@article{KumariHarirchianLahmeretal.,
  author    = {Kumari, Vandana and Harirchian, Ehsan and Lahmer, Tom and Rasulzade, Shahla},
  title     = {Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings},
  series = {Buildings},
  volume    = {2022},
  journal   = {Buildings},
  number    = {Volume 12, issue 5, article 578},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/buildings12050578},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20220509-46387},
  pages     = {1 -- 23},
  abstract  = {The seismic vulnerability assessment of existing reinforced concrete (RC) buildings is a significant source of disaster mitigation plans and rescue services. Different countries evolved various Rapid Visual Screening (RVS) techniques and methodologies to deal with the devastating consequences of earthquakes on the structural characteristics of buildings and human casualties. Artificial intelligence (AI) methods, such as machine learning (ML) algorithm-based methods, are increasingly used in various scientific and technical applications. The investigation toward using these techniques in civil engineering applications has shown encouraging results and reduced human intervention, including uncertainties and biased judgment. In this study, several known non-parametric algorithms are investigated toward RVS using a dataset employing different earthquakes. Moreover, the methodology encourages the possibility of examining the buildings' vulnerability based on the factors related to the buildings' importance and exposure. In addition, a web-based application built on Django is introduced. The interface is designed with the idea to ease the seismic vulnerability investigation in real-time. The concept was validated using two case studies, and the achieved results showed the proposed approach's potential efficiency},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@article{AlaladeReichertKoehnetal.,
  author    = {Alalade, Muyiwa and Reichert, Ina and K{\"o}hn, Daniel and Wuttke, Frank and Lahmer, Tom},
  title     = {A Cyclic Multi-Stage Implementation of the Full-Waveform Inversion for the Identification of Anomalies in Dams},
  series = {Infrastructures},
  volume    = {2022},
  journal   = {Infrastructures},
  number    = {Volume 7, issue 12, article 161},
  editor    = {Qu, Chunxu and Gao, Chunxu and Zhang, Rui and Jia, Ziguang and Li, Jiaxiang},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/infrastructures7120161},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20221201-48396},
  pages     = {19},
  abstract  = {For the safe and efficient operation of dams, frequent monitoring and maintenance are required. These are usually expensive, time consuming, and cumbersome. To alleviate these issues, we propose applying a wave-based scheme for the location and quantification of damages in dams. To obtain high-resolution "interpretable" images of the damaged regions, we drew inspiration from non-linear full-multigrid methods for inverse problems and applied a new cyclic multi-stage full-waveform inversion (FWI) scheme. Our approach is less susceptible to the stability issues faced by the standard FWI scheme when dealing with ill-posed problems. In this paper, we first selected an optimal acquisition setup and then applied synthetic data to demonstrate the capability of our approach in identifying a series of anomalies in dams by a mixture of reflection and transmission tomography. The results had sufficient robustness, showing the prospects of application in the field of non-destructive testing of dams.},
  subject      = {Damm},
  language  = {en}
}
@article{AlYasiriMutasharGuerlebecketal.,
  author    = {Al-Yasiri, Zainab Riyadh Shaker and Mutashar, Hayder Majid and G{\"u}rlebeck, Klaus and Lahmer, Tom},
  title     = {Damage Sensitive Signals for the Assessment of the Conditions of Wind Turbine Rotor Blades Using Electromagnetic Waves},
  series = {Infrastructures},
  volume    = {2022},
  journal   = {Infrastructures},
  number    = {Volume 7, Issue 8 (August 2022), article 104},
  editor    = {Shafiullah, GM},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/infrastructures7080104},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20220831-47093},
  pages     = {18},
  abstract  = {One of the most important renewable energy technologies used nowadays are wind power turbines. In this paper, we are interested in identifying the operating status of wind turbines, especially rotor blades, by means of multiphysical models. It is a state-of-the-art technology to test mechanical structures with ultrasonic-based methods. However, due to the density and the required high resolution, the testing is performed with high-frequency waves, which cannot penetrate the structure in depth. Therefore, there is a need to adopt techniques in the fields of multiphysical model-based inversion schemes or data-driven structural health monitoring. Before investing effort in the development of such approaches, further insights and approaches are necessary to make the techniques applicable to structures such as wind power plants (blades). Among the expected developments, further accelerations of the so-called "forward codes" for a more efficient implementation of the wave equation could be envisaged. Here, we employ electromagnetic waves for the early detection of cracks. Because in many practical situations, it is not possible to apply techniques from tomography (characterized by multiple sources and sensor pairs), we focus here on the question of whether the existence of cracks can be determined by using only one source for the sent waves.},
  subject      = {Windkraftwerk},
  language  = {en}
}