Structural Monitoring and Maintenance

  • 제9권4호
  • /
  • Pages.337-357
  • /
  • 2022
  • /
  • 2288-6605(pISSN)
  • /
  • 2288-6613(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Prediction of ultimate shear strength and failure modes of R/C ledge beams using machine learning framework

  • Ahmed M. Yousef (Department of Structural Engineering, Faculty of Engineering, Mansoura University) ;
  • Karim Abd El-Hady (Department of Civil Engineering, Faculty of Engineering, Damietta University) ;
  • Mohamed E. El-Madawy (Department of Structural Engineering, Faculty of Engineering, Mansoura University)
  • 투고 : 2022.05.31
  • 심사 : 2022.12.12
  • 발행 : 2022.12.25
⟨ 이전 논문 다음 논문 ⟩

초록

The objective of this study is to present a data-driven machine learning (ML) framework for predicting ultimate shear strength and failure modes of reinforced concrete ledge beams. Experimental tests were collected on these beams with different loading, geometric and material properties. The database was analyzed using different ML algorithms including decision trees, discriminant analysis, support vector machine, logistic regression, nearest neighbors, naïve bayes, ensemble and artificial neural networks to identify the governing and critical parameters of reinforced concrete ledge beams. The results showed that ML framework can effectively identify the failure mode of these beams either web shear failure, flexural failure or ledge failure. ML framework can also derive equations for predicting the ultimate shear strength for each failure mode. A comparison of the ultimate shear strength of ledge failure was conducted between the experimental results and the results from the proposed equations and the design equations used by international codes. These comparisons indicated that the proposed ML equations predict the ultimate shear strength of reinforced concrete ledge beams better than the design equations of AASHTO LRFD-2020 or PCI-2020.

