Geomechanics and Engineering

  • 제28권6호
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  • Pages.599-611
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  • 2022
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  • 2005-307X(pISSN)
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  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Optimised neural network prediction of interface bond strength for GFRP tendon reinforced cemented soil

  • Zhang, Genbao (College of Civil Engineering, Hunan City University) ;
  • Chen, Changfu (Key Laboratory of Building Safety and Energy Efficiency of the Ministry of Education, Hunan University) ;
  • Zhang, Yuhao (School of Civil and Environmental Engineering, University of New South Wales) ;
  • Zhao, Hongchao (School of Geology and Mining Engineering, Xinjiang University) ;
  • Wang, Yufei (Institute for Smart City of Chongqing University in Liyang, Chongqing University) ;
  • Wang, Xiangyu (School of Design and Built Environment, Curtin University)
  • 투고 : 2021.05.30
  • 심사 : 2022.02.04
  • 발행 : 2022.03.25
⟨ 이전 논문 다음 논문 ⟩

초록

Tendon reinforced cemented soil is applied extensively in foundation stabilisation and improvement, especially in areas with soft clay. To solve the deterioration problem led by steel corrosion, the glass fiber-reinforced polymer (GFRP) tendon is introduced to substitute the traditional steel tendon. The interface bond strength between the cemented soil matrix and GFRP tendon demonstrates the outstanding mechanical property of this composite. However, the lack of research between the influence factors and bond strength hinders the application. To evaluate these factors, back propagation neural network (BPNN) is applied to predict the relationship between them and bond strength. Since adjusting BPNN parameters is time-consuming and laborious, the particle swarm optimisation (PSO) algorithm is proposed. This study evaluated the influence of water content, cement content, curing time, and slip distance on the bond performance of GFRP tendon-reinforced cemented soils (GTRCS). The results showed that the ultimate and residual bond strengths were both in positive proportion to cement content and negative to water content. The sample cured for 28 days with 30% water content and 50% cement content had the largest ultimate strength (3879.40 kPa). The PSO-BPNN model was tuned with 3 neurons in the input layer, 10 in the hidden layer, and 1 in the output layer. It showed outstanding performance on a large database comprising 405 testing results. Its higher correlation coefficient (0.908) and lower root-mean-square error (239.11 kPa) were obtained compared to multiple linear regression (MLR) and logistic regression (LR). In addition, a sensitivity analysis was applied to acquire the ranking of the input variables. The results illustrated that the cement content performed the strongest influence on bond strength, followed by the water content and slip displacement.

키워드

과제정보

The research described in this paper was financially supported by the National Natural Science Foundation of China (grant numbers 51908201 and 51978254), Natural Science Foundation of Hunan Province (grant number 2020JJ5024), and the Key R&D Project of Hunan Province Intelligent Disaster Prevention and Mitigation and Ecological Restoration in Civil Engineering (grant number 2020SK2109). Meanwhile, this work was supported by Hunan Key Laboratory of Intelligent Disaster Prevention and Mitigation and Ecological Restoration in Civil Engineering, Hunan Provincial Engineering Research Center, Catastrophe and Reinforcement of Dangerous Engineering Structures. This research was also supported by Academic Research Council of Australia Linkage Projects for Asset Intelligence: Maximising Operational Effectiveness for Digital Era (Grant No. LP180100222).

