Silvio Savarese, Martin Fischer, Forest Flager, Hesam Hamledari
The construction of our built environment today is often managed without a current and accurate understanding of the as-built condition on site, leading to poor project cost and schedule conformance. The proposed solution is a method that automatically updates objects in the Building Information Model (BIM) to reflect the as-built condition and to inform stakeholders of potential quality control and schedule issues associated with current site conditions so that necessary corrections can be made at a highly accelerated rate. The proposed research approach integrates three technologies: unmanned aerial vehicles (UAVs), computer vision, and BIM. First, a UAV will be used to efficiently capture a point cloud representation of the as-built condition on site. Computer vision will then identify constructed objects from the point cloud geometry. This information will be used to automatically update the position of elements in the BIM. Finally, the as-built and as-designed BIMs will be to update the project schedule and to inform stakeholders of potential quality control issues.
Researchers and practitioners have been interested in achieving regular model updates. Conventionally, modelers need to manually detect the discrepancies, identify the corresponding elements, and update them. This is a time-consuming, inefficient, and costly process, proven to hamper BIM use.
The use of UAVs for data capture not only eliminates the tedious data collection processes, but it also provides a unique opportunity for streamlining the site-to-BIM data communication which can potentially increase the use of BIM over the facility life-cycle and support timely and data-driven decision making.
PHASE 1: Identification of Reality Capture Method
The objective is to determine which UAV-based reality capture method is best suited to support an automated construction monitoring and identifying quality control issues. Photogrammetry and LiDAR methods will be compared on the basis of:
- Data suitability for automated object recognition using computer vision
- Relative and absolute accuracy of object spatial localization
- Time and costs associated with data capture and computing
PHASE 2: Development of BIM-based Construction Progress Monitoring and Quality Control Method
This phase aims to:
- Effectively document and communicate actual versus planned construction of building elements and to automatically update the project schedule, semantics, and geometry accordingly.
- Identify potential issues resulting from the object's as-built position, including objects that are outside of the acceptable construction tolerances in a timely fashion, so the most cost effective remediation can be performed.
H. Hamledari (2017), "IFC-Enabled Site-to-BIM Automation: an Interoperable Approach Toward the Integration of Unmanned Aerial Vehicle (UAV)-Captured Reality into BIM", BuildingSMART Int. Award 2017, bSI International Summit, London, UK
H. Hamledari, B. McCabe, S. Davari, A. Shahi, E. Azar, F. Flager (2017), "Evaluation of Computer Vision- and 4D BIM-based Construction Progress Tracking on a UAV Platform", 6th International CRC/CSCE Construction Specialty Conference, Vancouver
H. Hamledari, E. Azar, B. McCabe (2017), "IFC-based Development of As-Built and As-is BIMs Using Construction and Facility Inspection Data: Towards Site-to-BIM Data Transfer Automation", ASCE Journal of Computing in Civil Engineering
H. Hamledari, B. McCabe, S. Davari, A. Shahi (2017), "Automated Schedule and Progress Updating of IFC-based 4D BIMs", ASCE Journal of Computing in Civil Engineering, 31 (4)
H. Hamledari, B. McCabe, S. Davari (2017), "Automated Computer Vision-based Detection of Components of Indoor Under-Construction Partitions", Automation in Construction, Volume 74, pages 78-94
H. Hamledari (2016), "InPRO: Automated Indoor Construction Progress Monitoring Using Unmanned Aerial Vehicles", MASc Thesis, University of Toronto
IN PREPARATION/ UNDER REVIEW
H. Hamledari, O. Sajedi, B. McCabe, F. Flager, M. Fischer (2018), "UAV Mission Planning Using Swarm Intelligence and IFC-based 4D BIMs for Computer Vision-Based Construction Progress Monitoring", Construction Research Congress 2018 (under review)
H. Hamledari, E. Azar, B. McCabe, F. Flager, M. Fischer (2018), "UAV-Based Site-to-BIM Data Transfer Automation: Integrated Model-Driven Approach Toward Quality Control and Facility Inspection Using IFC Data Model", Construction Research Congress 2018 (under review)
H. Hamledari, O. Sajedi, B. McCabe, A. Shahi, P. Zangeneh, "BIM-Enabled and Automated UAV-Based Inspection Mission Planning in Support of Image Capture and Computer Vision-Based Progress Tracking for Indoor Sites", ASCE Journal of Construction Engineering and Management (in preparation)
H. Hamledari, M. Fischer, F. Flager, "Automating the IFC-based Development of Site-to-BIM Data Transfer Models in Support of BIM Updates, On-Site Data Collection, and Information Retrieval", ASCE Journal of Construction Eng and Management (in preparation)
H. Hamledari, M. Fischer, "Bag-of-Visual-Word- and Texture-Based Automated Classification of State of Progress for Under-Construction Indoor Partitions", Journal of Advanced Engineering Informatic (in preparation)
"Roles, Benefits, and Challenges of Using UAVs for Indoor Smart Construction Applications", International Workshop on Computing in Civil Engineering 2017, Seattle, United States (June 2017)
"Evaluation of Computer Vision- and 4D BIM-based Construction Progress Tracking on a UAV Platform", 6th CRC/CSC Construction Specialty Conference, Vancouver, Canada (June 2017)
"Automated UAV-based Indoor Construction Progress Tracking Using Machine Intelligence, and nD BIMs", Invited Talk, Virginia Polytechnic Institute and State University, United States (May 2017)