The effect of the consideration of slab dimensions on optimum design of reinforced concrete beams

Yıl 2019, Cilt: 3 Sayı: 2, 81 - 85, 10.10.2019

Sinan Melih Nigdeli Gebrail Bekdaş

Öz

In the design of reinforced concrete (RC) beams, the slab
can be also considered as a part of the beam and a t-shaped cross section is
considered. In the presented study, the optimum design of RC beams are
investigated for different slab thickness values. Thus, the effect of the
consideration of slab dimensions for the optimum design is investigated. In the
optimization methodology, an iterative cost optimization process is proposed.
The process contains the optimization of design variables such as the
cross-section dimensions and amount of rebar of RC beams subjected to flexural
moments. In order to find a precise optimum solution without trapping local
optimums, a metaheuristic based method called harmony search is employed. The
optimum values are chosen according to user selected range and the design
constraints. The design constraints are generated according to ACI318- Building
code requirements for structural concrete. By the increase of compressive force
in the compressive section of the beam, the amount of the rebar shows a
decreasing manner and this situation is effective on the optimum design and
cost.

Anahtar Kelimeler

Metaheuristic algorithm, Frames, Optimization, Teaching learning based optimization

Kaynakça

• [1]. Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: harmony search. Simulation, 76(2), 60-68.
• [2]. ACI 318M-05, Building code requirements for structural concrete and commentary, American Concrete Institute, 2005.
• [3]. Goldberg, D.E. (1989), Genetic algorithms in search, Optimization and machine learning, Boston MA: Addison Wesley.
• [4]. Dorigo, M., Maniezzo, V. and Colorni A (1996), “The ant system: Optimization by a colony of cooperating agents”, IEEE Transactions on Systems Man and Cybernet B, 26, 29–41.
• [5]. Koumousis, V. K., Arsenis, S. J. (1998), “Genetic Algorithms in Optimal Detailed Design of Reinforced Concrete Members”, Comput-Aided Civ. Inf., 13, 43-52.
• [6]. Govindaraj, V., Ramasamy, J. V. (2005), “Optimum detailed design of reinforced concrete continuous beams using Genetic Algorithms”, Comput. Struct., 84, 34–48.
• [7]. Bekdaş, G. and Nigdeli, S.M. (2012), “Cost optimization of T-shaped reinforced concrete beams under flexural effect according to ACI 318”, In: 3rd European Conference of Civil Engineering, Paris, France.
• [8]. Nigdeli, S. M., & Bekdaş, G. (2016). Optimum design of RC continuous beams considering unfavourable live-load distributions. KSCE Journal of Civil Engineering, DOI: 10.1007/s12205-016-2045-5.
• [9]. Rafiq, M. Y., Southcombe, C. (1998), “Genetic algorithms in optimal design and detailing of reinforced concrete biaxial columns supported by a declarative approach for capacity checking”, Comput. Struct. 69, 443-457.
• [10]. Nigdeli, S. M., Bekdas, G., Kim, S., & Geem, Z. W. (2015). A novel harmony search based optimization of reinforced concrete biaxially loaded columns.Structural Engineering and Mechanics, 54(6), 1097-1109.
• [11]. Camp, C. V., Pezeshk, S., Hansson, H. (2003), “Flexural Design of Reinforced Concrete Frames Using a Genetic Algorithm”, J Struct. Eng.-ASCE., 129, 105-11.
• [12]. Govindaraj, V., Ramasamy, J. V. (2007), “Optimum detailed design of reinforced concrete frames using genetic algorithms”, Eng. Optimiz., 39(4), 471–494.
• [13]. Camp, C.V. and Akin, A. (2012), “Design of retaining walls using big bang–big crunch optimization”, Journal of Structural Engineering, Vol.138, No.3, pp.438–448, DOI:10.1061/(ASCE)ST.1943-541X.0000461.
• [14]. Temur, R, Bekdaş, G. (2016), “Teaching learning-based optimization for design of cantilever retaining walls”, Structural Engineering and Mechanics, Vol.57, No.4, pp.763-783, DOI: 10.12989/sem.2016.57.4.763.