Browse > Article
http://dx.doi.org/10.12989/sem.2014.52.4.739

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)
Publication Information
Structural Engineering and Mechanics / v.52, no.4, 2014 , pp. 739-753 More about this Journal
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
artificial neural networks; optimization; plane frame; ratio variable;
Citations & Related Records
연도 인용수 순위
  • Reference
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.   DOI
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.   DOI   ScienceOn
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.   DOI   ScienceOn
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.   DOI   ScienceOn
5 Gholizadeh, S. and Samavati, O.A. (2011), "Structural optimization by wavelet transforms and neural networks", Appl. Math. Model., 35(2), 915-929.   DOI   ScienceOn
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.   DOI
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.   DOI   ScienceOn
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.   DOI   ScienceOn
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.   DOI
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.   DOI
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.   DOI   ScienceOn
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.   DOI
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. Civil Eng. Constr. Tech., 3(3), 99-107.
17 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.   DOI   ScienceOn
18 Yeh, I.C. (1999), "Hybrid genetic algorithms for optimization of truss structures", Comput. Aid. Civil Infra., 14(3), 199-206.   DOI   ScienceOn