Computers and Concrete

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Predicting the shear strength of reinforced concrete beams using Artificial Neural Networks

  • Asteris, Panagiotis G. (Computational Mechanics Laboratory, School of Pedagogical and Technological Education) ;
  • Armaghani, Danial J. (Institute of Research and Development, Duy Tan University) ;
  • Hatzigeorgiou, George D. (School of Science and Technology, Hellenic Open University) ;
  • Karayannis, Chris G. (Department of Civil Engineering, Democritus University of Thrace) ;
  • Pilakoutas, Kypros (Department of Civil and Structural Engineering, University of Sheffield)
  • Received : 2019.08.23
  • Accepted : 2019.10.22
  • Published : 2019.11.25
⟨ Previous Next ⟩

Abstract

In this research study, the artificial neural networks approach is used to estimate the ultimate shear capacity of reinforced concrete beams with transverse reinforcement. More specifically, surrogate approaches, such as artificial neural network models, have been examined for predicting the shear capacity of concrete beams, based on experimental test results available in the pertinent literature. The comparison of the predicted values with the corresponding experimental ones, as well as with available formulas from previous research studies or code provisions highlight the ability of artificial neural networks to evaluate the shear capacity of reinforced concrete beams in a trustworthy and effective manner. Furthermore, for the first time, the (quantitative) values of weights for the proposed neural network model, are provided, so that the proposed model can be readily implemented in a spreadsheet and accessible to everyone interested in the procedure of simulation.

Keywords

References

  1. ACI Committee 318 (2015), Building Code Requirements for Reinforced Concrete, ACI 318M-14 and Commentary-ACI 318RM-14, American Concrete Institute, Farmington Hills, Michigan.
  2. Adeli, H. (2001), "Neural networks in civil engineering: 1989-2000", Compu. Aid. Civil Infrastr Eng., 16(2), 126-142. https://doi.org/10.1111/0885-9507.00219.
  3. Akkurt, S., Tayfur, G. and Can, S. (2004), "Fuzzy logic model for the prediction of cement compressive strength", Cement Concrete Res., 34(8), 1429-1433. https://doi.org/10.1016/j.cemconres.2004.01.020.
  4. Amani, J. and Moeini, R. (2012), "Prediction of shear strength of reinforced concrete beams using adaptive neuro-fuzzy inference system and artificial neural network", Scientia Iranica, 19(2), 242-248. https://doi.org/10.1016/j.scient.2012.02.009
  5. Angelakos, D., Bentz, E.C. and Collins, M.P. (2001), "Effect of concrete strength and minimum stirrups on shear strength of large members", ACI Struct. J., 98(3), 290-300.
  6. Apostolopoulou, M., Armaghani, D.J., Bakolas, A., Douvika, M.G., Moropoulou, A. and Asteris, P.G. (2019), "Compressive strength of natural hydraulic lime mortars using soft computing techniques", Procedia Struct. Integ., 17, 914-923. https://doi.org/10.1016/j.prostr.2019.08.122.
  7. Armaghani, D.J., Hatzigeorgiou, G.D., Karamani, Ch., Skentou, A., Zoumpoulaki, I. and Asteris, P.G. (2019), "Soft computingbased techniques for concrete beams shear strength", Procedia Struct. Integ., 17, 924-933. https://doi.org/10.1016/j.prostr.2019.08.123.
  8. Armaghani, D.J., Mohamad, E.T., Narayanasamy, M.S., Narita, N. and Yagiz, S. (2017), "Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition", Tunnel. Underg. Space Technol., 63, 29-43. https://doi.org/10.1016/j.tust.2016.12.009.
  9. Asteris, P.G. and Kolovos, K.G. (2019), "Self-compacting concrete strength prediction using surrogate models", Neur. Comput. Appl., 31(1), 409-424. https://doi.org/10.1007/s00521-017-3007-7.
  10. Asteris, P.G. and Nikoo, M. (2019), "Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures", Neur. Comput. Appl., 1-11. https://doi.org/10.1007/s00521-018-03965-1.
