3D Case Studies of Monitoring Dynamic Structural Tests using Long Exposure Imagery
- School of Civil and Building Engineering, Loughborough University, UK
Keywords: Vibration, Engineering, Monitoring, Long Exposure Imagery, Image Processing, Close Range Photogrammetry
Abstract. Structural health monitoring uses non-destructive testing programmes to detect long-term degradation phenomena in civil engineering structures. Structural testing may also be carried out to assess a structure's integrity following a potentially damaging event. Such investigations are increasingly carried out with vibration techniques, in which the structural response to artificial or natural excitations is recorded and analysed from a number of monitoring locations. Photogrammetry is of particular interest here since a very high number of monitoring locations can be measured using just a few images. To achieve the necessary imaging frequency to capture the vibration, it has been necessary to reduce the image resolution at the cost of spatial measurement accuracy. Even specialist sensors are limited by a compromise between sensor resolution and imaging frequency.
To alleviate this compromise, a different approach has been developed and is described in this paper. Instead of using high-speed imaging to capture the instantaneous position at each epoch, long-exposure images are instead used, in which the localised image of the object becomes blurred. The approach has been extended to create 3D displacement vectors for each target point via multiple camera locations, which allows the simultaneous detection of transverse and torsional mode shapes. The proposed approach is frequency invariant allowing monitoring of higher modal frequencies irrespective of a sampling frequency. Since there is no requirement for imaging frequency, a higher image resolution is possible for the most accurate spatial measurement. The results of a small scale laboratory test using off-the-shelf consumer cameras are demonstrated. A larger experiment also demonstrates the scalability of the approach.