Next Generation Artificial Neural Networks and Their Application to Civil Engineering

  • Ian Flood
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4200)


The aims of this paper are: to stimulate interest within the civil engineering research community for developing the next generation of applied artificial neural networks; to identify what the next generation of devices needs to achieve, and; to provide direction in terms of how their development may proceed. An analysis of the current situation indicates that progress in the development of this technology has largely stagnated. Suggestions are made for achieving the above goals based on the use of genetic algorithms and related techniques. It is noted that this approach will require the design of some very sophisticated genetic coding mechanisms in order to develop the required higher-order network structures, and may utilize development mechanisms observed in nature such as growth, self-organization, and multi-stage objective functions. The capabilities of such an approach and the way in which they can be achieved are explored in reference to the truck weigh-in-motion problem.


Artificial Neural Network ASCE Journal Input Data Stream Cial Neural Network Strain Reading 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    ASCE: Research Library (2006), At:
  2. 2.
    Bullock, T.H., Bennett, M.V.L., Johnston, D., Josephson, R., Marder, E., Fields, R.D.: The Neuron Doctrine, Redux. Science 310, 791–793 (2005)CrossRefGoogle Scholar
  3. 3.
    Cigizoglu, H.K., Tolun, S., Öztürk, A.: Evolutionary Artificial Neural Networks in Hydrological Forecasting. In: Bizier, P., De Barry, P. (eds.) Proceedings of the World Water and Environmental Resources Congress 2003, vol. 118, p. 149. ASCE (2003)Google Scholar
  4. 4.
    Fahlman, S.E., Lebiere, C.: The Cascaded-Correlation Learning Architecture. Rep. CMU-CS-90-100. Carnegie Mellon University, Pittsburgh, PA (1990)Google Scholar
  5. 5.
    Flood, I., Kartam, N.: Neural Networks in Civil Engineering, I: Principles and Understanding, II: Systems and Application. Journal of Computing in Civil Engineering 8(2), 131–162 (1994)CrossRefGoogle Scholar
  6. 6.
    Flood, I., Kartam, N.: Systems. In: Kartam, N., Flood, I., Garrett Jr., J.H. (eds.) Artificial Neural Networks for Civil Engineers: Fundamentals and Applications, pp. 19–43. ASCE, Nashville (1997)Google Scholar
  7. 7.
    Flood, I., Issa, R.R.A., Abi Shdid, C.: Simulating the Thermal Behavior of Buildings Using ANN-Based Coarse-Grain Modeling. Journal of Computing in Civil Engineering 18(3), 207–214 (2004)CrossRefGoogle Scholar
  8. 8.
    Gagarin, N., Flood, I., Albrecht, P.: Computing Truck Attributes with Artificial Neural Networks. Journal of Computing in Civil Engineering 8(2), 179–200 (1994)CrossRefGoogle Scholar
  9. 9.
    Goldberg, D.E., Kuo, C.H.: Genetic Algorithms in Pipeline Optimization. Journal of Computing in Civil Engineering 1(2), 128–141 (1987)CrossRefGoogle Scholar
  10. 10.
    Jerison, H.J.: The Evolution of Intelligence. In: Sternberg, R.J. (ed.) Handbook of Intelligence. Cambridge University Press, Cambridge (2000)Google Scholar
  11. 11.
    Karunanithi, N., Grenney, W.J., Whitley, D., Bovee, K.: Neural Networks for River Flow Prediction. Journal of Computing in Civil Engineering 8(2), 201–220 (1994)CrossRefGoogle Scholar
  12. 12.
    Kohonen, T.: Self-organization and associative memory, 3rd edn. Springer, Berlin (1989)Google Scholar
  13. 13.
    Koumousis, V.K., Georgiou, P.G.: Genetic Algorithms in Discrete Optimization of Steel Truss Roofs. Journal of Computing in Civil Engineering 8(3), 309–325 (1994)CrossRefGoogle Scholar
  14. 14.
    Moses, F., Kriss, M.: Weight-in-Motion Instrumentation. Report FHWA/RD-78/81. Federal Highway Administration, McLean, VA (1978)Google Scholar
  15. 15.
    Rosenblatt, F.: The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain. Psychological Review 65(6), 386–408 (1958)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Sirosh, J., Miikkulainen, R.: Topographic Receptive Fields and Patterned Lateral Interaction in a Self-Organizing Model of the Primary Visual Cortex. Neural Computation 9, 577–594 (1997)CrossRefGoogle Scholar
  17. 17.
    Szewczyk, Z.P., Hajela, P.: Damage Detection in Structures Based on Feature- Sensitive Neural Networks. Journal of Computing in Civil Engineering 8(2), 163–178 (1994)CrossRefGoogle Scholar
  18. 18.
    Thomson Corporation: ISI Web of Knowledge, Web of Science (2006), At:

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ian Flood
    • 1
  1. 1.Rinker School, College of Design Construction and PlanningUniversity of FloridaGainesvilleUSA

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