Filtern
Erscheinungsjahr
Dokumenttyp
- Beitrag zu einem Tagungsband (51)
- Zeitschriftenartikel (29)
- Vortrag (26)
- Posterpräsentation (13)
- Buchkapitel (9)
- Beitrag zu einem Sammelband (7)
- Dissertation (1)
Schlagworte
- Structural health monitoring (136) (entfernen)
Organisationseinheit der BAM
- 8 Zerstörungsfreie Prüfung (44)
- 8.6 Faseroptische Sensorik (20)
- 7 Bauwerkssicherheit (17)
- 8.2 Zerstörungsfreie Prüfmethoden für das Bauwesen (16)
- 7.2 Ingenieurbau (15)
- 8.4 Akustische und elektromagnetische Verfahren (9)
- 3 Gefahrgutumschließungen; Energiespeicher (3)
- VP Vizepräsident (3)
- VP.1 eScience (3)
- 3.0 Abteilungsleitung und andere (2)
Paper des Monats
- ja (2)
Eingeladener Vortrag
- nein (26)
Growth of vehicle traffic density can be observed in many countries all over the world. This accretion is caused by world-wide population growth on the one hand, but also by increasing freight volumes and, thus, freight transports on the streets on the other hand.
This increased exposure becomes more and more of a problem for the civil infrastructure such as bridges. Many of these bridges are nowadays stressed to their limits by higher loads than they were originally designed for and/or operating times beyond the initially predicted life span. This raises questions about structural safety and lifetime prediction, of course, and therefore illustrates the need for accurate structural monitoring.
Since the lifetime of bridge structures is primarily influenced by their traffic loading, an accurate identification of load configurations over the whole length of a structure is most desirable.
In this paper, a method for vehicle load identification is proposed. It involves Bayesian Analysis and (quasi-)static importance functions in order to estimate vehicle positions, velocities and weights. The structure is modeled with finite elements in order to generate model predictions for different load configurations. The model predictions are compared to the actual measured data to identify the most probable loading configuration for that measurement. This involves the use of enhanced Monte Carlo simulations such as MCMC to reduce the computational effort. The measured data from different kinds of sensors can (and should) be combined for accuracy gain – in this case a combination of measured displacements and inclinations.
Since the measurements take place over some time during the passage of the vehicle, these estimations are carried out for several time instants for which the estimation is carried out. The advantage of using Bayesian Updating Method is the embodied learning effect leading to an improvement of the estimation when adding new information in a new calculation step.
Using the estimates for the loading conditions of a bridge structure as well as measurements of the structural responses, Bayesian analysis is again used in order to estimate localized structural parameters such as Young's modulus or Moments of Inertia in form of probability density functions yielding most probable values for the parameters.
Considering the difficulties for load identification close to the support poles of the bridge and therefore for the proposed structural parameter identification procedure, it is clear that this problem is ill posed. Bayesian regularization methods also have proven to be very effective when handling ill posed problems.
Guided wave ultrasonic features in composite overwrapped pressure vessels towards digital twin
(2023)
The digitalization of quality control processes and the underlying data infrastructures for safety relevant components, such as hydrogen pressure vessels, plays a significant role in the transition towards Industry 4.0. In the current safety regulations for hydrogen pressure vessels, there is no established concept for structural health monitoring. The development of a reliable structural health monitoring methodology for monitoring the structural integrity of pressure vessels enables a fast-forward transition from personnel- and costintensive recurring inspections, a.k.a. periodic maintenance, to predictive maintenance. In the work presented; we investigated the application of ultrasonic guided wave propagation to monitor and assess the condition of Type IV composite overwrapped pressure vessel (COPV). A sensor network of fifteen piezo-electric wafers is placed on the carbon fibre reinforced composite cylinder. Five different artificial damage configurations are created by gluing two different weight blocks on three different locations. The database containing measured guided wave data sets is enriched by two different boundary conditions. We utilized an open-source software, openBIS labnotebook, to store and analyse experimental datasets. The guided wave ultrasonic signals were investigated and analysed by using commonly used ultrasonic features (e.g., amplitude, frequency, time of flight) as well as non-traditional time-series features (kurtosis, skewness, variance). The features were used to calculate damage index and the detection performance for the results has been evaluated. The results suggest that both traditional and non-traditional features assume significant importance in artificial damage detection. The future works will additionally involve the impacts of operational conditions, such as periodic pressure variations temperature loadings as well as material degradations.