참고문헌

  1. Abedini, M. and C. Zhang (2021), "Dynamic performance of concrete columns retrofitted with FRP using segment pressure technique", Compos. Struct., 260, 113473. https://doi.org/10.1016/j.compstruct.2020.113473.
  2. Abedini, M. and Zhang, C. (2021), "Dynamic vulnerability assessment and damage prediction of RC columns subjected to severe impulsive loading", Struct. Eng. Mech., 77(4), 441-461. https://doi.org/10.12989/sem.2021.77.4.441.
  3. Abedini, M., Zhang, C., Mehrmashhadi, J. and Akhlaghi, E (2020), "Comparison of ALE, LBE and pressure time history methods to evaluate extreme loading effects in RC column", Structures, 28, 456-466. https://doi.org/10.1016/j.istruc.2020.08.084.
  4. Abido, M.A. (2002), "Optimal power flow using particle swarm optimizatio", Int. J. Elec. Power & Energy Syst., 24(7), 563-571. https://doi.org/10.1016/S0142-0615(01)00067-9.
  5. A hnberg, H. (2006), Strength of Stabilised Soil-A Laboratory Study on Clays and Organic Soils Stabilised with different Types of Binder. Avd. for Byggnadsmekanik / Department of Construction Sciences, LTH, Lund, Sweden.
  6. Alam, Z., Sun, L., Zhang, C., Su, Z. and Samali, B. (2020), "Experimental and numerical investigation on the complex behaviour of the localised seismic response in a multi-storey plan-asymmetric structure", Struct. Infrastruct. Eng., 17(1), 86-102. https://doi.org/10.1080/15732479.2020.1730914.
  7. Alam, Z., Zhang, C. and Samali, B. (2020), "Influence of seismic incident angle on response uncertainty and structural performance of tall asymmetric structure", Struct. Des. Tall Spec. Build., 29(12), e1750. https://doi.org/10.1002/tal.1750
  8. Alam, Z., Zhang, C. and Samali, B. (2020), "The role of viscoelastic damping on retrofitting seismic performance of asymmetric reinforced concrete structures", Earthq. Eng. Eng. Vib., 19(1), 223-237. https://doi.org/10.1007/s11803-020-0558-x.
  9. Babasaki, R. (1996), "Japanese Geotechnical Society technical committee report-Factors influencing the strength of improved soil", Proceedings of the 2nd Int. Conf. Ground Improvement Geosystems-Grouting and Deep Mixing-, Tokyo, Japan, May.
  10. Cai, K., Chen, H., Ai, W., Miao, X., Lin, Q. and Feng, Q. (2021), "Feedback convolutional network for intelligent data fusion based on near-infrared collaborative IoT technology", IEEE T. Ind. Inform., 17, 1123-1135. https://doi.org/10.1109/TII.2021.3076513.
  11. Cawley, G.C. and Talbot, N. L. (2010), "On over-fitting in model selection and subsequent selection bias in performance evaluation", J. Machine Learning Research, 11(1), 2079-2107. https://dl.acm.org/doi/10.5555/1756006.1859921.
  12. CECS147, C. (2016), Technical specification for soil mass with reinforced cement soil pile and anchors, China Planning Press, Beijing, China.
  13. Chandwani, V., Agrawal, V., Nagar, R. and Signh, S. (2015), "Modeling slump of ready mix concrete using genetic algorithms assisted training of Artificial Neural Networks", Int. J. Technol., 42(2), 885-893. https://doi.org/10.1016/j.eswa.2014.08.048.
  14. Chen, C., Zhang, G., Zornberg, J. G., Morsy, A. M., & Huang, J. (2020), "Interface bond behavior of tensioned glass fiberreinforced polymer (GFRP) tendons embedded in cemented soils", Construction and Building Materials, 263, 120-132. https://doi.org/10.1016/j.conbuildmat.2020.120132
  15. Chen, C., Zhang, G., Zornberg, J.G., Morsy, A.M., Zhu, S. and Zhao, H. (2018), "Interface behavior of tensioned bars embedded in cement-soil mixtures", Constr. Build. Mater., 186, 840-853. https://doi.org/10.1016/j.conbuildmat.2018.07.211.
  16. Chew, S.H., Kamruzzaman, A.H.M. and Lee, F.H. (2004), "Physicochemical and engineering behavior of cement treated clays", J. Geotech. Geoenviron. Eng., 130(7), 696-706. https://ascelibrary.org/doi/abs/10.1061/(ASCE)10900241(2004)130:7(696).
  17. Fan, P., Deng, R., Qiu, J., Zhao, Z.. and Wu, S. (2021), "Well logging curve reconstruction based on kernel ridge regression", Arabian J. Geosci., 14(16), 1-10. https://doi.org/10.1007/s12517-021-07792-y.
