Computers and Concrete

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Optimal design of reinforced concrete plane frames using artificial neural networks

  • Kao, Chin-Sheng (Department of Civil Engineering, Tamkang University) ;
  • Yeh, I-Cheng (Department of Civil Engineering, Tamkang University)
  • Received : 2014.02.12
  • Accepted : 2014.10.06
  • Published : 2014.10.30
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Abstract

To solve structural optimization problems, it is necessary to integrate a structural analysis package and an optimization package. There have been many packages that can be employed to analyze reinforced concrete plane frames. However, because most structural analysis packages suffer from closeness of systems, it is very difficult to integrate them with optimization packages. To overcome the difficulty, we proposed a possible alternative, DAMDO, which integrates Design, Analysis, Modeling, Definition, and Optimization phases into an integration environment as follows. (1) Design: first generate many possible structural design alternatives. Each design alternative consists of many design variables X. (2) Analysis: employ the structural analysis software to analyze all structural design alternatives to obtain their internal forces and displacements. They are the response variables Y. (3) Modeling: employ artificial neural networks to build the models Y=f(X) to obtain the relationship functions between the design variables X and the response variables Y. (4) Definition: employ the design variables X and the response variables Y to define the objective function and constraint functions. (5) Optimization: employ the optimization software to solve the optimization problem consisting of the objective function and the constraint functions to produce the optimum design variables. The RC frame optimization problem was examined to evaluate the DAMDO approach, and the empirical results showed that it can be solved by the approach.

Keywords

References

  1. Adeli, H. and Karim, A. (1997), "Neural network model for optimization of cold-formed steel beams", J. Struct. Eng., ASCE, 123(11), 1535-1543. https://doi.org/10.1061/(ASCE)0733-9445(1997)123:11(1535)
  2. Cheng, J. and Li, Q.S. (2008), "Reliability analysis of structures using artificial neural network based genetic algorithms", Comput. Method Appl. M., 197(45), 3742-3750. https://doi.org/10.1016/j.cma.2008.02.026
  3. Cheng, J. and Li, Q.S. (2009), "A hybrid artificial neural network method with uniform design for structural optimization", Comput .Mech., 44(1), 61-71. https://doi.org/10.1007/s00466-008-0355-2
  4. Gholizadeh, S. and Salajegheh, E. (2010), "Optimal design of structures for earthquake loading by self organizing radial basis function neural networks", Adv. Struct .Eng., 13(2), 339-356. https://doi.org/10.1260/1369-4332.13.2.339
  5. Goldberg, D.E. (1989), Genetic algorithms in search, optimization, and machine learning. Reading Menlo Park: Addison-Wesley.
  6. Haykin, S. (2007), Neural Networks: A Comprehensive Foundation, Englewood Cliffs, NJ: Prentice Hall.
  7. Iranmanesh, A. and Kaveh, A. (1999), "Structural optimization by gradient-based neural networks", Int. J. Numer. Meth . Eng., 46(2), 297-311. https://doi.org/10.1002/(SICI)1097-0207(19990920)46:2<297::AID-NME679>3.0.CO;2-C
  8. Kodiyalam, S. and Gurumoorthy, R. (1997), "Neural network approximator with novel learning scheme for design optimization with variable complexity data", AIAA J., 35(4), 736-739. https://doi.org/10.2514/2.166
  9. Lagaros, N.D., Charmpis, D.C. and Papadrakakis, M. (2005), "An adaptive neural network strategy for improving the computational performance of evolutionary structural optimization", Comput.Method Appl. M, 194(30), 3374-3393. https://doi.org/10.1016/j.cma.2004.12.023
  10. Meon, M.S., Anuar, M.A., Ramli, M.H.M., Kuntjoro, W. and Muhammad, Z. (2012), "Frame optimization using neural network", IJASEIT, 2(1)28-33.
  11. Moller, O., Foschi , R.O., Quiroz, L.M. and Rubinstein, M. (2009), "Structural optimization for performance-based design in earthquake engineering: Applications of neural networks". Struct Saf, 31(6), 490-499. https://doi.org/10.1016/j.strusafe.2009.06.007
  12. Papadrakakis, M., Lagaros, N. and Tsompanakis, Y. (1998), "Structural optimization using evolution strategies and neural networks", Comput. Method. Appl. M, 156(1), 309-333. https://doi.org/10.1016/S0045-7825(97)00215-6
  13. Patel, J. and Choi, S.K. (2012), "Classification approach for reliability-based topology optimization using probabilistic neural networks", Struct. Multidiscip O., 45(4), 529-543. https://doi.org/10.1007/s00158-011-0711-2
  14. Song, H.H. (1996), "Optimizing the design of reinforced concrete structures study", Master. Thesis, Department of Civil Engineering, National Taiwan University.
  15. Yeh, I.C. (1999), "Hybrid genetic algorithms for optimization of truss structures", Comput-Aided Civ Inf, 14(3), 199-206. https://doi.org/10.1111/0885-9507.00141
  16. Yeh, J.P. and Chen, K.U. (2012), "Forecasting the lowest cost and steel ratio of reinforced concrete simple beams using the neural network", J. Civ. Eng. Constr., 3(3), 99-107.
  17. Yin, H. (2009), "Using meta heuristic rules to optimize the RC structures", Master's Thesis, Department of Construction Engineering, National Taiwan University of Science and Technology.
  18. Zhang, L. and Subbarayan, G. (2002), "An evaluation of back-propagation neural networks for the optimal design of structural systems: Part I. Training procedures", Comput. Method Appl. M., 191(25), 2873-2886. https://doi.org/10.1016/S0045-7825(01)00372-3
  19. Zhang, L. and Subbarayan, G. (2002), "An evaluation of back-propagation neural networks for the optimal design of structural systems: Part II. Numerical evaluation", Comput. Method Appl. M., 191(25), 2887-2904. https://doi.org/10.1016/S0045-7825(02)00213-X

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