Integration of Textural and Material Information into BIM Using Spectrometry and Infrared Sensing

Abstract

As-built data and drawings are essential documents that contain object dimensions, locations, materials, and other important information used by different parties during the processes of the building design, construction, and operation in order to perform commissioning or energy analysis, and preventive and corrective maintenance. These documents go through many changes and updates throughout the construction phase and after the handover, and this process is currently tracked manually which is time consuming. In the Architecture, Engineering, and Construction (AEC) industry, Building Information Modeling is increasingly used throughout a facility's life cycle for various applications, such as planning, conceptual design, detailing, fabrication, renovations, space usage planning, and managing building maintenance. For existing buildings, as-built Building Information Models (BIMs) are often constructed using dense, three dimensional (3D) point clouds data obtained from laser scanners. Laser scanners can quickly detect and capture the "as-is" conditions of a structure, and then the points get processed to obtain the 3D geometric model. Traditionally, as-built BIMs do not have material and textural information of the buildings integrated into them. This thesis presents a methodology for generation of textural and material rich as-built BIM Models. The proposed method used thermal infrared sensing to capture thermal images of the interior walls of an existing building. These images were then processed and only walls' features were extracted using a segmentation algorithm. The digital numbers of resulted images were then transformed into radiance values that represent the emitted thermal infrared radiation recorded at each pixel of the interior walls images. These radiance values were used to extract textural information from the images. Statistical correlations between these values and models of interior gypsum and concrete were obtained through a Monte Carlo simulation approach and further used to extract material information from the images. The extracted texture and material information were then integrated in the BIM Model, providing the data needed for the assessment of building conditions in relation to energy efficiency and water and waste water systems leaks.