  11. Asteris, P.G. and Plevris, V. (2013), "Neural network approximation of the masonry failure under biaxial compressive stress", ECCOMAS Special Interest Conference-SEECCM 2013: 3rd South-East European Conference on Computational Mechanics, Proceedings-An IACM Special Interest Conference, 584-598.
  12. Asteris, P.G. and Plevris, V. (2017), "Anisotropic Masonry failure criterion using artificial neural networks", Neur. Comput. Appl., 28(8), 2207-2229. https://doi.org/10.1007/s00521-016-2181-3.
  13. Asteris, P.G., Apostolopoulou, M., Skentou, A.D. and Antonia Moropoulou, A. (2019d), "Application of artificial neural networks for the prediction of the compressive strength of cement-based mortars", Comput. Concrete, 24(4), 329-345. https://doi.org/10.12989/cac.2019.24.4.329.
  14. Asteris, P.G., Ashrafian, A. and Rezaie-Balf, M. (2019a), "Prediction of the compressive strength of self-compacting concrete using surrogate models", Comput. Concrete, 24(2), 137-150. https://doi.org/10.12989/cac.2019.24.2.137.
  15. Asteris, P.G., Kolovos, K.G., Douvika, M.G. and Roinos, K. (2016a), "Prediction of self-compacting concrete strength using artificial neural networks", Eur. J. Environ. Civil Eng., 20, s102-s122. https://doi.org/10.1080/19648189.2016.1246693.
  16. Asteris, P.G., Moropoulou, A., Skentou, A.D., Apostolopoulou, M., Mohebkhah, A., Cavaleri, L., Rodrigues, H. and Varum, H. (2019c), "Stochastic vulnerability assessment of masonry structures: Concepts, modeling and restoration aspects", Appl. Sci., 9(2), 243. https://doi.org/10.3390/app9020243.
  17. Asteris, P.G., Nozhati, S., Nikoo, M., Cavaleri, L. and Nikoo, M. (2019b), "Krill herd algorithm-based neural network in structural seismic reliability evaluation", Mech. Adv. Mater. Struct., 26(13), 1146-1153. https://doi.org/10.1080/15376494.2018.1430874.
  18. Asteris, P.G., Roussis, P.C. and Douvika, M.G. (2017), "Feedforward neural network prediction of the mechanical properties of sandcrete materials", Sensor., 17(6), 1344. https://doi.org/10.3390/s17061344.
  19. Asteris, P.G., Tsaris, A.K., Cavaleri, L., Repapis, C.C., Papalou, A., Di Trapani, F. and Karypidis, D.F. (2016b), "Prediction of the fundamental period of infilled RC frame structures using artificial neural networks", Comput. Intel. Neurosci., 2016, 5104907. https://doi.org/10.1155/2016/5104907
  20. Baykasoglu, A., Dereli, T.U. and Tanis, S. (2004), "Prediction of cement strength using soft computing techniques", Cement Concrete Res., 34(11), 2083-2090. https://doi.org/10.1016/j.cemconres.2004.03.028.
  21. Berry, M.J.A. and Linoff, G. (1997), Data Mining Techniques, John Wiley & Sons, NY.
  22. Blum, A. (1992), Neural Networks in C++, Wiley, NY.
  23. Boger, Z. and Guterman, H. (1997), "Knowledge extraction from artificial neural network models", IEEE Systems, Man, and Cybernetics Conference, Orlando, FL, USA.
  24. Cavaleri, L. Chatzarakis, G.E., DiTrapani, F., Douvika, M.G., Foskolos, F.M., Fotos, Α., Giovanis, D.G., Karypidis, D.F., Livieratos, S., Roinos, K., Tsaris, A.K., Vaxevanidis, N.M., Vougioukas, E. and Asteris, P.G. (2016), "Surface roughness prediction of electro-discharge machined components using artificial neural networks", 5th International Conference on Integrity, Reliability and Failure, Faculty of Engineering, Porto, July.
