Refine
Has Fulltext
- yes (19) (remove)
Document Type
- Article (19) (remove)
Institute
- Institut für Strukturmechanik (ISM) (16)
- Bauhaus-Institut für zukunftsweisende Infrastruktursysteme (b.is) (1)
- Graduiertenkolleg 1462 (1)
- Materialforschungs- und -prüfanstalt an der Bauhaus-Universität (1)
- Professur Modellierung und Simulation - Konstruktion (1)
- Professur Stochastik und Optimierung (1)
Keywords
- Maschinelles Lernen (6)
- Erdbeben (5)
- OA-Publikationsfonds2020 (5)
- rapid visual screening (4)
- Machine learning (3)
- OA-Publikationsfonds2022 (3)
- damaged buildings (3)
- earthquake safety assessment (3)
- Fahrleitung (2)
- Finite-Elemente-Methode (2)
- OA-Publikationsfonds2021 (2)
- Schaden (2)
- Vulnerability assessment (2)
- buildings (2)
- soft computing techniques (2)
- vulnerability assessment (2)
- 3D reinforced concrete buildings (1)
- Arc-direct energy deposition (1)
- Baustahl (1)
- Bayesian inference (1)
- Beam-to-column connection; semi-rigid; flush end-plate connection; moment-rotation curve (1)
- Bodenmechanik (1)
- Building safety assessment (1)
- Catenary poles (1)
- Computational modeling (1)
- Damm (1)
- Defekt (1)
- Dielectric materials (1)
- Dreidimensionales Modell (1)
- Dual phase steel (1)
- Earthquake (1)
- Elektrostatische Welle (1)
- Empire XPU 8.01 (1)
- Erdbebensicherheit (1)
- Fahrleitungsmast (1)
- Finite element methods (1)
- Fire resistance; Parameter optimization; Sensitivity analysis; Thermal properties (1)
- Frequency (1)
- Fuzzy Logic (1)
- Fuzzy-Logik (1)
- High-speed electric train (1)
- Impedance measurement (1)
- MATLAB (1)
- Machine Learning (1)
- Manufacturing (1)
- Materialverhalten (1)
- Matlab (1)
- Mild steel (1)
- Model-free status monitoring (1)
- Multi-criteria decision making (1)
- Neuronales Netz (1)
- OA-Publikationsfonds2023 (1)
- Optimierung (1)
- Partial differential equations (1)
- Piezoelectric materials (1)
- Polymorphie (1)
- Rapid Visual Screening (1)
- Resonance (1)
- Resonanz (1)
- Rotorblatt (1)
- SHM (1)
- Schadenserkennung (1)
- Sensitivitätsanalyse (1)
- Sigmoid function (1)
- Spannungs-Dehnungs-Beziehung (1)
- Stahlbetonkonstruktion (1)
- Stochastik (1)
- Stoffeigenschaft (1)
- Stress-strain curve (1)
- Strukturanalyse (1)
- Strukturmechanik (1)
- Stütze (1)
- TPOGS (1)
- Thermodynamische Eigenschaft (1)
- Tragfähigkeit (1)
- Träger (1)
- Vulnerability (1)
- Windkraftwerk (1)
- artificial neural network (1)
- artificial neural networks (1)
- crack detection (1)
- damage identification (1)
- dams (1)
- earthquake (1)
- earthquake damage (1)
- earthquake vulnerability assessment (1)
- electromagnetic waves (1)
- experimental validation (1)
- extreme events (1)
- fisher-information matrix (1)
- full-waveform inversion (1)
- ground structure (1)
- hybride Werkstoffe (1)
- inverse analysis (1)
- machine learning (1)
- mean-squared error (1)
- mitigation (1)
- natural hazard (1)
- polymorphe Unschärfemodellierung (1)
- rapid assessment (1)
- rapid classification (1)
- seismic assessment (1)
- seismic risk estimation (1)
- seismic vulnerability (1)
- supervised learning (1)
- support vector machine (1)
- topology optimization (1)
- tower-like structures (1)
- vibration-based damage identification (1)
- wave propagation (1)
- wind turbine rotor blades (1)
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.