@article{HarirchianLahmer,
  author    = {Harirchian, Ehsan and Lahmer, Tom},
  title     = {Improved Rapid Visual Earthquake Hazard Safety Evaluation of Existing Buildings Using a Type-2 Fuzzy Logic Model},
  series = {Applied Sciences},
  volume    = {2020},
  journal   = {Applied Sciences},
  number    = {Volume 10, Issue 3, 2375},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/app10072375},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200331-41161},
  pages     = {14},
  abstract  = {Rapid Visual Screening (RVS) is a procedure that estimates structural scores for buildings and prioritizes their retrofit and upgrade requirements. Despite the speed and simplicity of RVS, many of the collected parameters are non-commensurable and include subjectivity due to visual observations. This might cause uncertainties in the evaluation, which emphasizes the use of a fuzzy-based method. This study aims to propose a novel RVS methodology based on the interval type-2 fuzzy logic system (IT2FLS) to set the priority of vulnerable building to undergo detailed assessment while covering uncertainties and minimizing their effects during evaluation. The proposed method estimates the vulnerability of a building, in terms of Damage Index, considering the number of stories, age of building, plan irregularity, vertical irregularity, building quality, and peak ground velocity, as inputs with a single output variable. Applicability of the proposed method has been investigated using a post-earthquake damage database of reinforced concrete buildings from the Bing{\"o}l and D{\"u}zce earthquakes in Turkey.},
  subject      = {Fuzzy-Logik},
  language  = {en}
}
@article{HarirchianLahmerRasulzade,
  author    = {Harirchian, Ehsan and Lahmer, Tom and Rasulzade, Shahla},
  title     = {Earthquake Hazard Safety Assessment of Existing Buildings Using Optimized Multi-Layer Perceptron Neural Network},
  series = {Energies},
  volume    = {2020},
  journal   = {Energies},
  number    = {Volume 13, Issue 8, 2060},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/en13082060},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200504-41575},
  pages     = {16},
  abstract  = {The latest earthquakes have proven that several existing buildings, particularly in developing countries, are not secured from damages of earthquake. A variety of statistical and machine-learning approaches have been proposed to identify vulnerable buildings for the prioritization of retrofitting. The present work aims to investigate earthquake susceptibility through the combination of six building performance variables that can be used to obtain an optimal prediction of the damage state of reinforced concrete buildings using artificial neural network (ANN). In this regard, a multi-layer perceptron network is trained and optimized using a database of 484 damaged buildings from the D{\"u}zce earthquake in Turkey. The results demonstrate the feasibility and effectiveness of the selected ANN approach to classify concrete structural damage that can be used as a preliminary assessment technique to identify vulnerable buildings in disaster risk-management programs.},
  subject      = {Erdbeben},
  language  = {en}
}