  18. Feng, W., Wang, Y., Sun, J., Tang, Y., Wu, D., Jiang, Z., Wang, J. and Wang, X. (2021), "Prediction of thermo-mechanical properties of rubber-modified recycled aggregate concrete", ., 318, 125970. https://doi.org/10.1016/j.conbuildmat.2021.125970.
  19. Fernandez-Martinez, J. and Fernandez-Muniz, Z. (2020), "The curse of dimensionality in inverse problems", J. Comput. Appl. Math., 369, 112571. https://doi.org/10.1016/j.cam.2019.112571.
  20. Gholipour, G., Zhang, C. and Mousavi, A. A. (2020), "Nonlinear numerical analysis and progressive damage assessment of a cable-stayed bridge pier subjected to ship collision", Mar. Struct., 69, 102662. https://doi.org/10.1016/j.marstruc.2019.102662.
  21. Han, J. (2014), Principles and Practice of Ground Improvement, John Wiley & Sons, Inc., Hoboken, New Jersey, United States.
  22. Horpibulsuk, S., Miura, N. and Nagaraj, T.S. (2003), "Assessment of strength development in cement-admixed high water content clays with Abrams' law as a basis", Geotechnique, 53(4), 439-444. https://doi.org/10.1680/geot.2003.53.4.439.
  23. Hosmer Jr, D.W., Lemeshow, S. and Sturdivant, R.X. (2013), Applied logistic regression, John Wiley & Sons, Inc., Hoboken, New Jersey, United States.
  24. Hou, L., Wu, S., Zhang, G., Tan, Y. and Wang, X. (2021), "Literature review of digital twins applications in construction workforce safety", Appl. Sci., 11(1), 339. https://doi.org/10.3390/app11010339.
  25. Hsu, C.W., Chang, C.C. and Lin, C.J. (2003), "A practical guide to support vector classification", Technical report, Department of Computer Science, National Taiwan University.
  26. Ju, Y., Shen, T. and Wang, D. (2020), "Bonding behavior between reactive powder concrete and normal strength concrete", Constr. Build. Mater., 242, 118024 https://doi.org/118010.111016/j.conbuildmat.112020.118024.
  27. Kitazume, M. and Terashi, M. (2013), The Deep Mixing Method, CRC Press/Balkeman, Leiden, Netherlands.
  28. Li, J., Qin, Q., Sun, J., Ma, Y. and Li, Q. (2020), "Mechanical and conductive performance of electrically conductive cementitious composite using graphite, steel slag, and GGBS", Struct. Concrete, early view. https://doi.org/10.1002/suco.202000617
  29. Liu, J., Wu, C., Wu, G. and Wang, X. (2015), "A novel differential search algorithm and applications for structure design", Appl. Math. Comput., 268, 246-269. https://doi.org/10.1016/j.amc.2015.06.036.
  30. Luo, J., Li, M., Liu, X., Tian, W., Zhong, S. and Shi, K. (2020), "Stabilization analysis for fuzzy systems with a switched sampled-data control", J. Franklin Inst., 357(1), 39-58. https://doi.org/10.1016/j.jfranklin.2019.09.029.
  31. Medvedeva, M.A., Simos, T.E. and Tsitouras, C. (2020), "Variable step-size implementation of sixth-order Numerov-type methods", Math. Method. Appl. Sci., 43(3), 1204-1215. https://doi.org/10.1002/mma.5929.
  32. Mou, B. and Bai, Y. (2002), "Experimental investigation on shear behavior of steel beam-to-CFST column connections with irregular panel zone", Eng. Struct., 168, 487-504. https://doi.org/10.1016/j.engstruct.2018.04.029.
  33. Nathans, L.L., Oswald, F.L. and Nimon, K. (2012), "Interpreting multiple linear regression: A guidebook of variable importance", Pract. Assessment, Res. Eval., 17(9), 1-19. https://doi.org/10.7275/5fex-b874.
  34. Qin, C., Tao, J., Shi, H., Xiao, D., Li, B. and Liu, C. (2020), "A novel Chebyshev-wavelet-based approach for accurate and fast prediction of milling stability", Precision Eng., 62, 244-255. https://doi.org/10.1016/j.precisioneng.2019.11.016.
  35. Shen, S.L., Wang, Z.F., Yang, J. and Ho, C.E. (2013), "Generalized approach for prediction of jet grout column diameter", J. Geotech. Geoenviron. Eng., 139(12), 2060-2069. https://ascelibrary.org/doi/full/10.1061/%28ASCE%29GT.1943-5606.0000932.
  36. Shi, J., Lu, Y. and Zhang, J. (2020), "Approximation Attacks on Strong PUFs", IEEE T. Comput.-Aided Des. Integrated Circuit. Syst., 39(10), 2138-2151. https://doi.org/10.1109/TCAD.2019.2962115.
