Skip to main content
SpringerLink
Log in

Advances in Neural Networks in Computational Mechanics and Engineering

  • Chapter
Advances of Soft Computing in Engineering

Part of the book series: CISM International Centre for Mechanical Sciences ((CISM,volume 512))

Abstract

A vast majority of engineering problems are inverse problems, while the mathematically based engineering problem solving methods and computational mechanics are primarily capable of solving forward problems. Nature has evolved highly effective, robust and imprecision tolerant problem solving strategies for very difficult inverse problems. Biologically inspired soft computing methods such as neural network, genetic algorithm and fuzzy logic inherit the basic characteristics of nature’s problem solving methods and, as such, they are suitable for inverse problems in engineering. In this chapter we start by discussing the fundamental differences between the mathematically based engineering problem solving methods and biologically inspired soft computing methods. Bulk of the rest of the chapter is devoted to applications of neural networks in computational mechanics and several difficult inverse engineering problems.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Bani-Hani, K. and Ghaboussi, J. (1998a). Nonlinear structural control using neural networks. Journal of Engineering Mechanics Division, ASCE 124: 319–327.

    Article  Google Scholar 

  • Bani-Hani, K. and Ghaboussi, J. (1998b). Neural networks for structural control of a benchmark problem: Active tendon system. International Journal for Earthquake Engineering and Structural Dynamics 27:1225–1245.

    Article  Google Scholar 

  • Bani-Hani, K., Ghaboussi, J. and Schneider, S.P. (1999a). Experimental study of identification and structural control using neural network: I. Identification. International Journal for Earthquake Engineering and Structural Dynamics 28:995–1018.

    Article  Google Scholar 

  • Bani-Hani, K., Ghaboussi, J. and Schneider, S.P. (1999b). Experimental study of identification and structural control using neural network: II. Control. International Journal for Earthquake Engineering and Structural Dynamics 28:1019–1039.

    Article  Google Scholar 

  • Chang, Y.-K. (2006). Evolutionary Based Methodology for Form-finding and Design of Tensegrity Structures. PhD thesis, Dept. of Civil and Environmental Engineering, Univ. of Illinois at Urbana-Champaign, Urbana, Illinois.

    Google Scholar 

  • Chou, J.-H. (2000). Study of Condition Monitoring of Bridges Using Genetic Algorithm. PhD thesis, Dept. of Civil and Environmental Engineering, Univ. of Illinois at Urbana-Champaign, Urbana, Illinois.

    Google Scholar 

  • Chou, J.-H. and Ghaboussi, J. (1997). Structural damage detection and identification using genetic algorithm. Proceedings, International Conference on Artificial Neural Networks in Engineering, ANNIE. St. Louis, Missouri.

    Google Scholar 

  • Chou, J.-H. and Ghaboussi, J. (1998). Studies in bridge damage detection using genetic algorithm. Proceedings, Sixth East Asia-Pacific Conference on Structural Engineering and Construction (EASEC6). Taiwan.

    Google Scholar 

  • Chou J. H. and Ghaboussi, J. (2001). Genetic algorithm in structural damage detection. International Journal of Computers and Structures 79:1335–1353.

    Article  Google Scholar 

  • Chou, J.-H., Ghaboussi, J. and Clark, R. (1998). Application of neural networks to the inspection of railroad rail. Proceedings, Twenty-Fifth Annual Conference on Review of Progress in Quantitative Nondestructive Evaluation. Snowbird Utah.

    Google Scholar 

  • Fu, Q., Hashash, Y. M. A., Jung, S., and Ghaboussi, J. (2007). Integration of laboratory testing and constitutive modeling of soils. Computer and Geotechnics 34: 330–345.

    Article  Google Scholar 

  • Ghaboussi, J. (1992a). Potential applications of neuro-biological computational models in geotechnical engineering. Proceedings, International Conference on Numerical Models in Geotechnical Engineering, NUMOG IV. Swansea, UK

    Google Scholar 

  • Ghaboussi, J. (1992b). Neuro-biological computational models with learning capabilities and their applications in geomechanical modeling. Proceedings, Workshop on Recent Accomplishments and Future Trends in Geomechanics in the 21st Century. Norman, Oklahoma.

    Google Scholar 

  • Ghaboussi, J. (2001) Biologically Inspired Soft Computing Methods in Structural Mechanics and Engineering, International Journal of Structural Engineering and Mechanics 11:485–502.

