@article{HarirchianKumariJadhavetal.,
  author    = {Harirchian, Ehsan and Kumari, Vandana and Jadhav, Kirti and Rasulzade, Shahla and Lahmer, Tom and Raj Das, Rohan},
  title     = {A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings},
  series = {Applied Sciences},
  volume    = {2021},
  journal   = {Applied Sciences},
  number    = {Volume 11, issue 16, article 7540},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/app11167540},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210818-44853},
  pages     = {1 -- 33},
  abstract  = {A vast number of existing buildings were constructed before the development and enforcement of seismic design codes, which run into the risk of being severely damaged under the action of seismic excitations. This poses not only a threat to the life of people but also affects the socio-economic stability in the affected area. Therefore, it is necessary to assess such buildings' present vulnerability to make an educated decision regarding risk mitigation by seismic strengthening techniques such as retrofitting. However, it is economically and timely manner not feasible to inspect, repair, and augment every old building on an urban scale. As a result, a reliable rapid screening methods, namely Rapid Visual Screening (RVS), have garnered increasing interest among researchers and decision-makers alike. In this study, the effectiveness of five different Machine Learning (ML) techniques in vulnerability prediction applications have been investigated. The damage data of four different earthquakes from Ecuador, Haiti, Nepal, and South Korea, have been utilized to train and test the developed models. Eight performance modifiers have been implemented as variables with a supervised ML. The investigations on this paper illustrate that the assessed vulnerability classes by ML techniques were very close to the actual damage levels observed in the buildings.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@article{AlkamLahmer,
  author    = {Alkam, Feras and Lahmer, Tom},
  title     = {A robust method of the status monitoring of catenary poles installed along high-speed electrified train tracks},
  series = {Results in Engineering},
  volume    = {2021},
  journal   = {Results in Engineering},
  number    = {volume 12, article 100289},
  publisher = {Elsevier},
  address   = {Amsterdam},
  doi       = {10.1016/j.rineng.2021.100289},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20211011-45212},
  pages     = {1 -- 8},
  abstract  = {Electric trains are considered one of the most eco-friendly and safest means of transportation. Catenary poles are used worldwide to support overhead power lines for electric trains. The performance of the catenary poles has an extensive influence on the integrity of the train systems and, consequently, the connected human services. It became a must nowadays to develop SHM systems that provide the instantaneous status of catenary poles in- service, making the decision-making processes to keep or repair the damaged poles more feasible. This study develops a data-driven, model-free approach for status monitoring of cantilever structures, focusing on pre-stressed, spun-cast ultrahigh-strength concrete catenary poles installed along high-speed train tracks. The pro-posed approach evaluates multiple damage features in an unfied damage index, which leads to straightforward interpretation and comparison of the output. Besides, it distinguishes between multiple damage scenarios of the poles, either the ones caused by material degradation of the concrete or by the cracks that can be propagated during the life span of the given structure. Moreover, using a logistic function to classify the integrity of structure avoids the expensive learning step in the existing damage detection approaches, namely, using the modern machine and deep learning methods. The findings of this study look very promising when applied to other types of cantilever structures, such as the poles that support the power transmission lines, antenna masts, chimneys, and wind turbines.},
  subject      = {Fahrleitung},
  language  = {en}
}