  37. Singh, V., Gu, N. and Wang, X. (2011), "A theoretical framework of a BIM-based multi-disciplinary collaboration platform", Autom. Constr., 20(2), 134-144. https://doi.org/10.1016/j.autcon.2010.09.011.
  38. Sun, J., Ma, Y., Li, J., Zhang, J., Ren, Z. and Wang, X. (2021), "Machine learning-aided design and prediction of cementitious composites containing graphite and slag powder", J. Build. Eng., 43, 102544. https://doi.org/10.1016/j.jobe.2021.102544.
  39. Sun, J., Wang, X., Zhang, J., Xiao, F., Sun, Y., Ren, Z., Zhang, G., Liu, S. and Wang, Y. (2021), "Multi-objective optimisation of a graphite-slag conductive composite applying a BAS-SVR based model", J. Build. Eng., 44, 103223. https://doi.org/10.1016/j.jobe.2021.103223.
  40. Sun, J., Wang, Y., Yao, X., Ren, Z., Zhang, G., Zhang, C., Chen, X., Ma, W. and Wang, X. (2021), "Machine-learning-aided prediction of flexural strength and ASR expansion for waste glass cementitious composite", Appl. Sci., 11(15), 6686. https://doi.org/10.3390/app11156686.
  41. Sun, Y., Li, G., Zhang, J., Sun, J. and Xu, J. (2020), "Development of an ensemble intelligent model for assessing the strength of cemented paste backfill", Adv. Civil Eng., 2020, 1-6. https://doi.org/10.1155/2020/1643529.
  42. Sun, Y., Zhang, J., Li, G., Ma, G., Huang, Y., Sun, J. and Nener, B. (2019), "Determination of Young's modulus of jet grouted coalcretes using an intelligent model", Eng. Geol., 252, 43-53. https://doi.org/10.1016/j.enggeo.2019.02.021.
  43. Sun, Y., Zhang, J., Li, G., Wang, Y., Sun, J. and Jiang, C. (2019), "Optimized neural network using beetle antennae search for predicting the unconfined compressive strength of jet grouting coalcretes", Int. J. Numer. Anal. Method. Geomech., 43(4), 801-813. https://doi.org/10.1002/nag.2891.
  44. Tepfers, R., et al. (2000), Bond of reinforcement in concrete, International Federation for Structural Concrete (fib), Lausanne, Switzerland.
  45. Timoney, M.J. and McCabe, B.A. (2017), "Strength verification of stabilised soil-cement columns: A laboratory investigation of the Push-In Resistance Test (PIRT)", Can. Geotech. J., 54(6), 789-805. https://doi.org/10.1139/cgj-2016-0230.
  46. Timoney, M.J., McCabe, B.A. and Bell, A.L. (2012), "Experiences of dry soil mixing in highly organic soils", Proceedings of the Institution of Civil Engineers-Ground Improvement, 165(1), 3-14. https://doi.org/10.1680/grim.2012.165.1.3.
  47. Tsai, Y.H., Wang, J., Chien, W.T., Wei, C.Y., Wang, X. and Hsieh, S.H. (2019), "A BIM-based approach for predicting corrosion under insulation", Automat. Constr., 107, 102923. https://doi.org/10.1016/j.autcon.2019.102923.
  48. Wang, J., Dai, Q. and Si, R. (2022), "Experimental and numerical investigation of fracture behaviors of steel fiber-reinforced rubber self-compacting concrete", J. Mater.Civil Eng., 34(1), 04021379. https://doi.org/10.1061/(ASCE)MT.1943-5533.0004010.
  49. Wang, J., Dai, Q., Si, R. and Guo, S. (2018), "Investigation of properties and performances of Polyvinyl Alcohol (PVA) fiber-reinforced rubber concrete", Constr. Build. Mater., 193, 631-642. https://doi.org/10.1016/j.conbuildmat.2018.11.002.
  50. Wang, L., Yuan, J., Wu, C. and Wang, X. (2019), "Practical algorithm for stochastic optimal control problem about microbial fermentation in batch culture", Optim. Lett., 13(3), 527-541. https://doi.org/10.1007/s11590-017-1220-z.
  51. Wang, L., Peng, Y., Xie, Y., Chen, B. and Du,Y. (2021), "A new iteration regularization method for dynamic load identification of stochastic structures", Mech. Syst. Signal Pr., 156, 107586. https://doi.org/10.1016/j.ymssp.2020.107586.
  52. Wu, C., Wang, X., Chen, M. and Kim, M.J. (2019), "Differential received signal strength based RFID positioning for construction equipment tracking", Adv. Eng. Inform., 42, 100960. https://doi.org/10.1016/j.aei.2019.100960.