    Google Scholar 

  • Ghaboussi, J. and Banan, M.R. (1994). Neural networks in engineering diagnostics, Proceedings, Earthmoving Conference, Society of Automotive Engineers, Peoria, Illinois, SAE Technical Paper No. 941116.

    Google Scholar 

  • Ghaboussi, J., Banan, M.R. and Florom, R. L. (1994). Application of neural networks in acoustic wayside fault detection in railway engineering. Proceedings, World Congress on Railway Research. Paris, France.

    Google Scholar 

  • Ghaboussi, J., Garrett, Jr., J.H. and Wu, X. (1990). Material modeling with neural networks. Proceedings, International Conference on Numerical Methods in Engineering: Theory and Applications. Swansea, U.K.

    Google Scholar 

  • Ghaboussi, J., Garrett, J.H. and Wu, X. (1991). Knowledge-based modeling of material behavior with neural networks. Journal of Engineering Mechanics Division, ASCE 117:132–153.

    Article  Google Scholar 

  • Ghaboussi, J. and Joghataie, A. (1995). Active control of structures using Neural networks. Journal of Engineering mechanics Division, ASCE 121: 555–567.

    Article  Google Scholar 

  • Ghaboussi, J., Lade, P.V. and Sidarta, D.E. (1994). Neural network based modeling in geomechanics. Proceedings, International Conference on Numerical Methods and Advances in Geomechanics.

    Google Scholar 

  • Ghaboussi, J. and Lin, C.-C.J. (1998). A new method of generating earthquake accelerograms using neural networks. International Journal for Earthquake Engineering and Structural Dynamics 27:377–396.

    Article  Google Scholar 

  • Ghaboussi, J., Pecknold, D.A., Zhang, M. and HajAli, R. (1998). Autoprogressive training of neural network constitutive models. International Journal for Numerical Methods in Engineering 42:105–126.

    Article  MATH  Google Scholar 

  • Ghaboussi, J., Pecknold D.A. and Zhang, M. (1998). Autoprogressive training of neural networks in complex systems. Proceedings, International Conference on Artificial Neural Networks in Engineering, ANNIE. St. Louis, Missouri.

    Google Scholar 

  • Ghaboussi, J. and Sidarta, D.E. (1998). A new nested adaptive neural network for modeling of constitutive behavior of materials. International Journal of Computer and Geotechnics 22:29–51.

    Article  Google Scholar 

  • Ghaboussi J. and Wu, X. (1998). Soft computing with neural networks for engineering applications: Fundamental issues and adaptive approaches. International Journal of Structural Engineering and Mechanics 8: 955–969.

    Google Scholar 

  • Ghaboussi, J., Zhang, M., Wu X. and Pecknold, D.A. (1997). Nested adaptive neural networks: A new architecture. Proceedings, International Conference on Artificial Neural Networks in Engineering, ANNIE. St. Louis, Missouri.

    Google Scholar 

  • Gashash, Y.M.A., Jung, S. and Ghaboussi, J. (2004). Numerical implementation of a neural networks based material model in finite element. International Journal for Numerical Methods in Engineering 59:989–1005.

    Article  Google Scholar 

  • Hashash, Y.M.A., Marulanda, C. Ghaboussi, J. and Jung, S. (2003). Systematic update of a deep excavation model using field performance data”, Computers and Geotechnics 30:477–488.

    Article  Google Scholar 

  • Hashash, Y.M.A., Marulando, C., Ghaboussi, J. and Jung, S. (2006). Novel approach to integration of numerical modeling and field observation for deep excavations. Journal of Geotechnical and Geo-environmental Engineering, ASCE 132:1019–1031.

    Google Scholar 

  • Hashash, Y.M.A., Fu, Q.-W., Ghaboussi, J., Lade, P. V. and Saucier, C. (2009). Inverse analysis based interpretation of sand behavior from triaxial shear tests subjected to full end restraint. Canadian Geotechnical Journal, in press.

    Google Scholar 

  • Joghataie, A., Ghaboussi, J. and Wu, X. (1995). Learning and architecture determination through automatic node generation. Proceedings, International Conference on Artificial neural Networks in Engineering. ANNIE. St Louis

    Google Scholar 

  • Jung. S.-M. and Ghaboussi, J. (2006a). Neural network constitutive model for rate-dependent materials, Computer and Sstructures 84:955–963.

