Refine
Document Type
- Article (59) (remove)
Institute
- Institut für Strukturmechanik (ISM) (55)
- Professur Stochastik und Optimierung (40)
- Bauhaus-Institut für zukunftsweisende Infrastruktursysteme (b.is) (2)
- Graduiertenkolleg 1462 (1)
- Materialforschungs- und -prüfanstalt an der Bauhaus-Universität (1)
- Professur Modellierung und Simulation - Konstruktion (1)
Keywords
- Stochastik (41)
- Strukturmechanik (41)
- Angewandte Mathematik (40)
- 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)
- Stoffeigenschaft (1)
- Stress-strain curve (1)
- Strukturanalyse (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)
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.
When it comes to monitoring of huge structures, main issues are limited time, high costs and how to deal with the big amount of data. In order to reduce and manage them, respectively, methods from the field of optimal design of experiments are useful and supportive. Having optimal experimental designs at hand before conducting any measurements is leading to a highly informative measurement concept, where the sensor positions are optimized according to minimal errors in the structures’ models. For the reduction of computational time a combined approach using Fisher Information Matrix and mean-squared error in a two-step procedure is proposed under the consideration of different error types. The error descriptions contain random/aleatoric and systematic/epistemic portions. Applying this combined approach on a finite element model using artificial acceleration time measurement data with artificially added errors leads to the optimized sensor positions. These findings are compared to results from laboratory experiments on the modeled structure, which is a tower-like structure represented by a hollow pipe as the cantilever beam. Conclusively, the combined approach is leading to a sound experimental design that leads to a good estimate of the structure’s behavior and model parameters without the need of preliminary measurements for model updating.
The current study attempts to recognise an adequate classification for a semi-rigid beam-to-column connection by investigating strength, stiffness and ductility. For this purpose, an experimental test was carried out to investigate the moment-rotation (M-theta) features of flush end-plate (FEP) connections including variable parameters like size and number of bolts, thickness of end-plate, and finally, size of beams and columns. The initial elastic stiffness and ultimate moment capacity of connections were determined by an extensive analytical procedure from the proposed method prescribed by ANSI/AISC 360-10, and Eurocode 3 Part 1-8 specifications. The behaviour of beams with partially restrained or semi-rigid connections were also studied by incorporating classical analysis methods. The results confirmed that thickness of the column flange and end-plate substantially govern over the initial rotational stiffness of of flush end-plate connections. The results also clearly showed that EC3 provided a more reliable classification index for flush end-plate (FEP) connections. The findings from this study make significant contributions to the current literature as the actual response characteristics of such connections are non-linear. Therefore, such semirigid behaviour should be used to for an analysis and design method.
The economic losses from earthquakes tend to hit the national economy considerably; therefore, models that are capable of estimating the vulnerability and losses of future earthquakes are highly consequential for emergency planners with the purpose of risk mitigation. This demands a mass prioritization filtering of structures to identify vulnerable buildings for retrofitting purposes. The application of advanced structural analysis on each building to study the earthquake response is impractical due to complex calculations, long computational time, and exorbitant cost. This exhibits the need for a fast, reliable, and rapid method, commonly known as Rapid Visual Screening (RVS). The method serves as a preliminary screening platform, using an optimum number of seismic parameters of the structure and predefined output damage states. In this study, the efficacy of the Machine Learning (ML) application in damage prediction through a Support Vector Machine (SVM) model as the damage classification technique has been investigated. The developed model was trained and examined based on damage data from the 1999 Düzce Earthquake in Turkey, where the building’s data consists of 22 performance modifiers that have been implemented with supervised machine learning.
The study presents a Machine Learning (ML)-based framework designed to forecast the stress-strain relationship of arc-direct energy deposited mild steel. Based on microstructural characteristics previously extracted using microscopy and X-ray diffraction, approximately 1000 new parameter sets are generated by applying the Latin Hypercube Sampling Method (LHSM). For each parameter set, a Representative Volume Element (RVE) is synthetically created via Voronoi Tessellation. Input raw data for ML-based algorithms comprises these parameter sets or RVE-images, while output raw data includes their corresponding stress-strain relationships calculated after a Finite Element (FE) procedure. Input data undergoes preprocessing involving standardization, feature selection, and image resizing. Similarly, the stress-strain curves, initially unsuitable for training traditional ML algorithms, are preprocessed using cubic splines and occasionally Principal Component Analysis (PCA). The later part of the study focuses on employing multiple ML algorithms, utilizing two main models. The first model predicts stress-strain curves based on microstructural parameters, while the second model does so solely from RVE images. The most accurate prediction yields a Root Mean Squared Error of around 5 MPa, approximately 1% of the yield stress. This outcome suggests that ML models offer precise and efficient methods for characterizing dual-phase steels, establishing a framework for accurate results in material analysis.