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131.
Three cities in the center of Mexico, declared as cultural heritage, were erected mainly of volcanic tuffs as main construction element. Many of the historic buildings of these cities show significant damage and deterioration. One of the causes of these damages can be attributed to a phenomenon poorly studied in volcanic tuffs, the thermal expansion. To understand the response of volcanic tuffs to thermal expansion, as well as to know their thermal expansion coefficient, thermal expansion test was performed on 12 representative tuffs of these localities. In the same way and to know which of their petrographic, petrophysical, mineralogical, and geochemical properties influence this phenomenon, several laboratory studies were carried out to determine which of these plays an important role in the expansion. The results of our tests showed that volcanic tuffs rocks have very varied thermal expansion values, ranging from 0.7 mm/m at their maximum expansion to even contraction in the order of ??0.5 mm/m. Equally varied were the thermal expansion coefficient values ranging from 10.1 to ??7.2 10?6/K. The most dominant behavior type is the “anisotropic no residual strain behavior”. In general, the behavior of the tuffs in practically all the tests carried out showed a very important heterogeneity in the rocks; however, in spite of the non-homogeneous behavior of the tuffs, three main factors could be identified that play an important role in the thermal expansion of the studied rocks, the chemical composition, e.g., acid volcanic tuff rocks (rhyolitic composition) had greater expansions, while the more basic rocks (basaltic composition) expanded less, the textural and fabrics homogeneity, e.g., the most homogeneous rocks texturally have higher expansion values and the degree of crystallinity, and, e.g., the tuff rocks with the largest amount of glass showed smaller thermal expansions than the tuffs composed of microcrystals.  相似文献   
132.
Classification of Mobile Mapping LiDAR (Light Detection and Ranging) data is a challenge in the research community since the day when laser scanner system were integrated and mounted on vehicles for collection of 3D data in urban environment. The approach proposed here for classifying LiDAR data is analogous to the process followed for classifying data from satellite images. Pixel based and segmentation based methods have been employed in past for classifying images obtained from satellites. These methods were based on spectral properties of objects present in the images. But for Mobile mapping LiDAR data this approach has been applied and tested for the first time. The properties of this data are completely different from that of satellite images. So even if the basic approach remains the same, many changes have to be made in the entire classification process. The paper here aims to propose the basic procedure of using pixel-wise classification on dense 3D LiDAR data.  相似文献   
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