The design of reinforced concrete (RC) beams need special conditions to provide a ductile design. In this design, the maximum amount of tensile reinforcement must be limited to singly reinforced design. After the singly reinforced limit, the cost of doubly reinforced RC beam rapidly increases, and it may not be an optimum design. To consider this nonlinear behavior and other rules used in RC structures according to regulations such as ACI 318: Building code requirements for structural concrete and Eurocode 2: Design of concrete structures, an algorithmic and iteration optimization method is needed. In this chapter, two examples are presented, and optimum results are shared for methodologies employing several metaheuristic algorithms. The importance of using metaheuristic algorithms can be seen in this chapter.
TopIntroduction
For a reinforced concrete (RC) structure, loads on the structures are directed to slabs and the loads on slabs are directed to beams. The loads of beams are directed to columns and then to the foundation of the structure. Finally, all loads are directed to ground. Slab, beam and several parts of foundations are designed according to a member under tensile stresses and ductile behavior is provided by allowing the yielding of reinforcing steel bars (rebar) before fracture of concrete. This ductile behavior can be provided since a portion of the section is generally under tensile stresses. Whereas, this situation cannot be provided since columns are generally under compressive stresses and other rules are needed for structural ductility.
In order to provide ductility condition of beams, the amount of rebar must be limited and thus, moment capacity is also limited. In order to increase moment capacity, rebars in the part of RC sections under compressive stress must be provided.
As explained in Chapter 1, due to existence of design constraint provided in design codes, the optimization of RC member is highly non-linear especially for ductile behavior of RC beams. In that case, several metaheuristic-based optimization methods were proposed for RC beams (Coello, Hernández, & Farrera, 1997; Rafiq & Southcombe, 1998; Govindaraj & Ramasamy, 2005; Akın & Saka, 2010; Fedghouche & Tiliouine, 2012; Bekdaş & Nigdeli, 2013).
In this chapter, two applications of RC beams were given. For the first methodology, design of RC beams was presented according to ACI 318: Building code requirements for structural concrete (2005). The optimum results of several cases were presented according to various metaheuristic algorithms such as flower pollination algorithm (FPA) (Yang, 2012), teaching-learning-based optimization (TLBO) (Rao, Savsani, & Vakharia, 2011) and Jaya algorithm (JA) (Rao, 2016).
As the second methodology, optimum design of T-beams using JA was presented by considering design constraints formulated according to Eurocode 2: Design of concrete structures (2005). Also, Matlab (2018) code for optimization of the numerical example is presented as Appendix 1 with comment lines.