The digitalization of quality control processes and the underlying data infrastructures for safety relevant components, such as hydrogen pressure vessels, plays a significant role in the transition towards Industry 4.0. In the current safety regulations for hydrogen pressure vessels, there is no established concept for structural health monitoring. The development of a reliable structural health monitoring methodology for monitoring the structural integrity of pressure vessels enables a fast-forward transition from personnel- and costintensive recurring inspections, a.k.a. periodic maintenance, to predictive maintenance. In the work presented; we investigated the application of ultrasonic guided wave propagation to monitor and assess the condition of Type IV composite overwrapped pressure vessel (COPV). A sensor network of fifteen piezo-electric wafers is placed on the carbon fibre reinforced composite cylinder. Five different artificial damage configurations are created by gluing two different weight blocks on three different locations. The database containing measured guided wave data sets is enriched by two different boundary conditions. We utilized an open-source software, openBIS labnotebook, to store and analyse experimental datasets. The guided wave ultrasonic signals were investigated and analysed by using commonly used ultrasonic features (e.g., amplitude, frequency, time of flight) as well as non-traditional time-series features (kurtosis, skewness, variance). The features were used to calculate damage index and the detection performance for the results has been evaluated. The results suggest that both traditional and non-traditional features assume significant importance in artificial damage detection. The future works will additionally involve the impacts of operational conditions, such as periodic pressure variations temperature loadings as well as material degradations.
Smart geosynthetics for structural health monitoring using fully distributed fiber optic sensors
(2015)
We report on the development of a complete system for structural health monitaring of large-scale geotechnical structures. The system uses Brillouin frequency domain analysis (BOFDA) for distributed measurement of strain and temperature along glass optical fibers (GOFs) by determining the so-called Brillouin frequency shift (BFS). ln a number of research activities we managed to optimize the BOFDA measurement setup regarding the spatial resolution and the accuracy of the measured quantities. Moreover, we realized a simultaneaus measurement of strain and temperature by using nonzero dispersion-shifted fibers (NZDSFs) with several multiple Brillouin scattering resonances. ln cooperation with our German and ltalian industry partners we finally succeeded in fabricating low-attenuation smart geosynthetics as a sensor component of a fully distributed Brillouin-based fiber optic monitaring system.
We report on the development of a distributed fiber optical sensor system for structural health monitoring of largc-scale geotechnical and civil infrastructures. The system is based on Brillouin frequency domain analysis (BOFDA) for distributed measurement of strain and temperature along Standard silica optical fibers used as sensors. By additional incorporation of the fiber optic sensors in geosynthetics, two-dimensional sensors can be realized. Such smart geosynthetics can be used for reinforeerneut of geotechnical and masonry structures and the embedded fiber optic sensors can provide information about the condition of the structures and detect the presence of any damages and destructions in real time.
We present a prototype of a distributed POF sensing system for strain detection in road construction and civil engineering ready to be used in practical applications. The system is based on a cost-efficient digital incoherent optical frequency domain reflectometry (I-OFDR). In this approach, the strain-induced changes in the Rayleigh backscattering profile along a perfluorinated polymer optical fiber (PF-POF) are determined by measuring the complex transfer function of the sensing fiber using a compact digital data acquisition unit. The digital unit replaces an oversized vector network analyzer (VNA) enabling suitability of the measurement system for out-door use and providing a significant reduction of the total system costs at the same time. The entire sensor con-cept includes the use of geosynthetics with integrated PF-POFs as two-dimensional sensor structures for geotech-nical applications. The robustness and the functionality of the distributed POF I-OFDR sensors were proven in the field. The field test involved embedding of two geomats, each with two polymer optical sensor loops, into the embankment of the federal road B 91 in a section near Leipzig. The measurements conducted after the sensor-based geomats had been installed confirmed both the potential of such smart geosynthetics and the functionali-ty of the digital I-OFDR prototype.
This study evaluates whether subsurface features in rotor blades, mainly made of Glass Fibre Reinforced Plastics (GFRP), can generally be detected with ‘‘solar thermography”. First, the suitability of the sun is tested for acting as a heat source for applying active thermography on a 30 mm thick GFRP test specimen. Second, a defective rotor blade segment is inspected outdoors under ideal natural conditions using the sun as excitation source. Additionally, numerical FEM-simulations are performed and the comparability between experiment and simulation is evaluated for outdoor measurements.
Damages in infrastructure due to moisture amount to billions of Euros every year. For a more predictive structural health monitoring in civil engineering, the detection and monitoring of hazardous moisture in steel reinforced concrete constructions is of high interest. The sensors have to be wireless, elsewise they weaken the concrete cover of the rebars. The lifetime of such constructions is normally decades, thus the sensors have to be battery-free and fully passive. Considering these requirements, passive RFID-based sensors are developed. Communication and energy supply are realized wireless via the electromagnetic field of a RFID transmitter. The passive RFIDbased sensors are embedded into the concrete to enable the monitoring of moisture transport in porous materials. Results of the hydration process are shown.