Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Neurocomputing strategies in structural design — decomposition based optimization

  • 64 Accesses

  • 11 Citations


The present paper introduces a scheme utilizing neurocomputing strategies for a decomposition approach to large scale optimization problems. In this scheme the modelling capabilities of a backpropagation neural network are employed to detect weak couplings in a system and to effectively decompose it into smaller, more tractable subsystems. When such partitioning of a design space is possible (decomposable systems), independent optimization in each subsystem is performed with a penalty term added to an objective function to eliminate constraint violations in all other subsystems. Dependencies among subsystems are represented in terms of global design variables, and since only partial information is needed, a neural network is used to map relations between global variables and all system constraints. A featuresensitive network (a variant of ahierarchical vector quantization technique, referred to as the HVQ network) is used for this purpose as it offers easy training, approximations of an arbitrary accuracy, and processing of incomplete input vectors. The approach is illustrated with applications to minimum weight sizing of truss structures with multiple design constraints.

This is a preview of subscription content, log in to check access.


  1. Bloebaum, C.L.; Hajela, P. 1991: Heuristic decomposition for non-hierarchic systems.Proc. 32-nd AIAA/ASME/ASCE/ASC SDM Meeting (held in Baltimore, MD)

  2. Bloebaum, C.L.; Hajela, P.; Sobieski, J. 1990: Non-hierarchic system decomposition in structural optimization.Proc. 3-rd Air Force/NASA Symp. on Recent Advances in Multidisciplinary Analysis and Optimization (held in San Francisco)

  3. Haftka, R.T. 1984: An improved computational approach for multilevel optimum design.J. Struct. Mech. 12, 245–261

  4. Hajela, P.; Berke, L. 1991: Neural network based decomposition in optimal structural synthesis.Computing Systems in Engineering 3, 1–9

  5. Hajela, P.; Berke, L. 1992: Neural networks in engineering analysis and design: an overview.Computing Systems in Engineering 3, 525–538

  6. Kirsch, U. 1983: Multilevel optimal design of reinforced concrete structure.Eng. Optimiz. 6, 207–212

  7. Kirsch, U. 1985: An improved multilevel structural synthesis.J. Struct. Mech. 13, 123–144

  8. Rumelhart, D.E.; McClelland, J.L. 1988:Parallel distributed processing. Cambridge: The MIT Press

  9. Sobieski-Sobieszczanski, J. 1988: On the sensitivity of complex, internally coupled systems.Proc. 29-th SDM Conf. (held in Williamsburg)

  10. Sobieski-Sobieszczanski, J.; Barthelemy, J.F.; Riley, K.M. 1982: Sensitivity of optimum solutions of problem parameters.AIAA J. 20, 1291–1299

  11. Sobieski-Sobieszczanski, J.; James, B.B.; Dovi, A.R. 1985: Structural optimization by multilevel decomposition.AIAA J. 23, 1775–1782

  12. Szewczyk, Z. 1993:Neurocomputing based approximate models in structural analysis and optimal design. Ph.D. Thesis, Rensselaer Polytechnic Institute, Troy, N.Y.

  13. Szewczyk, Z.; Hajela, P. 1992: Feature-sensitive neural networks in structural response estimation. In: Dagli, C.; Berke, L. (eds.)Intelligent systems in engineering through neural networks, pp. 85–89. ASME Press

Download references

Author information

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Szewczyk, Z.P., Hajela, P. Neurocomputing strategies in structural design — decomposition based optimization. Structural Optimization 8, 242–250 (1994). https://doi.org/10.1007/BF01742709

Download citation


  • Vector Quantization
  • Constraint Violation
  • Scale Optimization
  • Truss Structure
  • Quantization Technique