• Title/Summary/Keyword: Genetic Algorism

Search Result 12, Processing Time 0.017 seconds

Generic optimization, energy analysis, and seismic response study for MSCSS with rubber bearings

  • Fan, Buqiao;Zhang, Xun'an;Abdulhadi, Mustapha;Wang, Zhihao
    • Earthquakes and Structures
    • /
    • v.19 no.5
    • /
    • pp.347-359
    • /
    • 2020
  • The Mega-Sub Controlled Structure System (MSCSS), an innovative vibration passive control system for building structures, is improved by adding lead rubber bearings (LRBs) on top of the substructure. For the new system, a genetic algorithm is used to optimize the dynamic parameters and distributions of dampers and LRBs. The program uses various seismic performance indicators as optimization objectives, and corresponding results are compared. It is found that the optimization procedure for maximizing the energy dissipation ratio yields the best solutions, and optimized models have consistent seismic performances under different earthquakes. Seismic performances of optimized MSCSS models with and without LRBs, as well as the traditional Mega-Sub Structure model, are evaluated and compared under El Centro wave, Taft wave and 20 other artificial waves. In both elastic and plastic analysis, the model with LRBs shows significantly smaller story drift and horizontal acceleration than those of the other two models, and fewer plastic hinges are developed during severe earthquakes. Energy analysis also shows that LRBs installed in proper locations increase the deformation and energy dissipation of dampers, thereby significantly reduce the kinetic, potential, and hysteretic energy in the structure. However, LRBs do not have to be mounted on all the additional columns. It is also demonstrated that LRBs at unfavorable locations can decrease the energy dissipation for dampers. After LRBs are installed, the optimal damping coefficient and the optimal damping exponent of dampers are reduced to produce the best damping effect.

Multi-objective Genetic Algorism Model for Determining an Optimal Capital Structure of Privately-Financed Infrastructure Projects (민간투자사업의 최적 자본구조 결정을 위한 다목적 유전자 알고리즘 모델에 관한 연구)

  • Yun, Sungmin;Han, Seung Heon;Kim, Du Yon
    • KSCE Journal of Civil and Environmental Engineering Research
    • /
    • v.28 no.1D
    • /
    • pp.107-117
    • /
    • 2008
  • Private financing is playing an increasing role in public infrastructure construction projects worldwide. However, private investors/operators are exposed to the financial risk of low profitability due to the inaccurate estimation of facility demand, operation income, maintenance costs, etc. From the operator's perspective, a sound and thorough financial feasibility study is required to establish the appropriate capital structure of a project. Operators tend to reduce the equity amount to minimize the level of risk exposure, while creditors persist to raise it, in an attempt to secure a sufficient level of financial involvement from the operators. Therefore, it is important for creditors and operators to reach an agreement for a balanced capital structure that synthetically considers both profitability and repayment capacity. This paper presents an optimal capital structure model for successful private infrastructure investment. This model finds the optimized point where the profitability is balanced with the repayment capacity, with the use of the concept of utility function and multi-objective GA (Generic Algorithm)-based optimization. A case study is presented to show the validity of the model and its verification. The research conclusions provide a proper capital structure for privately-financed infrastructure projects through a proposed multi-objective model.