Framework for Post-Wildfire Investigation of Buildings: Integrating LiDAR Data and Numerical Modeling

Abstract

Post-wildfire investigations of structures provide engineers with data to understand how to harden building infrastructure for future wildfire hazards. This paper presents a preliminary framework where field-collected LiDAR and drone data is integrated with numerical models for post-wildfire assessment of buildings with the goal of learning from wildfire-damaged buildings. The developed framework provides a new and novel approach for post-wildfire instigations of buildings using field-collected data, particularly LiDAR and drone data. This framework is demonstrated on a metal-framed school building in Paradise, California that was heavily damaged in the 2018 Camp Fire. The authors collected drone aerial images and light detection and ranging (LiDAR) scans of Achieve Charter High School after the 2018 Camp Fire. All data were used to create a system-level finite element model to simulate the fire behavior of a portal frame with non-prismatic members. A parametric study was performed with three typical compartment fires to examine the influence of building characteristics on the fire’s maximum temperature and duration. Several time–temperature (T–t) curves, representing different fire scenarios, were generated between the two extreme cases produced by the parametric study. Finally, the finite element model was subjected to the generated fire curves, and the results were compared with the measured deformations from the LiDAR data collected in the field. The finite element model, subjected to each fire Time–temperature curve scenario, simulated global deformations within 2% of the true deformations.