Structural Engineering and Mechanics

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Identification of a suitable ANN architecture in predicting strain in tie section of concrete deep beams

  • Received : 2013.01.18
  • Accepted : 2013.06.16
  • Published : 2013.06.25
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Abstract

The comparison of the effectiveness of artificial neural network (ANN) and linear regression (LR) in the prediction of strain in tie section using experimental data from eight high-strength-self-compact-concrete (HSSCC) deep beams are presented here. Prior to the aforementioned, a suitable ANN architecture was identified. The format of the network architecture was ten input parameters, two hidden layers, and one output. The feed forward back propagation neural network of eleven and ten neurons in first and second TRAINLM training function was highly accurate and generated more precise tie strain diagrams compared to classical LR. The ANN's MSE values are 90 times smaller than the LR's. The correlation coefficient value from ANN is 0.9995 which is indicative of a high level of confidence.

Keywords

Acknowledgement

Supported by : University of Malaya

References

  1. AASHTO (1998), "AASHTO LRFD Bridge Specifications for Highway Bridges", (2001 Interim Revisions), American Association of Highway and Transportation Officials, Washington, D.C.
  2. ACI Committee 318 (2002), Building Code Requirements for structural concrete (ACI 318-02) and commentary (318R-02). Farmington Hills: American Concrete Institute, 443.
  3. Adeli, H. (2001), "Neural networks in civil engineering", Comput-Aided Civil Infrastruct Eng., 16(1), 26-42.
  4. Ashour, A. and Yang, K.H. (2008), "Application of plasticity theory to reinforced Concrete deep beams: a review", Magazine of Concrete Research, 60, 9657-664.
  5. Ashrafi, H.R, Jalal, M. and Garmsiri, K. (2010), "Prediction of load-displacement curve of concrete reinforced by composite fibers (steel and polymeric) using artificial neural network", Expert Systems with Applications, 37(12), 7663-7668. https://doi.org/10.1016/j.eswa.2010.04.076
  6. Bilgehan, M. and Turgut, P. (2010), "Artificial neural network approach to predict compressive strength of concrete through ultrasonic pulse velocity", Res. Nondestruct. Eval., 21(1), 1-17. https://doi.org/10.1080/09349840903122042
  7. British Standard Institution (1985), "Structural Use of Concrete", (BS 8110: Part 1. Code of Practice for Design and Construction), BSI, London.
  8. Chandak, R., Upadhyay, A. and Bhargava, P. (2008), "Shear lag prediction in symmetrical laminated composite box beams using artificial neural network", Structural Engineering and Mechanics, 29(1), 77-89. https://doi.org/10.12989/sem.2008.29.1.077
  9. Chemrouk, M. and Kong, F.K. (2004), "High strength concrete continuous deep beams-with web reinforcement and shear-span variations", Advances in Structural Engineering, 7, 3229-243.
  10. CIRIA Guide 2 (1977), The design of deep beams in reinforced concrete, Over Arup and Partners, and Construction Industry Research and Information Association, London.
  11. Danielson, K.T., Adley, M.D. and O'Daniel, J.L. (2010), "Numerical procedures for extreme impulsive loading on high strength concrete structures", Computers and Concrete, 7(2), 159-167. https://doi.org/10.12989/cac.2010.7.2.159
  12. Eurocode 2 (1992), "Design of concrete structure, Part 1, general rules and regulations for building", British standards institution, London.
  13. Flood, I. and Kartam, N. (1994), "Neural networks in civil engineering, principle and understanding", ASCE J Comput Civil Eng, 8(2), 131-48. https://doi.org/10.1061/(ASCE)0887-3801(1994)8:2(131)
  14. Hakim, S.J.S. and Abdul Razak, H. (2013a), "Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANNs) for structural damage identification", Structural Engineering and Mechanics, 45(6), 779-802. https://doi.org/10.12989/sem.2013.45.6.779