  25. Cavaleri, L., Asteris, P.G., Psyllaki, P.P., Douvika, M.G., Skentou, A.D. and Vaxevanidis, N.M. (2019), "Prediction of surface treatment effects on the tribological performance of tool steels using artificial neural networks", Appl. Sci., 9(14), 2788. https://doi.org/10.3390/app9142788.
  26. Cavaleri, L., Chatzarakis, G.E., Di Trapani, F., Douvika, M.G., Roinos, K., Vaxevanidis, N.M. and Asteris, P.G. (2017), "Modeling of surface roughness in electro-discharge machining using artificial neural networks", Adv. Mater. Res., 6(2), 169-184. https://doi.org/10.12989/amr.2017.6.2.169.
  27. Chen, H., Asteris, P.G., Armaghani, D.J., Gordan, B. and Pham, B.T. (2019), "Assessing dynamic conditions of the retaining wall using two hybrid intelligent models", Appl. Sci., 9(6), 1042. https://doi.org/10.3390/app9061042.
  28. Chen, Z. (2013), "An overview of Bayesian methods for neural spike train analysis", Comput. Intel. Neurosci., 2013, 251905. https://doi.org/10.1155/2013/251905.
  29. Cladera, A. and Marí, A.R. (2004a), "Shear design procedure for reinforced normal and high-strength concrete beams using artificial neural networks. Part I: Beams without stirrups", Eng. Struct., 26(7), 917-926. https://doi.org/10.1016/j.engstruct.2004.02.010.
  30. Cladera, A. and Marí, A.R. (2004b), "Shear design procedure for reinforced normal and high-strength concrete beams using artificial neural networks. Part II: Beams with stirrups", Eng. Struct., 26(7), 927-936. https://doi.org/10.1016/j.engstruct.2004.02.011.
  31. Clark, A.P. (1951), "Diagonal tension in reinforced concrete beams", ACI J., 48(2), 145-156.
  32. CSA (2004), Design of Concrete Structures A23.3-04, Canadian Standards Association, Rexdale, Ontario.
  33. Delen, D., Sharda, R. and Bessonov, M. (2006), "Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks", Accid. Anal. Prev., 38, 434-444. https://doi.org/10.1016/j.aap.2005.06.024.
  34. Dias, W.P.S. and Pooliyadda, S.P. (2001), "Neural networks for predicting properties of concretes with admixtures", Constr. Build. Mater., 15(7), 371-379. https://doi.org/10.1016/S0950-0618(01)00006-X.
  35. El Chabib, H., Nehdi, M. and Saïd, A. (2006), "Predicting the effect of stirrups on shear strength of reinforced normal-strength concrete (NSC) and high-strength concrete (HSC) slender beams using artificial intelligence", Can. J. Civil Eng., 33(8), 933-944. https://doi.org/10.1139/l06-033.
  36. European Committee for Standardization CEN (2004), Eurocode 2: Design of concrete structures - Part 1-1: General Rules and Rules for Buildings, European Standard EN 1992-1-1.
  37. Feldman, A. and Siess, C.P. (1955), "Effect of moment shear ratio on diagonal tension cracking and strength in shear of reinforced concrete beams", Univ. of Illinois Civil Eng. Studies, Struct. Research Series No. 107.
  38. Flood, I. and Kartam, N. (1994), "Neural networks in civil engineering. I: Principles and understanding", J. Comput. Civil Eng., 8(2), 131-148. https://doi.org/10.1061/(ASCE)0887-3801(1994)8:2(131).
  39. Fukuhara, M. and Kokusho, S. (1982), "Effectiveness of high tension shear reinforcement in reinforcedconcrete members", J. Struct. Constr. Eng., AIJ 320, 12-20.
  40. Gandomi, A.H., Alavi, A.H., Gandomi, M. and Kazemi, S. (2017), "Formulation of shear strength of slender RC beams using gene expression programming, part II: With shear reinforcement", Measure., 95, 367-376. https://doi.org/10.1016/j.measurement.2016.10.024.