키워드

참고문헌

  1. AASHTO LRFD (2020), Bridge Design Specifications 9th edition, American Association of State Highway and Transportation Officials, Washington, D.C.
  2. Abuodeh, O.R., Abdalla, J.A. and Hawileh, R.A. (2020), "Prediction of shear strength and behavior of RC beams strengthened with externally bonded FRP sheets using machine learning techniques", Compos. Struct., 234, 111698. https://doi.org/10.1016/j.compstruct.2019.111698.
  3. ACI Committee 318 (2019), 'Building Code Requirements for Structural Concrete (ACI 318-19) and Commentary (ACI 318R-19)', American Concrete Institute. Farrnington Hills, Michigan 48333-9094.
  4. Balakrishnama, S. and Ganapathiraju, A. (1998), "Linear discriminant analysis-a brief tutorial", Institute for Signal and information Processing, 18, 1-8.
  5. Bayrak, O., Larson, N., Gomez, E.F. (2013), "Shear Cracking in Inverted-T Straddle Bents", Research Report No. 0-6416, The University of Texas, Texas.
  6. Berrar, D. (2018), "Bayes' theorem and naive bayes classifier", Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, Elsevier Science Publisher, Amsterdam, The Netherlands, 403-412.
  7. Birrcher, D., Tuchscherer, R., Huizinga, M., Bayrak, O., Wood, S.L. and Jirsa, J.O. (2009), "Strength and serviceability design of reinforced concrete deep beams", No. FHWA/TX-09/0-5253-1.
  8. Chalioris, C.E. and Karayannis, C.G. (2009), "Effectiveness of the use of steel fibres on the torsional behaviour of flanged concrete beams", Cement Concrete Compos., 31(5), 331-341. https://doi.org/10.1016/j.cemconcomp.2009.02.007.
  9. Cheung, A., Cabrera, C., Sarabandi, P., Nair, K.K., Kiremidjian, A. and Wenzel, H. (2008), "The application of statistical pattern recognition methods for damage detection to field data", Smart Mater. Struct., 17(6), 065023. https://doi.org/10.1088/0964-1726/17/6/065023.
  10. Chou, J.S., Tsai, C.F., Pham, A.D. and Lu, Y.H. (2014), "Machine learning in concrete strength simulations: Multi-nation data analytics", Constr. Build. Mater., 73, 771-780. https://doi.org/10.1016/j.conbuildmat.2014.09.054.
  11. Dantas, A.T.A., Leite, M.B. and de Jesus Nagahama, K. (2013), "Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks", Constr. Build. Mater., 38, 717-722. https://doi.org/10.1016/j.conbuildmat.2012.09.026.
  12. Deifalla, A. and A. Ghobarah (2014), "Behavior and analysis of inverted T-shaped RC beams under shear and torsion", Eng. Struct., 68, 57-70. https://doi.org/10.1016/j.engstruct.2014.02.011.
  13. Deifalla, A. and Ghobarah, A. (2006), "Assessing the north american bridge codes for the design of T-girders under torsion and shear". Proceeding of the 7th international conference on short & medium span bridges, Montreal.
  14. Deifalla, A. and Ghobarah, A. (2006), "Calculating the thickness of FRP jacket for shear and torsion strengthening of RC T-Girders". in third international conference on FRP composites in civil engineering (CICE), Miami, FL.
  15. Dietterich, T.G. (2000), "Ensemble methods in machine learning", International workshop on multiple classifier systems, Springer.
  16. Fereig, S. and Smith, K. (1977), "Indirect loading on beams with short shear spans", Journal Proceedings.
  17. Fernandez Gomez, E. (2012), "Design criteria for strength and serviceability of inverted-T straddle bent caps", PhD dissertation, The University of Texas, Texas.
  18. Furlong, R. and Mirza, S. (1974), Strength and serviceability of inverted-T beam cabs subjected to combined flexure, shear and torsion.
  19. Furlong, R.W., Ferguson, P.M. and Ma, J.S. (1971), "Shear and anchorage study of reinforcement in inverted T-beam bent cap girders", Center for highway research, University of Texas at Austin, 113.
  20. Galal, K. and Sekar, M. (2008), "Rehabilitation of RC inverted-T girders using anchored CFRP sheets", Compos. Part B: Eng., 39(4), 604-617. https://doi.org/10.1016/j.compositesb.2007.09.001.
  21. Garber, D.B. (2011), "Shear cracking in inverted-T straddle bents", PhD dissertation, The University of Texas, Texas.
  22. Garber, D.B., Varney, N.L., Gomez, E.F. and Bayrak, O. (2017), "Performance of ledges in inverted-T beams", ACI Struct. J., 114(2), 487-498. https://doi.org/10.14359/51689451.
  23. Gonzalez, M.P. and Zapico, J.L. (2008), "Seismic damage identification in buildings using neural networks and modal data", Comput. Struct., 86(3-5), 416-426. https://doi.org/10.1016/j.compstruc.2007.02.021.
  24. Gui, G., Pan, H., Lin, Z., Li, Y. and Yuan, Z. (2017), "Data-driven support vector machine with optimization techniques for structural health monitoring and damage detection", KSCE J. Civil Eng., 21(2), 523-534. https://doi.org/10.1007/s12205-017-1518-5.
  25. Gul, M. and Catbas, F.N. (2009), "Statistical pattern recognition for structural health monitoring using time series modeling: theory and experimental verifications", Mech. Syst. Signal Pr., 23(7), 2192-2204. https://doi.org/10.1016/j.ymssp.2009.02.013.
  26. He, L., Guo, H., Jin, Y., Zhuang, X., Rabczuk, T. and Li, Y. (2022), "Machine-learning-driven on-demand design of phononic beams", Science China Physics, Mechanics & Astronomy, 65(1), 1-12. https://doi.org/10.1007/s11425-020-1802-6
  27. Hedia, M.H., El-Metwally, S.E. and Yousef, A.M. (2020), "Behavior of Ledges in Inverted-T Beams", Ms.c. Dissertation, Mansoura University, Mansoura.
  28. Hedia, M.H., El-Metwally, S.E. and Yousef, A.M. (2020), "Design of reinforced concrete ledge beams safety and economy", Eng. Res. J., 166, 242-261. https://doi.org/10.21608/erj.2020.138830
  29. Karayannis, C. (1995), "Torsional analysis of flanged concrete elements with tension softening", Comput. Struct., 54(1), 97-110. https://doi.org/10.1016/0045-7949(94)00299-I.
  30. Karayannis, C.G. and Chalioris, C.E. (2000), "Experimental validation of smeared analysis for plain concrete in torsion", J. Struct. Eng., 126(6), 646-653. https://doi.org/10.1061/(ASCE)0733-9445(2000)126:6(646).
