Seismic behavior of a masonry chimney with severe cracking condition: preliminary study

This paper presents a structural analysis of a masonry chimney built in the 1940s, which is currently being cataloged as local interest heritage. This structure has not served any industrial purpose for the last thirty years. The chimney is located in the town of Agost (Alicante - Spain) and directly exposed to the prevailing winds from the sea, as it is approximately 12 km away from the waterfront and there are not any significant barriers, which could protect the structure against the wind. There are longitudinal cracks and fissures all along the shaft because of the chimney’s geometrical characteristics, the effect of the masonry creep and especially the lack of maintenance. Moreover, there is also a permanent bending deformation in the upper 1/3 of the height due to the wind pressure. A numerical analysis for the static behavior against gravity and wind loads was performed using the structure’s current conditions after a detailed report of its geometry, its construction system and the cracking pattern. Afterwards, the dynamic behavior was studied, i.e. a seismic analysis using both response spectra and accelerograms in order to examine the structural stability. This work shows the pre-monitoring analysis before any experimental testing. Using the current results the future test conditions will be determined (e.g. number of sensors and monitoring point location, excitation systems, etc) prior to a possible structural reinforcement by applying composite material (fiber reinforced polymers).

Real-time 3D semi-local surface patch extraction using GPGPU

Feature vectors can be anything from simple surface normals to more complex feature descriptors. Feature extraction is important to solve various computer vision problems: e.g. registration, object recognition and scene understanding. Most of these techniques cannot be computed online due to their complexity and the context where they are applied. Therefore, computing these features in real-time for many points in the scene is impossible. In this work, a hardware-based implementation of 3D feature extraction and 3D object recognition is proposed to accelerate these methods and therefore the entire pipeline of RGBD based computer vision systems where such features are typically used. The use of a GPU as a general purpose processor can achieve considerable speed-ups compared with a CPU implementation. In this work, advantageous results are obtained using the GPU to accelerate the computation of a 3D descriptor based on the calculation of 3D semi-local surface patches of partial views. This allows descriptor computation at several points of a scene in real-time. Benefits of the accelerated descriptor have been demonstrated in object recognition tasks. Source code will be made publicly available as contribution to the Open Source Point Cloud Library.