8.6 Faseroptische Sensorik
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A safety or security related assessment of explosions, accidental and intentional scenarios alike, often necessitate performance of replication-tests. Such test results are necessary to clarify the causes within the scope of forensic investigations. To gain important insights into the behavior of structures and materials under such loading, field tests may also be performed in accordance with different test standards. To determine the resistance of building-structures after explosions, estimation of the residual load-bearing capacity in addition to the assessment of dynamic structural response and damage to the building components is important. In most cases an evaluation of structural integrity is based only on the visual damage, resulting in an overestimation of the residual capacity.
The Bundesanstalt für Materialforschung und -prüfung (BAM) operates the Test site for Technical Safety (TTS) on an area measuring about 12 km2 in the Federal State of Brandenburg for execution of true-to-scale explosion tests. At the TTS, building component testing was performed to assess the suitability of different non-destructive testing methods to characterize the dynamic structural response and damage resulting from the detonation of high explosives.
Different blast-loading scenarios were realized by varying the net explosive mass and the standoff distance with all scenarios representing a near-field detonation. The test object was a reinforced concrete wall 2 m high, 2.5 m wide and 20 cm thick, fixed at both vertical edges. The dynamic loading of the wall was characterized with 8 piezoelectric pressure sensors flush-mounted on the front surface, thus measuring the reflected pressures from the shock wave. The tests were conducted with the aim of characterizing the global behavior of the wall under dynamic shock loading and the resulting local damage pattern, respectively. High speed digital image correlation was implemented in combination with multiple acceleration sensors to observe the rear surface of the wall to chart the dynamic deflection during the loading and to determine the residual deformation after the loading had ceased. In addition, one test specimen was instrumented with fiber optic sensor cables, both fixed to the rebars and embedded in the concrete-matrix, respectively. Firstly, these sensors were interrogated during the blast test by a distributed acoustic sensing (DAS) device using a particularly high sampling rate to measure the shock-induced vibrations in the structure with high temporal resolution. This delivers information on dynamics of compression and tension cycles from within the structure. Secondly, the local damage-pattern emerging during the series of blasts was determined via distributed fiber optic strain sensing (DSS) by interrogating the embedded fiber optic sensors with a high spatial resolution DSS device after each blast. This enabled the characterization of non-visual damage to the structure, in particular with regard to the formation of localized cracks in the concrete matrix. The DSS was further complimented by a structure-scanner based on ultrasonic measurements.
Our contribution describes this new test approach in detail. Results of the three datasets, namely dynamic shock loading, global behavior of the test object and the local damage pattern will be presented. The suitability of the implemented measurement methods will be discussed in combination with the challenges in their application for technical safety evaluation of building components under explosive loading.
This paper presents reported machine learning approaches in the field of Brillouin distributed fiber optic sensors (DFOSs). The increasing popularity of Brillouin DFOSs stems from their capability to continuously monitor temperature and strain along kilometer-long optical fibers, rendering them attractive for industrial applications, such as the structural health monitoring of large civil infrastructures and pipelines. In recent years, machine learning has been integrated into the Brillouin DFOS signal processing, resulting in fast and enhanced temperature, strain, and humidity measurements without increasing the system’s cost. Machine learning has also contributed to enhanced spatial resolution in Brillouin optical time domain analysis (BOTDA) systems and shorter measurement times in Brillouin optical frequency domain analysis (BOFDA) systems. This paper provides an overview of the applied machine learning methodologies in Brillouin DFOSs, as well as future perspectives in this area.
Fiber reinforced polymers (FRPs) are a well established material in lightweight applications, e.g. in automotive, aerospace or wind energy. The FRP components are subjected to multiaxial mechanical as well as hygrothermal loads. Common operation temperatures are in the range of 213 K and 373 K (-60 °C and 100 °C) at a relative humidity of 10% to 90%. In spacecraft applications, the environmental conditions are even more extreme. However, the correlation between multiaxial mechanical loading and harsh environment conditions have to-date not been investigated in detail. The project aims to investigate the fatigue behavior of FRPs dependent on multiaxial mechanical loading, temperature, and humidity. Extensive experimental testing is performed on flat plate and cylindrical tube specimens, accompanied by numerical and analytical calculations.