@phdthesis{RadmardRahmani,
  author    = {Radmard Rahmani, Hamid},
  title     = {Artificial Intelligence Approach for Seismic Control of Structures},
  doi       = {10.25643/bauhaus-universitaet.4135},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200417-41359},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  abstract  = {Abstract In the first part of this research, the utilization of tuned mass dampers in the vibration control of tall buildings during earthquake excitations is studied. The main issues such as optimizing the parameters of the dampers and studying the effects of frequency content of the target earthquakes are addressed. Abstract The non-dominated sorting genetic algorithm method is improved by upgrading generic operators, and is utilized to develop a framework for determining the optimum placement and parameters of dampers in tall buildings. A case study is presented in which the optimal placement and properties of dampers are determined for a model of a tall building under different earthquake excitations through computer simulations. Abstract In the second part, a novel framework for the brain learning-based intelligent seismic control of smart structures is developed. In this approach, a deep neural network learns how to improve structural responses during earthquake excitations using feedback control. Abstract Reinforcement learning method is improved and utilized to develop a framework for training the deep neural network as an intelligent controller. The efficiency of the developed framework is examined through two case studies including a single-degree-of-freedom system and a high-rise building under different earthquake excitation records. Abstract The results show that the controller gradually develops an optimum control policy to reduce the vibrations of a structure under an earthquake excitation through a cyclical process of actions and observations. Abstract It is shown that the controller efficiently improves the structural responses under new earthquake excitations for which it was not trained. Moreover, it is shown that the controller has a stable performance under uncertainties.},
  subject      = {Erdbeben},
  language  = {en}
}
@unpublished{RadmardRahmaniKoenke,
  author    = {Radmard Rahmani, Hamid and K{\"o}nke, Carsten},
  title     = {Passive Control of Tall Buildings Using Distributed Multiple Tuned Mass Dampers},
  doi       = {10.25643/bauhaus-universitaet.3859},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20190311-38597},
  pages     = {43},
  abstract  = {The vibration control of the tall building during earthquake excitations is a challenging task due to their complex seismic behavior. This paper investigates the optimum placement and properties of the Tuned Mass Dampers (TMDs) in tall buildings, which are employed to control the vibrations during earthquakes. An algorithm was developed to spend a limited mass either in a single TMD or in multiple TMDs and distribute them optimally over the height of the building. The Non-dominated Sorting Genetic Algorithm (NSGA - II) method was improved by adding multi-variant genetic operators and utilized to simultaneously study the optimum design parameters of the TMDs and the optimum placement. The results showed that under earthquake excitations with noticeable amplitude in higher modes, distributing TMDs over the height of the building is more effective in mitigating the vibrations compared to the use of a single TMD system. From the optimization, it was observed that the locations of the TMDs were related to the stories corresponding to the maximum modal displacements in the lower modes and the stories corresponding to the maximum modal displacements in the modes which were highly activated by the earthquake excitations. It was also noted that the frequency content of the earthquake has significant influence on the optimum location of the TMDs.},
  subject      = {Schwingungsd{\"a}mpfer},
  language  = {en}
}