build. Mater., 206, 355-363. https://doi.org/10.1016/j.conbuildmat.2019.02.071
  15. Lokuge, W., Wilson, A., Gunasekara, C., Law, D.W. and Setunge, S. (2018), "Design of fly ash geopolymer concrete mix proportions using multivariate adaptive regression spline model", Constr. Build. Mater., 166, 472-481. https://doi.org/10.1016/j.conbuildmat.2018.01.175
  16. Moradi, M.J., Khaleghi, M., Salimi, J., Farhangi, V. and Ramezanianpour, A.M. (2021), "Predicting the compressive strength of concrete containing metakaolin with different properties using ANN", Measurement, 183, 109790. https://doi.org/10.1016/j.measurement.2021.109790
  17. Peng, S., Chen, R., Yu, B., Xiang, M., Lin, X. and Liu, E. (2021), "Daily natural gas load forecasting based on the combination of long short term memory, local mean decomposition, and wavelet threshold denoising algorithm", J. Natur. Gas Sci. Eng., 95, 104175. https://doi.org/10.1016/j.jngse.2021.104175
  18. Rathakrishnan, V., Bt. Beddu, S. and Ahmed, A. N. (2022), "Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning algorithms", Sci. Rep., 12(1), 9539. https://doi.org/10.1038/s41598-022-12890-2
  19. Wang, W., Li, D.Q., Tang, X.S. and Du, W. (2023), "Seismic fragility and demand hazard analyses for earth slopes incorporating soil property variability", Soil Dyn. Earthq. Eng., 173, 108088. https://doi.org/10.1016/j.soildyn.2023.108088
  20. Yaseen, Z. M., Deo, R. C., Hilal, A., Abd, A. M., Bueno, L. C., Salcedo-Sanz, S. and Nehdi, M. L. (2018), "Predicting compressive strength of lightweight foamed concrete using extreme learning machine model", Adv. Eng. Softw., 115, 112-125. https://doi.org/10.1016/j.advengsoft.2017.09.004
  21. Yuan, X., Chen, G., Jiao, P., Li, L., Han, J. and Zhang, H. (2022), "A neural network-based multivariate seismic classifier for simultaneous post-earthquake fragility estimation and damage classification", Eng. Struct., 255, 113918. https://doi.org/10.1016/j.engstruct.2022.113918
  22. Zhou, F., Jiang, H., Huang, L., Hu, Y., Xie, Z., Zeng, Z., Liu, M., Wang, B. and Zhou, X. (2023), "Early shrinkage modeling of complex internally confined concrete based on capillary tension theory", Buildings, 13(9), 2201. https://doi.org/10.3390/buildings13092201
  23. Zhou, F., Li, W., Hu, Y., Huang, L., Xie, Z., Yang, J., Wu, D. and Chen, Z. (2023a), "Moisture diffusion coefficient of concrete under different conditions", Buildings, 13(10), 2421. https://doi.org/10.3390/buildings13102421
  24. Zhou, S., Lu, C., Zhu, X. and Li, F. (2021b), "Preparation and characterization of high-strength geopolymer based on BH-1 lunar soil simulant with low alkali content", Engineering, 7(11), 1631-1645. https://doi.org/10.1016/j.eng.2020.10.016
  25. Zhang, J., Li, D. and Wang, Y. (2020), "Predicting mechanical properties of manufactured-sand concrete using tree-based machine learning models", J. Clean. Prod., 258, 120665. https://doi.org/10.1016/j.jclepro.2020.120665
  26. Zhao, S., Ding, X., Zhao, M., Li, C. and Pei, S. (2017), "Experimental study on tensile strength development of concrete with manufactured sand", Constr. Build. Mater., 138, 247-253. https://doi.org/10.1016/j.conbuildmat.2017.01.093
  27. Ziyad Sami, B.H., Ziyad Sami, B.F., Kumar, P., Ahmed, A.N., Amieghemen, G.E., Sherif, M. M. and El-Shafie, A. (2023), "Feasibility analysis for predicting the compressive and tensile strength of concrete using machine learning algorithms", Case Stud. Constr. Mater., 18, e01893. https://doi.org/10.1016/j.cscm.2023.e01893