Structural Engineering and Mechanics

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Optimal design of plane frame structures using artificial neural networks and ratio variables

  • Kao, Chin-Sheng (Department of Civil Engineering, Tamkang University) ;
  • Yeh, I-Cheng (Department of Civil Engineering, Tamkang University)
  • Received : 2014.03.04
  • Accepted : 2014.06.19
  • Published : 2014.11.25
⟨ Previous Next ⟩

Abstract

There have been many packages that can be employed to analyze plane frames. However, because most structural analysis packages suffer from closeness of system, it is very difficult to integrate it with an optimization package. To overcome the difficulty, we proposed a possible alternative, DAMDO, which integrate Design, Analysis, Modeling, Definition, and Optimization phases into an integrative environment. The DAMDO methodology employs neural networks to integrate structural analysis package and optimization package so as not to need directly to integrate these two packages. The key problem of the DAMDO approach is how to generate a set of reasonable random designs in the first phase. According to the characteristics of optimized plane frames, we proposed the ratio variable approach to generate them. The empirical results show that the ratio variable approach can greatly improve the accuracy of the neural networks, and the plane frame optimization problems can be solved by the DAMDO methodology.

Keywords

References

  1. 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
  2. Gholizadeh, S. and Salajegheh, E. (2009), "Optimal design of structures subjected to time history loading by swarm intelligence and an advanced metamodel", Comput. Method. Appl. Mech. Eng., 198(37), 2936-2949. https://doi.org/10.1016/j.cma.2009.04.010
  3. Gholizadeh, S. and Salajegheh, E. (2010a), "Optimal design of structures for earthquake loading by self organizing radial basis function neural networks", Adv. in Struct. Eng., 13(2), 339-356. https://doi.org/10.1260/1369-4332.13.2.339
  4. Gholizadeh, S. and Salajegheh, E. (2010b), "Optimal seismic design of steel structures by an efficient soft computing based algorithm", J. Constr. Steel Res., 66(1), 85-95. https://doi.org/10.1016/j.jcsr.2009.07.006
  5. Gholizadeh, S. and Samavati, O.A. (2011), "Structural optimization by wavelet transforms and neural networks", Appl. Math. Model., 35(2), 915-929. https://doi.org/10.1016/j.apm.2010.07.046
  6. Gholizadeh, S., Sheidaii, M.R. and Farajzadeh, S. (2012), "Seismic design of double layer grids by neural networks", Int. J. Optim. Civ. Eng., 2(1), 29-45.
  7. Haykin, S. (2007), Neural Networks: A Comprehensive Foundation, Englewood Cliffs, Prentice Hall, NJ.
  8. 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
  9. 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
  10. 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. Mech. Eng., 194(30), 3374-3393. https://doi.org/10.1016/j.cma.2004.12.023
  11. Meon, M.S., Anuar, M.A., Ramli, M.H.M., Kuntjoro, W. and Muhammad, Z. (2012), "Frame optimization using neural network", Int. J. Adv. Sci. Eng. Inform. Tech., 2(1), 28-33. https://doi.org/10.18517/ijaseit.2.1.148
  12. 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. Safety, 31(6), 490-499. https://doi.org/10.1016/j.strusafe.2009.06.007
  13. Nocedal, J. and Wright, S.J. (1999), Numerical optimization, Springer, New York.
  14. Papadrakakis, M., Lagaros, N. and Tsompanakis, Y. (1998), "Structural optimization using evolution strategies and neural networks", Comput. Method. Appl. Mech. Eng., 156(1), 309-333. https://doi.org/10.1016/S0045-7825(97)00215-6
  15. Patel, J. and Choi, S.K. (2012), "Classification approach for reliability-based topology optimization using probabilistic neural networks", Struct. Multidiscip. Optim., 45(4), 529-543. https://doi.org/10.1007/s00158-011-0711-2
  16. Perera, R., Barchin, M., Arteaga, A. and Diego, A.D. (2010), "Prediction of the ultimate strength of reinforced concrete beams FRP-strengthened in shear using neural networks", Compos. Part B - Eng., 41(4), 287-298. https://doi.org/10.1016/j.compositesb.2010.03.003
  17. Yeh, I.C. (1999), "Hybrid genetic algorithms for optimization of truss structures", Comput. Aid. Civil Infra., 14(3), 199-206. https://doi.org/10.1111/0885-9507.00141
  18. 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. Civil Eng. Constr. Tech., 3(3), 99-107.

Cited by

  1. Prediction of behavior of fresh concrete exposed to vibration using artificial neural networks and regression model vol.60, pp.4, 2016, https://doi.org/10.12989/sem.2016.60.4.655
  2. Probabilistic seismic response transformation factors between SDOF and MDOF systems using artificial neural networks vol.18, pp.4, 2016, https://doi.org/10.21595/jve.2016.16506
  3. Optimal dimensioning for the corner combined footings vol.2, pp.2, 2014, https://doi.org/10.12989/acd.2017.2.2.169
  4. Modeling for the strap combined footings Part I: Optimal dimensioning vol.30, pp.2, 2014, https://doi.org/10.12989/scs.2019.30.2.097
  5. A new empirical formula for prediction of the axial compression capacity of CCFT columns vol.33, pp.2, 2019, https://doi.org/10.12989/scs.2019.33.2.181
  6. Displacement prediction of precast concrete under vibration using artificial neural networks vol.74, pp.4, 2014, https://doi.org/10.12989/sem.2020.74.4.559