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Design of prestressed concrete flat slab using modern heuristic optimization techniques

机译:应用现代启发式优化技术设计预应力混凝土平板

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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.
机译:当前研究的主要目的是利用现代启发式搜索算法的强大功能来优化预应力混凝土板的结构设计。提供了一个通用,灵活且相对易于使用的工具,供实践工程师使用。为此,开发了一种强大的数字工具,集成了设计,分析和优化技术。该工具利用有限元方法对系统进行结构分析。找到板坯厚度,钢筋束的数量和大小以及钢筋束轮廓的最佳值时要遵守相关实践准则施加的设计约束。目标函数包括混凝土和预应力筋的成本。尽管目标函数简单且单调,但由于某些变量的离散性以及约束的复杂性和非线性,优化问题仍然面临着挑战。作为演示问题,寻求方形预应力平板的最佳设计。考虑了直接搜索方法,启发式优化技术(例如遗传算法)和多目标优化技术。结果表明,搜索应沿约束边界进行。建议将设计变量分为两组,并定义代表距离约束的距离的第二个目标函数。应该同时针对两个目标函数(成本和距离)进行优化。使用建议的过程,可以更有效地找到最佳设计。

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