Nonlinear Design Optimization of Reinforced Concrete Structures Using Genetic Algorithms

Conference paper
Part of the Springer Natural Hazards book series (SPRINGERNAT)

Abstract

The aim of this study is to identify the optimal cross sections and reinforcement area of the beams and the columns in a frame structure in order to improve their seismic response. A six-storey reinforced concrete structure, modelled using fibre finite elements to represent the nonlinear behaviour, was studied. The structure was first designed according to existing codes (the equivalent static load method) and the total quantity of material computed (concrete and reinforcement). Secondly, a genetic algorithm was used in order to determine the optimized beam and column cross section dimensions along with the longitudinal reinforcement area for both types of elements. The optimization process aims to minimize the cost of the reinforced concrete structure while at the same time improving or at least maintaining the seismic response of the structure. A set of constraints was imposed for the optimization process: the minimum reinforcement area, the ratio between the beam height and width and the maximum drift of the structure at both the ultimate and service state, according to design codes. Dynamic time history analysis is used to determine the maximum drift of the structure. The analyses are carried out for three ground motion records which represent the seismic conditions given by the Vrancea source in Bucharest.

Keywords

Structure optimization Seismic design Nonlinear analysis Multi objective 

References

  1. Balling R, Yao X (1997) Optimization of reinforced concrete frames. J Struct Eng-ASCE 2:193–202CrossRefGoogle Scholar
  2. CR0-2011–Cod de proiectare. Bazele proiectarii structurilor in constructiiGoogle Scholar
  3. EN 1998-1:2004—Design of structures for earthquake resistance. Part 1: General rules, seismic actions and rules for buildingsGoogle Scholar
  4. EN 1992-1-1:2006—Design of concrete structures. Part 1-1: General rules and rules for buildingsGoogle Scholar
  5. Fadaee M, Grierson D (1996) Design optimization of 3D reinforced concrete structures. Struct Optim 12(2):127–134CrossRefGoogle Scholar
  6. Fraser A (1957) Simulation of genetic systems by automatic digital computers. I. Introduction. Aust J Biol Sci 10:484–491CrossRefGoogle Scholar
  7. Gharehbaghi S, Khatibinia M (2016) Optimal seismic design of reinforced concrete structures under time history earthquake loads using an intelligent hybrid algorithm—earthquake engineering and engineering vibration. Earthquake Eng Eng Vibr 14:97–109CrossRefGoogle Scholar
  8. Hejazi F, Toloue I, Jaafar MS, Noorzaei J (2013) Optimization of earthquake energy dissipation system by genetic algorithm. Comput Aided Civ Infrastruct Eng 28:796–810Google Scholar
  9. MATLAB and Statistics Toolbox 8.1 (2012) The MathWorks, Inc., Natick, Massachusetts, United StatesGoogle Scholar
  10. OpenSees 2.5.0, Open System for Earthquake Engineering Simulation (2015). University of California, Berkeley, United StatesGoogle Scholar
  11. P100-1/2013—Cod de Proiectare Seismică—Prevederi pentru cladiriGoogle Scholar
  12. Pezeshk S et.al (2002) State of the art on the use of genetic algorithms in design of steel structures, recent advances in optimal structural design. Structural Engineering Institute, ASCEGoogle Scholar
  13. Rajeev S, Krishnamoorthy C (1998) Genetic algorithm-based methodology for design optimization of reinforced concrete frames. Comput Aided Civ Infrastruct Eng 13:63–74CrossRefGoogle Scholar
  14. Seismostruct v7.0.3 (2017) Seismosoft, Earthquake Engineering Software SolutionsGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Technical University of Civil Engineering of BucharestBucharestRomania

Personalised recommendations