    Article  Google Scholar 

  • Jung. S-M and Ghaboussi, J. (2006b). Characterizing rate-dependent material behaviors in self-learning simulation. Computer Methods in Applied Mechanics and Engineering 196:608–619.

    Article  MATH  Google Scholar 

  • Jung, S.-M., Ghaboussi, J. and Kwon, S.-D. (2004). Estimation of aero-elastic parameters of bridge decks using neural networks. Journal of Engineering Mechanics Division, ASCE 130:1356–1364.

    Article  Google Scholar 

  • Jung S-M, Ghaboussi J. and Marulanda, C. (2007). Field calibration of time-dependent behavior in segmental bridges using self-learning simulations. Engineering Structures 29:2692–2700.

    Article  Google Scholar 

  • Kaklauskas, G. and Ghaboussi, J. (2000). Concrete stress-strain relations from R/C beam tests. Journal of Structural Engineering, ASCE 127, No. 1.

    Google Scholar 

  • Kim, Y.-J. (2000). Active Control of Structures Using Genetic Algorithm. PhD thesis, Dept. of Civil and Environmental Engineering, Univ. of Illinois at Urbana-Champaign. Urbana, Illinois

    Google Scholar 

  • Kim, Y. J. and Ghaboussi, J. (1999). A new method of reduced order feedback control using genetic algorithm. International Journal for Earthquake Engineering and Structural Dynamics 28:193–212.

    Article  Google Scholar 

  • Kwon T.-H (2006) Minimally Invasive Characterization and Intraocular Pressure Measurement via Numerical Simulation of Human Cornea. PhD thesis, Dept. of Civil and Environmental Engineering, Univ. of Illinois at Urbana-Champaign. Urbana, Illinois.

    Google Scholar 

  • Kwon, T.-H. Ghaboussi, J., Pecknold, D. A. and Hashash, Y.M.A. (2008). Effect of cornea stiffness on measured intraocular pressure. Journal of Biomechanics 41:1707–1713.

    Article  Google Scholar 

  • Kwon, T.-H. Ghaboussi, J., Pecknold, D. A. and Hashash, Y.M.A. (2009). Role of cornea biomechanical properties in applanation tonometry measurements. Journal of Refractive Surgery, in press

    Google Scholar 

  • Lin, C.-C. J. (1999). A neural network based methodology for generating spectrum compatible earthquake accelerograms. PhD thesis, Dept. of Civil and Environmental Engineering, Univ. of Illinois at Urbana-Champaign. Urbana, Illinois.

    Google Scholar 

  • Lin C.-C. J. and Ghaboussi, J. (2001). Generating multiple spectrum compatible accelrograms using neural networks. International Journal Earthquake Engineering and Structural Dynamics 30:1021–1042.

    Article  Google Scholar 

  • Limsamphancharon, N. (2003) Condition Monitoring of Structures by Using Ambient Dynamic Responses. PhD thesis, Dept. of Civil and Environmental Engineering, Univ. of Illinois at Urbana-Champaign.Urbana, Illinois.

    Google Scholar 

  • Nikzad, K., Ghaboussi, J. and Paul, S.L. (1996). A study of actuator dynamics and delay compensation using a neuro-controller. Journal of Engineering Mechanics Division, ASCE 122:966–975.

    Article  Google Scholar 

  • Raich, Anne M. (1998). An Evolutionary Based Methodology for Representing and Evolving Structural Design Solutions. PhD thesis, Dept. of Civil and Environmental Engineering, Univ. of Illinois at Urbana-Champaign. Urbana, Illinois.

    Google Scholar 

  • Raich, A. M. and Ghaboussi, J. (1997a). Implicit Representation in Genetic Algorithm Using Redundancy. International Journal of Evolutionary Computing 5, No. 3.

    Google Scholar 

  • Raich, A. M. and Ghaboussi, J. (1997b). Autogenesis and Redundancy in Genetic Algorithm Representation. Proceedings, International Conference on Artificial Neural Networks in Engineering, ANNIE. St. Louis, Missouri.

    Google Scholar 

  • Raich, A.M. and Ghaboussi, J. (1998). Evolving design solutions by transforming the design environment. Proceedings, International Conference on Artificial Neural Networks in Engineering, ANNIE. St. Louis, Missouri.

