Date of Award
Spring 2024
Document Type
Dissertation - Restricted
Degree Name
Doctor of Philosophy (PhD)
Department
Civil, Construction, and Environmental Engineering
First Advisor
Yong Bai
Abstract
The demand for building façade inspection continuously increases across the United States. Currently, an inspection is done manually during which an inspector must work in an unsafe environment. Recent advancements of drone, digital twin, and AI technologies make it possible to automate the building façade inspection operation and produce an editable as-is digital façade condition model for a better façade inspection as well as documentation.
This dissertation presents the research results of the development of the Scan4Façade3DBIM, which is a fully automatic reality capture (RC) data analytics technology designed for as-is building façade condition and defect BIM modeling using drone photograph and photogrammetric point clouds. The workflow of Scan4Façade3DBIM starts with raw data preprocessing, followed by the cropped single building point cloud going through (building) Point Cloud Alignment, (wall plane) Point Cloud Separation, (wall plane) Point Cloud to (wall plane) Orthoimage, (wall plane section) Orthoimage Separation, (walls, elements and defections) Coordinate Conversion, and (walls, elements, and defections) 3D and BIM Modeling using the developed point cloud and image understanding algorithms, functions and tools. AI-aided image pixelwise segmentation is used to recognize the targeted façade elements and defections. The extracted walls, façade elements and defections, and their geometrical information are saved into a four-level data structure of Wall List, Wall Coordinates, Element Boxes and Defection Location and Dimension, and Irregular Element/Defection Counters in the optimized table-like formatted plain text files. In addition, Dynamo for Revit script is developed for automatic creating the building façades and defections as an editable Revit BIM model.
Comprehensive case studies and experiments were conducted on different styles of buildings with 3D point clouds that were generated from different photogrammetry software with different drone-captured oblique photos. In addition, synthetic building points were used to analyze Scan4Façade3DBIM’s performance in terms of the point cloud quality (i.e., thickness). The results showed Scan4Façade3DBIM could accurately determine wall footprints even though the point cloud had a noise of 0.5 meters. The results of this research have advanced the knowledge of automatic generation of editable as-is façade+defect digital records from the RC data (i.e., 3D building point cloud and 2D defect oblique photos), which is a critical step to automate the building façade inspection using drone, digital twin, and AI technologies.