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Institute
- Fachbereich Energietechnik (242) (remove)
Monte Carlo Tree Search (MCTS) is a search technique that in the last decade emerged as a major breakthrough for Artificial Intelligence applications regarding board- and video-games. In 2016, AlphaGo, an MCTS-based software agent, outperformed the human world champion of the board game Go. This game was for long considered almost infeasible for machines, due to its immense search space and the need for a long-term strategy. Since this historical success, MCTS is considered as an effective new approach for many other scientific and technical problems. Interestingly, civil structural engineering, as a discipline, offers many tasks whose solution may benefit from intelligent search and in particular from adopting MCTS as a search tool. In this work, we show how MCTS can be adapted to search for suitable solutions of a structural engineering design problem. The problem consists of choosing the load-bearing elements in a reference reinforced concrete structure, so to achieve a set of specific dynamic characteristics. In the paper, we report the results obtained by applying both a plain and a hybrid version of single-agent MCTS. The hybrid approach consists of an integration of both MCTS and classic Genetic Algorithm (GA), the latter also serving as a term of comparison for the results. The study’s outcomes may open new perspectives for the adoption of MCTS as a design tool for civil engineers.
Multi-dimensional fragility analysis of a RC building with components using response surface method
(2017)
Conventional fragility curves describe the vulnerability of the main structure under external hazards. However, in complex structures such as nuclear power plants, the safety or the risk depends also on the components associated with a system. The classical fault tree analysis gives an overall view of the failure and contains several subsystems to the main event, however, the interactions in the subsystems are not well represented. In order to represent the interaction of the components, a method suggested by Cimellaro et al. (2006) using multidimensional performance limit state functions to obtain the system fragility curves is adopted. This approach gives the possibility of deriving the cumulative fragility taking into account the interaction of the response of different components. In this paper, this approach is used to evaluate seismic risk of a representative electrical building infrastructure, including the component, of a nuclear power plant. A simplified model of the structure, with nonlinear material behavior is employed for the analysis in Abaqus©. The input variables considered are the material parameters, boundary conditions and the seismic input. The variability of the seismic input is obtained from selected ground motion time histories of spectrum compatible synthetic ccelerograms. Unlike the usual Monte Carlo methods used for the probabilistic analysis of the structure, a computationally effective response surface method is used. This method reduces the computational effort of the calculations by reducing the required
number of samples.