Design of prestressed concrete flat slab using modern heuristic optimization techniques

  • Authors:

  • M. El Semelawy;A. O. Nassef;A. A. El Damatty

  • Affiliations:

  • Department of Civil and Environmental Engineering, University of Western Ontario, London, Ontario, Canada N6A 5B9;Mechanical Engineering Department, American University in Cairo, Egypt;Department of Civil and Environmental Engineering, University of Western Ontario, London, Ontario, Canada N6A 5B9

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2012

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Abstract

The main objective of the current study is to utilize the huge capabilities of modern heuristic search algorithms for structural design optimization of pre-stressed concrete slab; providing a general, flexible, and relatively easy to use tool for practicing engineers. A robust numerical tool integrating design, analysis, and optimization techniques is developed for this purpose. The tool utilizes the Finite Element method for the structural analysis of the system. Optimum values for the slab thickness, number and size of tendons, and tendon profile are found subject to design constraints imposed by the relevant code of practice. The objective function incorporates the cost of both concrete and prestressing tendons. Although the objective function is simple and monotonic, the optimization problem is quiet challenging due to the complexity and nonlinearity of the constraints in addition to the discreteness of some of the variables. As a demonstration problem, the optimum design of a square prestressed flat slab is sought. Direct search methods, heuristic optimization techniques such as Genetic Algorithms, and multi-objective optimization techniques are considered. Results indicated that search should be conducted along a constraint boundary. It is suggested to divide the design variables into two groups and to define a second objective function representing the distance away from the constraint. Optimization should be carried out for both objective functions (cost and distance) simultaneously. Using the suggested procedure, optimum design is found more efficiently.