  53. Xie, W., Zhang, R., Zeng, D., Shi, K. and Zhong, S. (2020), "Strictly dissipative stabilization of multiple-memory Markov jump systems with general transition rates: A novel eventtriggered control strategy", Int. J. Robust Nonlinear Control, 30(5), 1956-1978. https://doi.org/10.1002/rnc.4856.
  54. Xu, D., Liu, Q., Qin,Y. and Chen, B. (2020), "Analytical approach for crack identification of glass fiber reinforced polymer-sea sand concrete composite structures based on strain dissipations", Struct. Health Monit., 19(2), 1475921720974290. https://doi.org/10.1177/1475921720974290.
  55. Xu, D.S., Huang, M. and Zhou, Y. (2020), "One-dimensional compression behavior of calcareous sand and marine clay mixtures", Int. J. Geomech., 20(9), 04020137. https://doi.org/10.1061/(ASCE)GM.1943-5622.0001763.
  56. Xu, S., Wang, J., Shou, W., Ngo, T. and Wang, X. (2021), "Computer vision techniques in construction: a critical review", Arch. Comput. Method. Eng., 28, 3383-3397. https://doi.org/10.1007/s11831-020-09504-3.
  57. Yan, F. and Lin, Z. (2016), "Bond behavior of GFRP bar-concrete interface: damage evolution assessment and FE simulation implementations", Compos. Struct., 155, 63-76. https://doi.org/10.1016/j.compstruct.2016.07.078.
  58. Zhang, C. (2014), "Control Force Characteristics of Different Control Strategies for the Wind-Excited 76-Story Benchmark Building Structure", Advances in Structural Engineering, 17(4), 543-559. https://doi.org/10.1260/1369-4332.17.4.543
  59. Zhang, C. and H. Wang (2019), "Robustness of the active rotary inertia driver system for structural swing vibration control subjected to multi-type hazard excitations", Appl. Sci., 9(20), 4391. https://doi.org/10.3390/app9204391.
  60. Zhang, G., Chen, C., Li, K., Xiao, F., Sun, J., Wang, Y. and Wang, X. (2022), "Multi-objective optimisation design for GFRP tendon reinforced cemented soil", Constr. Build. Mater., 320, 126297. https://doi.org/10.1016/j.conbuildmat.2021.126297.
  61. Zhang, J., Huang, Y., Wang, Y. and Ma, G. (2020), "Multi-objective optimization of concrete mixture proportions using machine learning and metaheuristic algorithms", Constr. Build. Mater., 253, 119208. https://doi.org/10.1016/j.conbuildmat.2020.119208.
  62. Zhang, J., Sun, Y., Li, G., Wang, Y., Sun, J. and Li, J. (2020), "Machine-learning-assisted shear strength prediction of reinforced concrete beams with and without stirrups", Eng. with Comput., 1-15. https://doi.org/10.1007/s00366-020-01076-x.
  63. Zhang, W., Li, H., Li, Y., Liu, H., Chen, Y. and Ding, X. (2021), "Application of deep learning algorithms in geotechnical engineering: a short critical review", Artif. Intell. Rev., 54, 5633-5673. https://doi.org/10.1007/s10462-021-09967-1.
  64. Zhang, W., Wu, C., Zhong, H., Li, Y. and Wang, L. (2021), "Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization", Geosci. Frontiers, 12(1), 469-477. https://doi.org/10.1016/j.gsf.2020.03.007.
  65. Zhao, C., Zhong, S., Zhang, X., Zhong, Q.a and Shi, K. (2020), "Novel results on nonfragile sampled-data exponential synchronization for delayed complex dynamical networks", Int. J. Robust Nonlinear Control, 30(10), 4022-4042. https://doi.org/10.1002/rnc.4975.
  66. Zhao, R., Zhang, L., Guo, B., Chen, Y., Fan, G., Jin, Z., Guan, X., and Zhu, J. (2021), "Unveiling substitution preference of chromium ions in sulphoaluminate cement clinker phases", Compos. Part B: Eng., 222(14), 109092. https://doi.org/10.1016/j.compositesb.2021.109092.
  67. Zheng, J., Zhang, C. and Li, A. (2020), "Experimental investigation on the mechanical properties of curved metallic plate dampers", Appl. Sci., 10(1), 269. https://doi.org/10.3390/app10010269.
  68. Zhu, J., Yang, K., Chen, Y., Fan, G., Zhang, L., Guo, B. and Zhao, R. (2021), "Revealing the substitution preference of zinc in ordinary Portland cement clinker phases: A study from experiments and DFT calculations", J. Hazard. Mater., 409, 124504. https://doi.org/10.1016/j.jhazmat.2020.124504.