  15. Hakim, S.J.S. and Abdul Razak, H. (2013b), "Structural damage detection of steel bridge girder using artificial neural networks and finite element models", Steel and Composite Structures, 4(14), 367-377.
  16. Hakim, S.J.S., Noorzaei, J., Jaafar, M.S., Jameel, M. and Mohammadhassani, M. (2011), "Application of Artificial Neural Networks to Predict Compressive strength of High Strength Concrete", International Journal of the Physical Sciences (IJPS), 6(5), 975-981.
  17. Nielsen, M.P. (1971), On the strength of reinforced concrete discs, Civil Engineering and Building Construction Series, No. 70, Acta Polytechnica Scandinavica, Copenhagen.
  18. Kang, H.T., Teng, S., Kong, F.K. and Lu, H.Y. (1997), "Main tension steel in high strength concrete deep and short beams", Structural Journal, 94(6), 752-768.
  19. Lam, J.Y.K., Ho, J.C.M. and Kwan, A.K.H. (2009), "Maximum axial load level and minimum confinement for limited ductility design of high strength concrete columns", Computers and Concrete, 6(5), 357-376. https://doi.org/10.12989/cac.2009.6.5.357
  20. Lee, H.S., Ko, D.W. and Sun, S.M. (2011), "Behavior of continuous RC deep girders that support walls with long end shear spans", Structural Engineering and Mechanics, 38(4), 385-403. https://doi.org/10.12989/sem.2011.38.4.385
  21. Londhe, R.S. (2011), "Shear strength analysis and prediction of reinforced concrete transfer beams in highrise buildings", Structural Engineering and Mechanics, 37(1), 39-59. https://doi.org/10.12989/sem.2011.37.1.039
  22. Lu, W.Y., Hwang, S.J. and Lin, I.J. (2010), "Deflection prediction for reinforced concrete deep beams", Computers and Concrete, 7(1), 1-16. https://doi.org/10.12989/cac.2010.7.1.001
  23. Malekly, H., Meysam Mousavi, S. and Hashemi, H. (2010), "A fuzzy integrated methodology for evaluating conceptual bridge design", Expert Systems with Applications, 37(7), 4910-4920. https://doi.org/10.1016/j.eswa.2009.12.024
  24. Mirzahosseini, M.R., Aghaeifar, A., Alavi, A.H., Gandomi, A.H. and Seyednour, R. (2011), "Permanent deformation analysis of asphalt mixtures using soft computing techniques", Expert Systems with Applications, 38(5), 6081-6100. https://doi.org/10.1016/j.eswa.2010.11.002
  25. Mohammadhassani, M., Jumaat, M.Z., Nezamabadi-pour, H., Jameel, M. and Arumugam Arul, M.S. (2013), "Application of artificial neural network (ANN) and linear regressions (LR) in predicting the deflection of concrete deep beams", Computers and Concrete, 11(3), 237-252. https://doi.org/10.12989/cac.2013.11.3.237
  26. Mohammadhassani, M., Jumaat, M.Z., Jameel, M. and Ashour, A. (2011a), "Failure modes and serviceability of high strength self compacting concrete deep beams", Engineering Failure Analysis, 18(8), 2272-2281. https://doi.org/10.1016/j.engfailanal.2011.08.003
  27. Mohammadhassani, M., Jumaat, M.Z., Chemrouk, M., Maghsoudi, A.A., Jameel, M. and Akib, S. (2011b), "An experimental investigation on bending stiffness and neutral axis depth variation of over-reinforced high strength concrete beams", Nuclear Engineering and Design, 241, 2060-2067. https://doi.org/10.1016/j.nucengdes.2011.02.022
  28. Mohammadhassani, M. (2011c), "Structural behaviour of high strength self compacting concrete deep beams", PhD Thesis, University Malaya.
  29. Mohammadhassani, M., Jumaat, M.Z., Chemrouk, M., Ghasemi, A., Hakim, S.J.S. and Rafieipour, N. (2011d), "An experimental investigation of the stress-strain distribution in high strength concrete deep beams", Procedia Engineering, 14, 2141-2150. https://doi.org/10.1016/j.proeng.2011.07.269
  30. Mohebbi, A., Shekarchi, M., Mahoutian, M. and Mohebbi, S. (2011), "Modeling the effects of additives on rheological properties of fresh self-consolidating cement paste using artificial neural network", Computers and Concrete, 8(3), 279-292. https://doi.org/10.12989/cac.2011.8.3.279
