Abstract
About 50% of buildings in the US were constructed before energy codes were introduced. Modular overclad panel retrofits, in which a new envelope is constructed over the existing building, are a promising solution given that it minimizes occupant disruption and shortens construction time at the jobsite. Current state-of-the-art retrofit panel layout and dimensioning consists of three steps: 1) 3D point cloud data generation of the building envelope using commonly available surveying equipment, 2) manual segmentation of 3D point cloud data by a trained professional to identify and dimension window openings, door openings, and other architectural features, and 3) modular panel layout optimization and dimensioning by an architect or engineer. Among these steps, the second one remains the most difficult and costly because it is very labor-intensive. We propose a methodology to automatically label 3D point cloud data to reduce the time and expense spent in manual segmentation. Machine learning methods were employed to classify the point cloud data into distinct groups, each of which corresponds to different features of the building envelope. After classification, a segmentation algorithm was developed to perform boundary detection and separate the components of the façade. Finally, the algorithm returns the relative positions and dimensions of the features in the building envelope. The measurements obtained with the proposed automated method were compared against the actual dimensions to determine the overall algorithm accuracy. The proposed algorithm can then be used to reduce manual efforts for 3D point cloud labeling before modular panel layout optimization is performed.
| Original language | English |
|---|---|
| Title of host publication | Thermal Performance of the Exterior Envelopes of Whole Buildings XV International Conference |
| Publisher | American Society of Heating Refrigerating and Air-Conditioning Engineers |
| Pages | 520-527 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781955516280 |
| State | Published - 2022 |
| Event | 15th International Conference on Thermal Performance of the Exterior Envelopes of Whole Buildings 2022 - Clearwater Beach, United States Duration: Dec 5 2022 → Dec 8 2022 |
Publication series
| Name | Thermal Performance of the Exterior Envelopes of Whole Buildings |
|---|---|
| ISSN (Electronic) | 2166-8469 |
Conference
| Conference | 15th International Conference on Thermal Performance of the Exterior Envelopes of Whole Buildings 2022 |
|---|---|
| Country/Territory | United States |
| City | Clearwater Beach |
| Period | 12/5/22 → 12/8/22 |
Funding
Overclad envelope retrofits using premanufactured components require precise measurements of the existing envelope to adequately design and manufacture prefabricated panels. However, traditional surveying of existing buildings is a time-consuming and expensive process. Since the incorporation of modern scanning capabilities into terrestrial surveying equipment, there is a significant opportunity to automate and expedite the surveying process. However, even the state-of-the-art scanning method of acquiring precise dimensions of the existing envelope requires significant user input and time requirements. Current state-of-the-art retrofit panel design and dimensioning consists of three steps: 1) 3D point cloud data generation of the building envelope using commonly available surveying equipment, 2) manual segmentation of 3D point cloud data by a trained Bryan Maldonado, Nolan Hayes, and Diana Hun are with the Buildings and Transportation Science Division, Oak Ridge National Laboratory, Oak Ridge, TN. Daniel Howard is with the University of Tennessee. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This material is based upon work supported by the US Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office under contract DE-AC05-00OR22725.