Skip Ribbon Commands
Skip to main content
Sign In

​​​​​​​​​​Light Detection and Ranging (LiDAR) Technology


Goal

Provide a technical assessment and evaluation of how LiDAR data may be collected and transformed into links directly with plant database information to simplify the use in hazard analysis. LiDAR uses fast rotating distance measurement equipment to construct a 3D model out of points of measurement, called a point cloud. This provides accurate spatial information but nothing else. To be more useful, facilities must label or tag information to specific points in the model. This work looked at ways to use artificial intelligence to assist humans in tagging this data with the main task of the human to verify and complete unknown items. The final results would be a model with detailed item information along with the location for use in various analysis tools.​​

Outcome

Researchers demonstrated how LiDAR technology can be used to support hazard analyses. Using an existing Plant Data Model System or FRANX databases from existing plants, this project showed how to simplify the process of linking the data to spatial locations or items in the three-dimensional (3D) model developed using LiDAR. The pilot project demonstrated the process of how a LiDAR-developed 3D model was linked to plant data and then exported for direct use in fire probabilistic risk assessment with very little manually linking of data. This pilot demonstrated a process that significantly reduced manual efforts typically needed for similar tasks.​

​Major Accomplishment

Completed the development of LiDAR technology and demonstrate the enhanced capability of an application (using Fire Risk in 3D) from the incorporation of LiDAR into the application process.

Related Reports​

 

 

Industry Level Feasibility of LiDAR Data into FRI3D, INL/RPT-22-67714247141This report presents Idaho National Lab’s work with Environmental Intellect (Ei) covering two main efforts. First, to reduce the effort of “tagging” data in large 3D models. By using both existing plant database information and artificial intelligence (AI) to find and read equipment labels. This research explores the ability to provide a simple way for the user to tag items and verify plant data, capturing both the speed of AI and human verification.This report presents Idaho National Lab’s work with Environmental Intellect (Ei) covering two main efforts. First, to reduce the effort of “tagging” data in large 3D models. By using both existing plant database information and artificial intelligence (AI) to find and read equipment labels. This research explores the ability to provide a simple way for the user to tag items and verify plant data, capturing both the speed of AI and human verification.6/30/2022 4:11:54 PMINL/RPT-22-67714 Light Water Reactor Sustainability Program Industry Level Feasibility of LiDAR Data into Fire Modeling Using Fire Risk Investigation in 3D (FRI3D) June 2022 U.S. 106https://lwrs.inl.gov/RiskInformed Safety Margin Characterization/Forms/AllItems.aspxpdfFalsepdf
Investigating Application of LiDAR for Nuclear Power Plants, INL/EXT-21-64452234760Investigating Application of LiDAR for Nuclear Power Plants, INL/EXT-21-64452Investigating Application of LiDAR for Nuclear Power Plants, INL/EXT-21-644529/29/2021 5:37:07 PMINL/EXT-21-64452 Light Water Reactor Sustainability Program INVESTIGATING APPLICATION OF LiDAR FOR NUCLEAR POWER PLANTS September 2021 DOE Office of Nuclear EnergyDISCLAIMER This 318https://lwrs.inl.gov/RiskInformed Safety Margin Characterization/Forms/AllItems.aspxpdfFalsepdf

​For more information contact:

Svetlana (Lana) Lawrence
Risk-Informed Systems Analysis, Pathway Lead
Idaho National Laboratory
LIDAR Imagery.png

3D visualization of a plant area and housed equipment using LiDAR. Such detailed presentation of the plant equipment is very useful for multiple plant activities such as plant walk downs, training, and in support of applications such as fire modeling or internal flooding modeling.