  41. Ghannoum, W.M. (1998), "Size effect on shear strength of reinforced concrete beams", Master Thesis, Department of Civil Engineering and Applied Mechanics, McGill University, Montreal, Canada.
  42. Giovanis, D.G. and Papadopoulos, V. (2015), "Spectral representation-based neural network assisted stochastic structural mechanics", Eng. Struct., 84, 382-394. https://doi.org/10.1016/j.engstruct.2014.11.044.
  43. Iruansi, O., Guadagnini, M., Pilakoutas, K. and Neocleous, K. (2010), "Predicting the shear strength of RC beams without stirrups using Bayesian neural network", Proceedings of the 4th International Workshop on Reliable Engineering Computing, Robust Design-Coping with Hazards, Risk and Uncertainty, Singapore, March.
  44. Ismail, K.S. (2009), "Strength prediction of struts in high-strength reinforced concrete deep beams by strut-and-tie model", Master Thesis, University of Salahaddin, Hawler, Iraq.
  45. Kani, G.N.J. (1967), "How safe are our large reinforced concrete beams?", ACI J., 64(3), 128-141.
  46. Kaveh, A., Bakhshpoori, T. and Hamze-Ziabari, S.M. (2018), "GMDH-based prediction of shear strength of FRP-RC beams with and without stirrups", Comput. Concrete, 22(2), 197-207. https://doi.org/10.12989/cac.2018.22.2.197.
  47. Keskin, R.S.O. (2017), "Predicting Shear strength of SFRC slender beams without stirrups using an ANN model", Struct. Eng. Mech., 61(5), 605-615. https://doi.org/10.12989/sem.2017.61.5.605.
  48. Kotsovou, G.M., Cotsovos, D.M. and Lagaros, N.D. (2017), "Assessment of RC exterior beam-column Joints based on artificial neural networks and other methods", Eng. Struct., 144, 1-18. https://doi.org/10.1016/j.engstruct.2017.04.048.
  49. Lamanna, J., Malgaroli, A., Cerutti, S. and Signorini, M.G. (2012), "Detection of fractal behavior in temporal series of synaptic quantal release events: A feasibility study", Comput. Intel. Neurosci., 2012, 704673. https://doi.org/10.1155/2012/704673.
  50. Lee, S.C. (2003), "Prediction of concrete strength using artificial neural networks", Eng. Struct., 25(7), 849-857. https://doi.org/10.1016/S0141-0296(03)00004-X.
  51. Londhe, R.S. (2011), "Shear strength analysis and prediction of reinforced concrete transfer beams in high-rise buildings", Struct. Eng. Mech., 37(1), 39-59. https://doi.org/10.12989/sem.2011.37.1.039.
  52. Lourakis, M.I.A. (2005), "A brief description of the Levenberg-Marquardt algorithm implemented by levmar", Institute of Computer Science Foundation for Research and Technology - Hellas (FORTH), http://www.ics.forth.gr/-lourakis/levmar/levmar.pdf.
  53. Mansour, M.Y., Dicleli, M., Lee, J.Y. and Zhang, J. (2004), "Predicting the shear strength of reinforced concrete beams using artificial neural networks", Eng. Struct., 26(6), 781-799. https://doi.org/10.1016/j.engstruct.2004.01.011.
  54. Mohammadhassani, M., Nezamabadi-pour, H., Suhatril, M. and Shariati, M. (2014), "An evolutionary fuzzy modelling approach and comparison of different methods for shear strength prediction of high-strength concrete beams without stirrups", Smart Struct. Syst., 14(5), 785-809. http://dx.doi.org/10.12989/sss.2014.14.5.785.
  55. Mohammadhassani, M., Saleh, A.M.D., Suhatril, M. and Safa, M. (2015), "Fuzzy modelling approach for shear strength prediction of RC deep beams", Smart Struct. Syst., 16(3), 497-519. https://doi.org/10.12989/sss.2015.16.3.497.
  56. NZS 3101 (2006), Concrete Structures Standard, Part 1 - The Design of Concrete Structures, New Zealand Standards, Wellington.
