@phdthesis{Alkam,
  author    = {Alkam, Feras},
  title     = {Vibration-based Monitoring of Concrete Catenary Poles using Bayesian Inference},
  volume    = {2021},
  publisher = {Bauhaus-Universit{\"a}tsverlag},
  address   = {Weimar},
  doi       = {10.25643/bauhaus-universitaet.4433},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210526-44338},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  pages     = {177},
  abstract  = {This work presents a robust status monitoring approach for detecting damage in cantilever structures based on logistic functions. Also, a stochastic damage identification approach based on changes of eigenfrequencies is proposed. The proposed algorithms are verified using catenary poles of electrified railways track. The proposed damage features overcome the limitation of frequency-based damage identification methods available in the literature, which are valid to detect damage in structures to Level 1 only. Changes in eigenfrequencies of cantilever structures are enough to identify possible local damage at Level 3, i.e., to cover damage detection, localization, and quantification. The proposed algorithms identified the damage with relatively small errors, even at a high noise level.},
  subject      = {Parameteridentifikation},
  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}
}