A bi-level heuristic for skew reinforcement in concrete shells subjected to different loading conditions

In structural design, structures are often modeled using the finite elements method (FEM). One of the most common element type is the shell, which is used to model surfaces in three dimensional space as far as the surface thickness is smaller than the other two dimensions. Designers are generally interested in providing a solution that respects all the problem constraints and guarantees structural safety [1], without trying to further improve it as optimization is not trivial even if it could yield a huge benefit both from the economic and the construction point of view. Additionally, saving materials is one of the fundamental criteria for the sustainable approach to the design. In this paper we address the Skew Reinforcement Design in Reinforced Concrete Two Dimensional Elements (SRD2D) under multiple loading conditions. It consists of determining the minimum reinforcement required to respect all the constraints given by the geometric properties and the internal actions working on it, for all the loading conditions that may occur, i.e. for different combinations of internal actions acting on the element. We present a heuristic framework that guides a Genetic Algorithm. Computational results show the efficacy and the effectiveness of the method

[1]  Donald E. Grierson,et al.  Design optimization of 3D reinforced concrete structures , 1996 .

[2]  Gabriele Bertagnoli,et al.  Design and optimization of skew reinforcement in concrete shells , 2012 .

[3]  Gabriele Bertagnoli,et al.  Optimization of concrete shells using genetic algorithms , 2014 .

[4]  Richard J. Balling,et al.  Optimization of Reinforced Concrete Frames , 1997 .

[5]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[6]  Mitsuo Gen,et al.  Genetic algorithms and engineering design , 1997 .

[7]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[8]  Randy L. Haupt,et al.  Practical Genetic Algorithms , 1998 .

[9]  A. Muc,et al.  Genetic algorithms and finite element analysis in optimization of composite structures , 2001 .

[10]  Jan Zeman,et al.  Applying genetic algorithms to selected topics commonly encountered in engineering practice , 2000 .

[11]  Antonio Mancuso,et al.  A genetic algorithm for combined topology and shape optimisations , 2003, Comput. Aided Des..

[12]  Tarek I. Zohdi,et al.  Constrained inverse formulations in random material design , 2003 .

[13]  Guan-Chun Luh,et al.  A binary particle swarm optimization for continuum structural topology optimization , 2011, Appl. Soft Comput..

[14]  N. S. Ottosen A Failure Criterion for Concrete , 1977 .

[15]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[16]  Giuseppe Mancini,et al.  Design of RC membrane elements , 2001 .

[17]  Giuseppe Mancini,et al.  Global safety format for non‐linear analysis of reinforced concrete structures , 2013 .