@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}
}
@article{SchmidtLahmer,
  author    = {Schmidt, Albrecht and Lahmer, Tom},
  title     = {Efficient domain decomposition based reliability analysis for polymorphic uncertain material parameters},
  series = {Proceedings in Applied Mathematics \& Mechanics},
  volume    = {2021},
  journal   = {Proceedings in Applied Mathematics \& Mechanics},
  number    = {Volume 21, issue 1},
  publisher = {Wiley-VHC},
  address   = {Weinheim},
  doi       = {10.1002/pamm.202100014},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20220112-45563},
  pages     = {1 -- 4},
  abstract  = {Realistic uncertainty description incorporating aleatoric and epistemic uncertainties can be described within the framework of polymorphic uncertainty, which is computationally demanding. Utilizing a domain decomposition approach for random field based uncertainty models the proposed level-based sampling method can reduce these computational costs significantly and shows good agreement with a standard sampling technique. While 2-level configurations tend to get unstable with decreasing sampling density 3-level setups show encouraging results for the investigated reliability analysis of a structural unit square.},
  subject      = {Polymorphie},
  language  = {en}
}
@article{AlkamLahmer,
  author    = {Alkam, Feras and Lahmer, Tom},
  title     = {Eigenfrequency-Based Bayesian Approach for Damage Identification in Catenary Poles},
  series = {Infrastructures},
  volume    = {2021},
  journal   = {Infrastructures},
  number    = {Volume 6, issue 4, article 57},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/infrastructures6040057},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210510-44256},
  pages     = {1 -- 19},
  abstract  = {This study proposes an efficient Bayesian, frequency-based damage identification approach to identify damages in cantilever structures with an acceptable error rate, even at high noise levels. The catenary poles of electric high-speed train systems were selected as a realistic case study to cover the objectives of this study. Compared to other frequency-based damage detection approaches described in the literature, the proposed approach is efficiently able to detect damages in cantilever structures to higher levels of damage detection, namely identifying both the damage location and severity using a low-cost structural health monitoring (SHM) system with a limited number of sensors; for example, accelerometers. The integration of Bayesian inference, as a stochastic framework, in the proposed approach, makes it possible to utilize the benefit of data fusion in merging the informative data from multiple damage features, which increases the quality and accuracy of the results. The findings provide the decision-maker with the information required to manage the maintenance, repair, or replacement procedures.},
  subject      = {Fahrleitung},
  language  = {en}
}