  57. Oreta, A.W.C. (2004), "Simulating size effect on shear strength of RC beams without stirrups using neural networks", Eng. Struct., 26(5), 681-691. https://doi.org/10.1016/j.engstruct.2004.01.009.
  58. Ozcan, F., Atis, C.D., Karahan, O., Uncuoǧlu, E. and Tanyildizi, H. (2009), "Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete", Adv. Eng. Softw., 40(9), 856-863. https://doi.org/10.1016/j.advengsoft.2009.01.005.
  59. Papadopoulos, V., Giovanis, D.G., Lagaros, N.D. and Papadrakakis, M. (2012), "Accelerated subset simulation with neural networks for reliability analysis", Comput. Meth. Appl. Mech. Eng., 223-224, 70-80. https://doi.org/10.1016/j.cma.2012.02.013.
  60. Placas, A. and Regan, P.E. (1971), "Shear failure of reinforced concrete beams", ACI J., 68(10), 763-773.
  61. Plevris, V. and Asteris, P. (2015), "Anisotropic failure criterion for brittle materials using Artificial Neural Networks", COMPDYN 2015 - 5th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, 2259-2272.
  62. Plevris, V. and Asteris, P.G. (2014a), "Modeling of masonry compressive failure using Neural Networks", OPT-i 2014 - 1st International Conference on Engineering and Applied Sciences Optimization, 2843-2861.
  63. Plevris, V. and Asteris, P.G. (2014b), "Modeling of masonry failure surface under biaxial compressive stress using Neural Networks", Constr. Build. Mater., 55, 447-461. https://doi.org/10.1016/j.conbuildmat.2014.01.041.
  64. Robinson, J.R. (1968), "Internal CEB (Comlt- Europeen du Beton) correspondence", 12th Plenary Session, Lausanne, April.
  65. Russo, G., Mitri, D. and Pauletta, M. (2013). "Shear strength design formula for RC beams with stirrups", Eng. Struct., 51, 226-235. https://doi.org/10.1016/j.engstruct.2013.01.024
  66. Sanad, A. and Saka, M.P. (2001), "Prediction of ultimate shear strength of reinforced-concrete deep beams using neural networks", J. Struct. Eng., 127(7), 818-828. https://doi.org/10.1061/(ASCE)0733-9445(2001)127:7(818).
  67. Sarir, P., Chen, J., Asteris, P.G., Armaghani, D.J. and Tahir, M.M. (2019), "Developing GEP tree-based, neuro-swarm, and whale optimization models for evaluation of bearing capacity of concrete-filled steel tube columns", Eng. Comput., 1-19. https://doi.org/10.1007/s00366-019-00808-y.
  68. Sarveghadi, M., Gandomi, A.H., Bolandi, H. and Alavi, A.H. (2019), "Development of prediction models for shear strength of SFRCB using a machine learning approach", Neur. Comput. Appl., 31(7), 2085-2094. https://doi.org/10.1007/s00521-015-1997-6.
  69. Seleemah, A.A. (2005), "A neural network model for predicting maximum shear capacity of concrete beams without transverse reinforcement", Can. J. Civil Eng., 32(4), 644-657. https://doi.org/10.1139/l05-003.
  70. Seleemah, A.A. (2012), "A multilayer perceptron for predicting the ultimate", J. Civil Eng. Constr. Technol., 3(2), 64-79. https://doi.org/10.5897/JCECT11.098.
  71. Tompos, E.J. and Frosch, R.J. (2002), "Influence of beam size, longitudinal reinforcement, and stirrup effectiveness on concrete shear strength", ACI J., 99(5), 559-567.
  72. Topcu, I.B. and Saridemir, M. (2008), "Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic", Comput. Mater. Sci., 41(3), 305-311. https://doi.org/10.1016/j.commatsci.2007.04.009.
  73. Trtnik, G., Kavcic, F. and Turk, G. (2009), "Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks", Ultrasonics, 49(1), 53-60. https://doi.org/10.1016/j.ultras.2008.05.001.
