||Investigation of Terrestrial Laser Scanning Reflectance Intensity and RGB Distributions to Assist Construction Material Identification
||Muhammad Usman Hassan, Asli Akcamete-Gungor and Cagla Meral
||Terrestrial Laser Scanning (TLS) allows collection of dense 3D point cloud data that captures a structure's as-is conditions. The geometric information from the collected data could be used to generate a 3D-model of the structure. However, the generated model usually lacks functional information - a basic requirement for a semantically rich information model. Some of the functional information (such as cost, mechanical and thermal performance) could be derived if the material used in a particular geometry is identified. The material related information in the collected data, which is mostly underutilized in the construction industry, could be potentially used for material identification. This paper investigates the possibility of material identification from the TLS data using the obtained scaled reflectance intensity (I_i) from the laser scanner and the Red-Green-Blue (RGB) values from the in-built camera images. For this purpose, structural concrete, light-weight concrete and clay brick samples were scanned under two different conditions: (i) high/low ambient illumination, (ii) dry/wet surface. Obtained results show that scanned materials have different I_i distributions. The illumination conditions have no effect on the obtained I_i distribution; however, the surface moisture conditions vary the measured I_i distributions considerably. Recorded RGB values of the illuminated samples could be used as a secondary parameter for material identification. These results show that TLS has a potential of augmenting the visual material identification processes.
|Year of publication:
||Laser Scanning, Point Cloud, Automated Material Identification, Construction Materials
Muhammad Usman Hassan, Asli Akcamete-Gungor and Cagla Meral (2017).
Investigation of Terrestrial Laser Scanning Reflectance Intensity and RGB Distributions to Assist Construction Material Identification. Lean and Computing in Construction Congress (LC3): Volume I Ð Proceedings of the Joint Conference on Computing in Construction (JC3), July 4-7, 2017, Heraklion, Greece, pp. 507-515,