    Google Scholar 

  • Raich, A. M. and Ghaboussi, J. (2000a). Evolving structural design solutions using an implicit redundant genetic algorithm. Journal of Structural and Multi-disciplinary Optimization 20:222–231.

    Article  Google Scholar 

  • Raich, A. M. and Ghaboussi, J., (2000b). Applying the implicit redundant representation genetic algorithm. In Lance Chambers, ed, Practical Handbook of Genetic Algorithm, vol. 1. Applications, Ch.9 Unstructured Problem Domain. Chapman&Hall/CRC Publishers.

    Google Scholar 

  • Raich, A.M., and Ghaboussi, J. (2001) “Evolving the topology and geometry of frame structures during optimization. Structural Optimization 20:222–231.

    Google Scholar 

  • Sidarta, D.E. and Ghaboussi, J. (1998). Modeling constitutive behavior of materials from non-uniform material tests. International Journal of Computer and Geotechnics 22, No. 1.

    Google Scholar 

  • Shrestha, S.M. (1998). Genetic Algorithm Based Methodology for Unstructured Design of Skeletal Structures. PhD thesis, Dept. of Civil and Environmental Engineering, Univ. of Illinois at Urbana-Champaign. Urbana, Illinois

    Google Scholar 

  • Shrestha, S.M. and Ghaboussi, J. (1997). Genetic algorithm in structural shape design and optimization. Proceedings, 7th International Conference on Computing in Civil and Building Engineering (ICCCBE-VII). Seoul, South Korea

    Google Scholar 

  • Shrestha, S.M. and Ghaboussi, J. (1998). Evolution of optimal structural shapes using genetic algorithm. Journal of Structural Engineering, ASCE. 124:1331–1338.

    Article  Google Scholar 

  • Wu, X. (1991). Neural Network Based Material Modeling. PhD thesis, Dept. of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign. Urbana, Illinois

    Google Scholar 

  • Wu, X. and Ghaboussi, J. (1993). Modeling unloading and cyclic behavior of concrete with adaptive neural networksn. Proceedings, APCOM’93, 2nd Asian-Pacific Conference on Computational Mechanics. Sydney Australia

    Google Scholar 

  • Wu, X., Ghaboussi, J. and Garrett, J.H. (1992). Use of neural networks in detection of structural damage. Computers and Structures, 42:649–660.

    Article  MATH  Google Scholar 

  • Yun, G.-J., (2006) Modeling of Hysteretic Behavior of Beam-column Connections Based on Self-learning Simulations. PhD thesis, Dept. of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois.

    Google Scholar 

  • Yun G, J, Ghaboussi J. and Elnashai, A.S. (2008a). A New Neural Network Based Model for Hysteretic Behavior of Materials. International Journal for Numerical Methods in Engineering 73:447–469.

    Article  MATH  MathSciNet  Google Scholar 

  • Yun G.J, Ghaboussi J. and Elnashai, A.S. (2008b). Self-learning simulation method for inverse nonlinear modeling of cyclic behavior of connections, Computer Methods in Applied Mechanics and Engineering 197:2836–2857.

    Article  Google Scholar 

  • Yun G.J. Ghaboussi J. and Elnashai, A.S. (2008c). A design-based hysteretic model for beam-column connections. International Journal of Earthquake Engineering and Structural Dynamics. 37:535–555.

    Article  Google Scholar 

  • Zhang, M. (1996). Determination of neural network material models from structural tests. PhD thesis, Dept. of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign. Urbana, Illinois.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, University of Illinois at Urbana Champaign, Urbana, Illinois, 61801, USA

    Jamshid Ghaboussi

Authors
  1. Jamshid Ghaboussi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Rzeszow and Cracow Universities of Technology, Poland

    Zenon Waszczyszyn

Rights and permissions

Reprints and permissions

Copyright information

© 2010 CISM, Udine

About this chapter

Cite this chapter

Ghaboussi, J. (2010). Advances in Neural Networks in Computational Mechanics and Engineering. In: Waszczyszyn, Z. (eds) Advances of Soft Computing in Engineering. CISM International Centre for Mechanical Sciences, vol 512. Springer, Vienna. https://doi.org/10.1007/978-3-211-99768-0_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-211-99768-0_4

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-99767-3

  • Online ISBN: 978-3-211-99768-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation