@article{ArnoldKraus,
  author    = {Arnold, Robert and Kraus, Matthias},
  title     = {On the nonstationary identification of climate-influenced loads for the semi-probabilistic approach using measured and projected data},
  series = {Cogent Engineering},
  volume    = {2022},
  journal   = {Cogent Engineering},
  number    = {Volume 9, issue 1, article 2143061},
  editor    = {Pham, Duc},
  publisher = {Taylor \& Francis},
  address   = {London},
  doi       = {10.1080/23311916.2022.2143061},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20221117-47363},
  pages     = {1 -- 26},
  abstract  = {A safe and economic structural design based on the semi-probabilistic concept requires statistically representative safety elements, such as characteristic values, design values, and partial safety factors. Regarding climate loads, the safety levels of current design codes strongly reflect experiences based on former measurements and investigations assuming stationary conditions, i.e. involving constant frequencies and intensities. However, due to climate change, occurrence of corresponding extreme weather events is expected to alter in the future influencing the reliability and safety of structures and their components. Based on established approaches, a systematically refined data-driven methodology for the determination of design parameters considering nonstationarity as well as standardized targets of structural reliability or safety, respectively, is therefore proposed. The presented procedure picks up fundamentals of European standardization and extends them with respect to nonstationarity by applying a shifting time window method. Taking projected snow loads into account, the application of the method is exemplarily demonstrated and various influencing parameters are discussed.},
  subject      = {Reliabilit{\"a}t},
  language  = {en}
}
@article{ChowdhuryKraus,
  author    = {Chowdhury, Sharmistha and Kraus, Matthias},
  title     = {Design-related reassessment of structures integrating Bayesian updating of model safety factors},
  series = {Results in Engineering},
  volume    = {2022},
  journal   = {Results in Engineering},
  number    = {Volume 16, article 100560},
  publisher = {Elsevier},
  address   = {Amsterdam},
  doi       = {10.1016/j.rineng.2022.100560},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20221028-47294},
  pages     = {1 -- 1},
  abstract  = {In the semi-probabilistic approach of structural design, the partial safety factors are defined by considering some degree of uncertainties to actions and resistance, associated with the parameters' stochastic nature. However, uncertainties for individual structures can be better examined by incorporating measurement data provided by sensors from an installed health monitoring scheme. In this context, the current study proposes an approach to revise the partial safety factor for existing structures on the action side, γE by integrating Bayesian model updating. A simple numerical example of a beam-like structure with artificially generated measurement data is used such that the influence of different sensor setups and data uncertainties on revising the safety factors can be investigated. It is revealed that the health monitoring system can reassess the current capacity reserve of the structure by updating the design safety factors, resulting in a better life cycle assessment of structures. The outcome is furthermore verified by analysing a real life small railway steel bridge ensuring the applicability of the proposed method to practical applications.},
  subject      = {Lebenszyklus},
  language  = {en}
}