  31. Keller, J.M., Gray, M.R. and Givens, J.A. (1985), "A fuzzy k-nearest neighbor algorithm", IEEE T. Syst. Man Cy., 4, 580-585. https://doi.org/10.1109/TSMC.1985.6313426.
  32. Klein, G.J. (1986), "Design of spandrel beams", PCI J., 31, 76-124. https://doi.org/10.15554/pcij.09011986.76.124.
  33. Larson, N., Gomez, E.F., Garber, D., Bayrak, O. and Ghannoum, W. (2013), "Strength and serviceability design of reinforced concrete inverted-T beams", Research Report No. FHWA/TX-13/0-6416-1. 2013, The University of Texas, Texas.
  34. Ly, H.B., Le, T.T., Vu, H.L.T., Tran, V.Q., Le, L.M. and Pham, B.T. (2020), "Computational hybrid machine learning based prediction of shear capacity for steel fiber reinforced concrete beams", Sustainability, 12(7), 2709. https://doi.org/10.3390/su12072709.
  35. Markou, G. and Bakas, N.P, (2021), "Prediction of the shear capacity of reinforced concrete slender beams without stirrups by applying artificial intelligence algorithms in a big database of beams generated by 3d nonlinear finite element analysis", Comput. Concrete, 28(6), 533-547. https://doi.org/10.12989/cac.2021.28.6.533.
  36. Mirza, S. and Furlong, R. (1985), "Design of reinforced and prestressed concrete inverted T beams for bridge structures", PCI J., 30(4), 112-137. https://doi.org/10.15554/pcij.07011985.112.136
  37. Mirza, S., Furlong, R. and Ma, J. (1989), "Flexural shear and ledge reinforcement in reinforced concrete inverted T-girders", Struct. J., 85(5), 509-520. https://doi.org/10.14359/2790.
  38. Mirza, S.A. and Furlong, R.W. (1983), "Serviceability behavior and failure mechanisms of concrete inverted T-beam bridge bent caps", J. Proceedings, 80(4), 294-304. https://doi.org/10.14359/10850.
  39. Mirza, S.A. and Furlong, R.W. (1983), "Strength criteria for concrete inverted T girders", Struct. Eng., 109(8), 1836-1853. https://doi.org/10.1016/j.engstruct.2014.02.011.
  40. Noble, W.S. (2006), "What is a support vector machine?", Nature Biotechnol., 24(12), 1565-1567. https://doi.org/10.1038/nbt1206-1565.
  41. PCI Design Handbook (2020), 8th edition Precast and Prestressed Concrete. Chicago: Precast/Prestressed Concrete Institute.
  42. Rahman, J., Ahmed, K.S., Khan, N.I., Islam, K. and Mangalathu, S., (2021), "Data-driven shear strength prediction of steel fiber reinforced concrete beams using machine learning approach", Eng. Struct., 233, 111743. https://doi.org/10.1016/j.engstruct.2020.111743.
  43. Reddy, T.A., Devi, K.R. and Gangashetty, S.V. (2011), "Multilayer feedforward neural network models for pattern recognition tasks in earthquake engineering", Proceedings of the International Conference on Advanced Computing, Networking and Security, Springer, Berlin, Heidelberg.
  44. Rokach, L. and Maimon, O. (2005), "Decision trees. Data mining and knowledge discovery handbook", Springer.
  45. Salehi, H., Das, S., Chakrabartty, S., Biswas, S. and Burgueno, R. (2018), "Structural damage identification using image-based pattern recognition on event-based binary data generated from self-powered sensor networks", Struct. Control Health Monit., 25(4), e2135. https://doi.org/10.1002/stc.2135.
  46. Salehi, H., Das, S., Chakrabartty, S., Biswas, S. and Burgueno, R. (2019), "An algorithmic framework for reconstruction of time-delayed and incomplete binary signals from an energy-lean structural health monitoring system", Eng. Struct., 180, 603-620. https://doi.org/10.1016/j.engstruct.2018.11.072.
  47. Salman, W.A., El-kersh, I.H., Lotfy, E.M. and Ahmed, M.A. (2019), "Behavior of reinforced concrete inverted T-section beams containing Nano-silica", IOSR J. Mech. Civil Eng. (IOSR-JMCE), 16(5), 13-22.
  48. Siddique, R., Aggarwal, P. and Aggarwal, Y. (2011), "Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks", Adv. Eng. Software, 42(10),780-786. https://doi.org/10.1016/j.advengsoft.2011.05.016.
  49. Smith, K. and Fereig, S. (1974), "Effect of loading and supporting conditions on the shear strength of deep beams", Special Publication, 42, 441-460.
  50. Solhmirzaei, R., Salehi, H., Kodur, V. and Naser, M.Z. (2020), "Machine learning framework for predicting failure mode and shear capacity of ultra high performance concrete beams", Eng. Struct., 224, 111221. https://doi.org/10.1016/j.engstruct.2020.111221.
  51. Tan, K., Kong, F. and Weng, L. (1997), "High strength concrete deep beams subjected to combined top-and bottom-loading", Struct. Engineer, 75(11).
  52. Uddin, M.N., Yu, K., Li, L., Ye, J., Tafsirojjaman, T. and Alhaddad, W. (2022), "Developing machine learning model to estimate the shear capacity for RC beams with stirrups using standard building codes", Innov. Infrastructure Solutions, 7(3), 1-20. https://doi.org/10.1007/s41062-022-00826-8.
  53. Varney, N.L., Fernandez-Gomez, E., Garber, D.B., Ghannoum, W.M. and Bayrak, O. (2015), "Inverted-T Beams: experiments and strut-and-tie modeling", ACI Struct. J., 112(2), 147-156.
  54. Wakjira, T.G., Al-Hamrani, A., Ebead, U. and Alnahhal, W. (2022), "Shear capacity prediction of FRP-RC beams using single and ensenble ExPlainable Machine learning models", Compos. Struct., 287, 115381. https://doi.org/10.1016/j.compstruct.2022.115381.
  55. Yan, K. and Shi, C. (2010), "Prediction of elastic modulus of normal and high strength concrete by support vector machine", Constr. Build. Mater., 24(8), 1479-1485. https://doi.org/10.1016/j.conbuildmat.2010.01.006.
  56. Yang, W.Y., Cao, W., Kim, J., Park, K.W., Park, H.H., Joung, J., Ro, J.S., Lee, H.L., Hong, C.H. and Im, T. (2020), "Applied numerical methods using MATLAB", John Wiley & Sons, Hoboken, New Jersey, USA.
  57. 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.
  58. Zhu, R.H., Dhonde, H. and Hsu, T.T.C. (2003), "Crack control for ledges in inverted'-T 'bent caps", Research Report No 0-1854-5m University of Houston, Department of Civil & Environmental Engineering.