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 visual 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.
Novel techniques have been introduced for site-to-BIM automation and also the design of model-driven data capture solutions that ensure the high quality of as-built models. The developed technqiues focus on interoperable and software-free model udpates.
Fortunately, the project has been selected as the Winner of the 2017 BuildingSMART International Award. The award submission can be found in the list of related publications below.
UAV-Enabled Site-to-BIM Automation: Aerial Robotic and Computer Vision-Based Development of As-Built/As-Is BIMs and Quality Control
The automated integration of as-built and as-is conditions into building information models (BIM) remains a primary challenge for unoccupied aerial vehicles (UAV)-enabled facility and construction inspection. Due to the lack of site-to-BIM data pipelines supporting reality capture technologies such as camera-mounted UAVs, BIMs lose their effectiveness over time since they do not accurately reflect the as-built and as-is conditions; the UAV-captured visual data also remains underutilized. This paper proposes and demonstrates a novel industry foundation classes (IFC)-based solution for UAV-enabled as-built and as-is BIM development, quality control, and smart inspections. It first identifies the requirements of UAV-enabled site-to-BIM automation and its framework; then, it elaborates on the proposed solution which aims to: (1) enable automated conformance checking and quality control using data queried from BIMs and UAV-captured reality in the form of images; and more importantly; and (2) enable the integration of on-site captured reality, including the as-built and as-is conditions, into BIMs. The method leverages the non-proprietary IFC schema, empowering OpenBIM applications, and facilitating interoperability, a core challenge in the information modeling domain. In addition to its support for UAV-enabled applications, the technique can provide the same site-to-BIM functionalities for other types of reality capture and other robotic data collection efforts during construction, commissioning, and facility management.
UAV Mission Planning Using Swarm Intelligence and 4D BIMs in Support of Vision-Based Construction Progress Monitoring and As-Built Modeling
Inspection planning is a primary element of computer vision- and unoccupied aerial vehicle (UAV)-enabled construction monitoring. Prior to the on-site deployment of camera-mounted UAVs, the inspection objectives need to be identified, and optimal inspection plans must be developed; Such plans should ensure complete data acquisition and minimize the use of UAV’s limited flight time. The image capture configuration must be taken into account since it directly affects the downstream applications of the captured data such as progress detection and as-built modeling. This paper proposes a framework and a novel technique which utilizes four-dimensional (4D) building information models (BIM) and swarm intelligence to automatically generate the UAV inspection mission plans. It computationally supports both static and dynamic site layouts. The inspection objectives, their geometry, and their semantics are automatically extracted from BIM, and the corresponding elements are identified. An optimal inspection plan is developed using artificial intelligence, ensuring complete coverage of inspection targets while minimizing flight duration. The method has been tested in UAV-enabled data acquisition scenarios. It is based on the industry foundation classes (IFC), facilitating OpenBIM and reducing the costs associated with the lack of interoperability, a core challenge in information modeling. Due to the target extraction at element and sub-element levels, it supports computer vision-based construction progress monitoring and automated as-built and as-is BIM development.
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
Hamledari, Davari, Sajedi, Zangeneh, McCabe, Fischer (2018), "UAV Mission Planning Using Swarm Intelligence and 4D BIMs in Support of Vision-Based Construction Progress Monitoring and As-built Modeling", Construction Research Congress 2018, New Orleans, USA
Hamledari, Davari, Azar, McCabe, Flager, Fischer (2018), "UAV-Based Site-to-BIM Automation: Aerial Robotic and Computer Vision-Based Development of As-Built/As-is BIMs and Quality Control", Construction Research Congress 2018, New Orleans, USA
In preparatation: "BIM-Enabled and Automated UAV-Based Inspection Mission Planning in Support of Image Capture and Computer Vision-Based Progress Tracking for Indoor Sites", Journal of Automation in Construction
Final Project Reports
TR230 - UAV-Enabled Site-to-BIM Automation: Aerial Robotic-and Computer Vision-Based Development of As-Built/As-Is BIMs and Quality Control, Hamledari, Davari, Sajedi, Zangeneh, McCabe, Fischer
TR231 - UAV Mission Planning Using Swarm Intelligence and 4D BIMs in Support of Vision-Based Construction Progress Monitoring and As-Built Modeling, Hamledari, Davari, Sajedi, Zangeneh, McCabe, Fischer
Original Research Proposal