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Constraint logic programming for the analysis and partial synthesis of truss structures

Published online by Cambridge University Press:  27 February 2009

Sivand Lakmazaheri
Affiliation:
Department of Civil Engineering, North Carolina State University, Raleigh, NC 27695, U.S.A.
William J. Rasdorf
Affiliation:
Department of Civil Engineering, North Carolina State University, Raleigh, NC 27695, U.S.A.

Abstract

A general constraint-based formulation for the analysis and partial synthesis of two-dimensional truss structures is presented. This formulation is general in that it handles statically determinate and statically indeterminate trusses with pin and roller supports, and concentrated joint loads. The formulation is constraint-based in that the physical behavior of truss components is declaratively represented using constraints.

The analysis and partial synthesis of a truss structure manifest themselves in proving the satisfiability of the constraints associated with the structural components. An artificial intelligence approach called constraint logic programming is used for representing and satisfying constraints. A constraint logic programming language, called CLP(R), is used for implementing the formulation. The implemented program consists of sixteen rules. These rules are used for both the analysis and partial synthesis of truss structures. Several truss analysis and synthesis examples using the formulation are presented.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1989

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References

Aho, A. V., Hopcroft, J. E. and Ullman, J. D. 1983. Data Structures and Algorithms. Reading, MA: Addison-Wesley.Google Scholar
Borning, A. 1979 ThingLab—A Constraint-Oriented Simulation Laboratory. Xerox PARC Technical Report SSL-79–3, Palo Alto, California.Google Scholar
Bratko, I. 1986 PROLOG Programming for Artificial Intelligence. Reading, MA: Addison-Wesley.Google Scholar
Chan, W. T. 1986 Logic Programming for Managing Constraint-Based Engineering Design. Ph.D. Thesis, Stanford University.Google Scholar
Chan, W. T. and Paulson, B. C. Jr 1987. Exploratory design using constraints. (AIEDAM) 1, 5971.CrossRefGoogle Scholar
Duffy, A. 1987 Bibliography—artificial intelligence in design. Artificial Intelligence in Engineering, 3, 171179.Google Scholar
Ervin, S. M. and Gross, M. D. 1987 RoadLab—a constraint based laboratory for road design. Artificial Intelligence in Engineering, 2, 224234.CrossRefGoogle Scholar
Fenves, S. J. and Rasdorf, W. J. 1985 Treatment of engineering design constraints in relational databases. Engineering with Computers, 20, 2737.CrossRefGoogle Scholar
Harland, J. A. and Michaylov, S. 1986 Implementing an ODE Solver: A CLP Approach. Technical Report Number 7, Computer Science Department, Monash University.Google Scholar
Heintze, N. C., Michaylov, S. and Stuckey, P. J. 1986. CLP(R) and some problems in electrical engineering. In: Lassez, J-L., Ed. Proceedings of the Fourth International Conference in Logic Programming. Cambridge, MA: MIT Press, pp 675703.Google Scholar
Hubka, V. and Eder, W. E. 1987. A scientific approach to engineering design. Design Studies, 8 123137.CrossRefGoogle Scholar
Jaffar, J. and Michaylov, S. 1986 Methodology and implementation of a CLP system. In: Lassez, J. L., Ed. Proceedings of the Fourth International Conference on Logic Programming. Cambridge, MA: MIT Press, pp. 196218.Google Scholar
Jaffar, J. and Lassez, J-.L. 1987 a. Constraint logic programming. Proceedings of the Fourteenth (Association for Computing Machinary) Symposium on Principles of Programming Languages, pp. 111119.Google Scholar
Jaffar, J. and Lassez, J.-L. 1987 b. From unification to constraints. In: Furukawa, H., Tanaka, H. and Fujisaki, T., Eds. Proceedings of the Sixth Conference on Logic Programming. Berlin: Springer-Verlag, pp. 118.Google Scholar
Jaffar, J., Lassez, J-L. and Maher, M. J. 1986 A logic programming language scheme. In: DeGroot, D. and Lindstrom, G., Eds. Logic Programming: Functions, Relations, and Equations. Englewood Cliffs, NJ: Prentice Hall, pp. 441467.Google Scholar
Kowalski, R. 1985 The relation between logic programming and logic specification. In: Hoare, C. A. R. and Shepherdson, J. C., Eds. Mathematical Logic and Programming Languages. Englewood Cliffs, NJ: Prentice Hall, pp. 1127.Google Scholar
Lakmazaheri, S. and Rasdorf, W. J. (in press). The analysis and partial synthesis of truss structures via theorem proving. Engineering with Computers.Google Scholar
Lassez, C., McAloon, K. and Yap, R. 1987. Constraint logic programming and option trading. IEEE Expert, 2, (3), 4250.CrossRefGoogle Scholar
Leler, W. (1988). Constraint Programming Languages—Their Specification and Generation. Reading, MA: Addison-Wesley.Google Scholar
Lloyd, J. W. 1987. Foundations of Logic Programming, 2nd Ed. Berlin: Springer-Verlag.CrossRefGoogle Scholar
Maher, M. L. 1985. HI-RISE: A Knowledge-Based Expert System for the Preliminary Structural Design of High Rise Buildings. Report Number R-85–146, Department of Civil Engineering, Carnegie-Mellon University.Google Scholar
Rasdorf, W. J. and Wang, T. E. 1986. CDIS: an engineering constraint definition and integrity enforcement system for relational databases. Proceedings of the International Computers in Engineering Conference, Vol. 2. American Society of Mechanial Engineers, pp. 273280.Google Scholar
Rasdorf, W. J., Ulberg, K. J. and Baugh, J. W. 1987. A structure-based model of semantic integrity constraints for relational databases. Engineering with Computers, 2, 3139.CrossRefGoogle Scholar
Reddy, U. S. 1986. On the relationship between logic and functional languages. In: DeGroot, D. and Lindstrom, G., Eds Logic Programming-Functions, Relations, and Equations: Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Robinson, R. A. 1965. A machine oriented logic based on resolution principle. Journal of Association for Computing Machinery, 12, 2341.CrossRefGoogle Scholar
Salvadori, M. G. and Baron, M. L. 1961. Numerical Methods in Engineering. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Serrano, D. and Gossard, D. C. 1986 Combining mathematical models with geometric models in CAE systems. Proceedings of the 1986 ASME International Computers in Engineering Conference, 277284.Google Scholar
Serrano, D. and Gossard, D. C. 1987 Constraint management in conceptual design. In: Sriram, D., and Adey, R. A., Eds. Knowledge Based Expert Systems in Engineering: Planning and Design. Computational Mechanics, pp. 211224.Google Scholar
Southwell, R. V. 1940. Relaxation Methods in Engineering Science: A Treatise on Approximate Computation. Oxford University Press.Google Scholar
Sriram, D. 1987. ALL-RISE: a case study in constraint-based design. Artificial Intelligence in Engineering, 2, 186203.CrossRefGoogle Scholar
Sriram, D. and Maher, M. L. 1986. The representation and use of constraints in structural design. In: Sriram, D. and Adey, R., Eds., Applications of Artificial Intelligence in Engineering Problems, Vol. 1. Berlin: Springer-Verlag, pp. 355368.Google Scholar
Sussman, G. J. and Steele, G. L. 1980. Constraints—a language for expressing almost-hierarchical descriptions. Artificial Intelligence, 14, 139.CrossRefGoogle Scholar
Sutherland, I. 1963. SketchPad: A Man-Machine Graphical Communication System. IFIP Proceedings of the Spring Joint Computer Conference.CrossRefGoogle Scholar