  74. Waszczyszyn, Z. and Ziemianski, L. (2001), "Neural networks in mechanics of structures and materials-New results and prospects of applications", Comput. Struct., 79(22-25), 2261-2276. https://doi.org/10.1016/S0045-7949(01)00083-9.
  75. Xie, Y., Ahmad, S.H., Yu, T., Hino, S. and Chung, W. (1994), "Shear ductility of reinforced concrete beams of normal and high-strength concrete", ACI J., 91(2), 140-149.
  76. Xu, H., Zhou, J., Asteris, P.G., Armaghani, D.J. and Tahir, M.Md. (2019), "Supervised machine learning techniques to the prediction of tunnel boring machine penetration rate", Appl. Sci., 9(18), 3715. https://doi.org/10.3390/app9183715.
  77. Yaseen, Z.M., Tran, M.T., Kim, S., Bakhshpoori, T. and Deo, R.C. (2018), "Shear strength prediction of steel fiber reinforced concrete beam using hybrid intelligence models: A new approach", Eng. Struct., 177, 244-255. https://doi.org/10.1016/j.engstruct.2018.09.074.
  78. Yavuz, G. (2016), "Shear strength estimation of RC deep beams using the ANN and strut-and-tie approaches", Struct. Eng. Mech., 57(4), 657-680. https://doi.org/10.12989/sem.2016.57.4.657.
  79. Yavuz, G. (2019), "Determining the shear strength of FRP-RC beams using soft computing and code methods", Comput. Concrete, 23(1), 49-60. https://doi.org/10.12989/cac.2019.23.1.049.
  80. Yoon, Y.S., Cook, W.D. and Mitchell, D. (1996), "Minimum shear reinforcement in normal, medium and high-strength concrete beams", ACI J., 93(5), 576-584.
  81. Zararis, P.D., Karaveziroglou, M.K., Zararis, I.P., Pnevmatikos, G. and Sfika, M. (2009), "Shear strength of very short over reinforced concrete beams", 16th Concrete Conference, Paphos, Cyprus, October. (in Greek)

Cited by

  1. Random Forest and Bayesian Network Techniques for Probabilistic Prediction of Flyrock Induced by Blasting in Quarry Sites vol.29, pp.2, 2020, https://doi.org/10.1007/s11053-019-09611-4
  2. Surrogate Neural Network Model for Prediction of Load-Bearing Capacity of CFSS Members Considering Loading Eccentricity vol.10, pp.10, 2019, https://doi.org/10.3390/app10103452
  3. The Use of Fuzzy Linear Regression and ANFIS Methods to Predict the Compressive Strength of Cement vol.12, pp.8, 2019, https://doi.org/10.3390/sym12081295
  4. A Novel Hybrid Model Based on a Feedforward Neural Network and One Step Secant Algorithm for Prediction of Load-Bearing Capacity of Rectangular Concrete-Filled Steel Tube Columns vol.25, pp.15, 2019, https://doi.org/10.3390/molecules25153486
  5. A neuro-fuzzy approach to predict the shear contribution of end-anchored FRP U-jackets vol.26, pp.5, 2019, https://doi.org/10.12989/cac.2020.26.5.397
  6. A Novel Intelligent ELM-BBO Technique for Predicting Distance of Mine Blasting-Induced Flyrock vol.29, pp.6, 2020, https://doi.org/10.1007/s11053-020-09676-6
  7. Estimating the compressive strength of HPFRC containing metallic fibers using statistical methods and ANNs vol.10, pp.6, 2019, https://doi.org/10.12989/acc.2020.10.6.479
  8. Performance Evaluation of Hybrid WOA-SVR and HHO-SVR Models with Various Kernels to Predict Factor of Safety for Circular Failure Slope vol.11, pp.4, 2019, https://doi.org/10.3390/app11041922
  9. Investigation of ANN architecture for predicting the compressive strength of concrete containing GGBFS vol.16, pp.12, 2019, https://doi.org/10.1371/journal.pone.0260847