@article{HarirchianKumariJadhavetal.,
  author    = {Harirchian, Ehsan and Kumari, Vandana and Jadhav, Kirti and Raj Das, Rohan and Rasulzade, Shahla and Lahmer, Tom},
  title     = {A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings},
  series = {Applied Sciences},
  volume    = {2020},
  journal   = {Applied Sciences},
  number    = {Volume 10, issue 20, article 7153},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/app10207153},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20201022-42744},
  pages     = {18},
  abstract  = {Although averting a seismic disturbance and its physical, social, and economic disruption is practically impossible, using the advancements in computational science and numerical modeling shall equip humanity to predict its severity, understand the outcomes, and equip for post-disaster management. Many buildings exist amidst the developed metropolitan areas, which are senile and still in service. These buildings were also designed before establishing national seismic codes or without the introduction of construction regulations. In that case, risk reduction is significant for developing alternatives and designing suitable models to enhance the existing structure's performance. Such models will be able to classify risks and casualties related to possible earthquakes through emergency preparation. Thus, it is crucial to recognize structures that are susceptible to earthquake vibrations and need to be prioritized for retrofitting. However, each building's behavior under seismic actions cannot be studied through performing structural analysis, as it might be unrealistic because of the rigorous computations, long period, and substantial expenditure. Therefore, it calls for a simple, reliable, and accurate process known as Rapid Visual Screening (RVS), which serves as a primary screening platform, including an optimum number of seismic parameters and predetermined performance damage conditions for structures. In this study, the damage classification technique was studied, and the efficacy of the Machine Learning (ML) method in damage prediction via a Support Vector Machine (SVM) model was explored. The ML model is trained and tested separately on damage data from four different earthquakes, namely Ecuador, Haiti, Nepal, and South Korea. Each dataset consists of varying numbers of input data and eight performance modifiers. Based on the study and the results, the ML model using SVM classifies the given input data into the belonging classes and accomplishes the performance on hazard safety evaluation of buildings.},
  subject      = {Erdbeben},
  language  = {en}
}
@phdthesis{Weitzmann2009,
  author    = {Weitzmann, R{\"u}diger},
  title     = {Theory and application of optimization strategies for the design of seismically excited structures},
  doi       = {10.25643/bauhaus-universitaet.1406},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20091030-14917},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  year      = {2009},
  abstract  = {The study introduces into the theory and application of optimization strategies in earthquake engineering. The optimization algorithm substitutes the intuitive solution of practical problems done by the engineer in daily practice, providing automatic design tools and numerical means for further exploration of the design space for various extremum states. This requires a mathematical formulation of the design task, that is provided for typical seismic evaluations within this document. Utilizing the natural relation between design and optimization tasks, appropriate mechanical concepts are developed and discussed. The explanations start with an overview on the mechanical background for continua. Hereby the focus is placed on elasto-plastic structures. The given extremum formulations are treated with help of discretization methods in order to obtain optimization problems. These basics are utilized for derivation of programs for eigenvalue and stability analysis, that are applied in simplified linear analysis for the design of seismically excited structures. Another focus is set on the application in simplified nonlinear design, that uses limit state analyses on the basis of nonlinear problem formulations. Well known concepts as the response and pushover analysis are covered as well as alternative strategies on the basis of shakedown theory or cycle and deformation based evaluations. Furthermore, the study gives insight into the application of optimization problems in conjunction with nonlinear time history analyses. The solution of step-by-step procedures within optimization algorithms is shown and aspects of dynamic limit state analyses are discussed. For illustration of the great variety of optimization-based concepts in earthquake engineering, several specialized applications are presented, e.g. the generation of artificial ground motions and the determination of reduction coefficients for design spectrum reduction due to viscous and hysteretic damping. As well alternative strategies for the design of base isolated structures with controlled impact are presented. All presented applications are illustrated with help of various examples.},
  subject      = {Dynamik},
  language  = {en}
}