  31. Oh, J.K. and Shin, S.W. (2001), "Shear strength of reinforced high-strength concrete deep beams", ACI Structural Journal, 98(2), 164-173.
  32. Perera, R., Barchin, M., Arteaga, A., De Diego, A. (2010), "Prediction of the ultimate strength of reinforced concrete beams FRP-strengthened in shear using neural networks", Composites: Part B, 41, 287-298. https://doi.org/10.1016/j.compositesb.2010.03.003
  33. Perera, R. and Vique, J. (2009), "Strut-and-tie modelling of reinforced concrete beams using genetic algorithms optimization", Construction and Building Materials, 23(8), 2914-2925. https://doi.org/10.1016/j.conbuildmat.2009.02.016
  34. Pimentel, M., Cachim, P. and Figueiras, J. (2008), "Deep-beams with indirect supports: numerical modelling and experimental assessment", Computers and Concrete, 5(2), 117-134. https://doi.org/10.12989/cac.2008.5.2.117
  35. Rigoti, M. (2002), "Diagonal cracking in reinforced concrete deep beam-An experimental investigation", PhD Thesis, Concordia University, Montreal, Quebec, Canada.
  36. Sanad, A. and Saka, M.P. (2001), "Prediction of ultimate strength of reinforced concrete deep beams by neural networks", ASCE J. Struct. Eng., 127(7), 818-828. https://doi.org/10.1061/(ASCE)0733-9445(2001)127:7(818)
  37. Saridakis, K.M., Chasalevris, A.C., Papadopoulos, C.A. and Dentsoras, A.J. (2008), "Applying neural networks, genetic algorithms and fuzzy logic for the identification of cracks in shafts by using coupled response measurements", Comput. Struct. 86, 1318-1338. https://doi.org/10.1016/j.compstruc.2007.08.004
  38. Sasmal, S. and Ramanjaneyulu, K. (2008), "Condition evaluation of existing reinforced concrete bridges using fuzzy based analytic hierarchy approach", Expert Systems with Applications, 35(3), 1430-1443. https://doi.org/10.1016/j.eswa.2007.08.017
  39. Schlaich, J. and Schafer, K. (1991), "Design and detailing of structural concrete using strut-and-tie models", Structural Engineer, 69(6), 113-125.
  40. Sen, Z. (2010), "Rapid visual earthquake hazard evaluation of existing buildings by fuzzy logic modelling", Expert Systems with Applications, 37(8), 5653-5660. https://doi.org/10.1016/j.eswa.2010.02.046
  41. Tan, K., Kong, F., Teng, S. and Weng, L. (1997), "Effect of web reinforcement on high-strength concrete deep beams", ACI Structural Journal, 94(5), 572-582.
  42. Tapkin, S., Cevik, A. amd Usar, U. (2010), "Prediction of Marshall test results for polypropylene modified dense bituminous mixtures using neural networks", Expert Systems with Applications, 37(6), 4660-4670. https://doi.org/10.1016/j.eswa.2009.12.042
  43. Thompson, M.K. (2002), "The Anchorage Behavior of Headed Reinforcement in CCT Nodes and Lap Splices", Doctoral Dissertation, University of Texas at Austin.
  44. Yang, K.H., Eun, H.C., Lee, E.T. and Chung, H.S. (2003), "Shear characteristics of high strength concrete deep beams without shear reinforcement", Engineering Structures, 25(8), 1343-52. https://doi.org/10.1016/S0141-0296(03)00110-X
  45. Yang, K.H., Eun, H.C., Lee, E.T. and Chung, H.S. (2006), "The influence of web openings on the structural behaviour of reinforced high-strength concrete deep beams", Engineering Structures, 28, 1825-1834. https://doi.org/10.1016/j.engstruct.2006.03.021
  46. Watstein, D. and Mathey, R.G. (1958), "Strains in beams having diagonal cracks", ACI Journal, 55(12), 717-728.
  47. Yang, K.H., Chung, H.S. and Ashour, A.F. (2007), "Influence of section depth on the structural behaviour of reinforced concrete continuous deep beams", Magazine of Concrete Research, 59, 8575-586.
  48. Yun, Y.M. (2005), "Strut-tie model evaluation of behavior and strength of pre-tensioned concrete deep beams", Computers and Concrete, 2(4), 267-291. https://doi.org/10.12989/cac.2005.2.4.267

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