Digital Twins as Testbeds for Vision-Based Post-earthquake Inspections of Buildings

Vedhus Hoskere, Yasutaka Narazaki, Billie F. Spencer

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Manual visual inspections typically conducted after an earthquake are high-risk, subjective, and time-consuming. Delays from inspections often exacerbate the social and economic impact of the disaster on affected communities. Rapid and autonomous inspection using images acquired from unmanned aerial vehicles offer the potential to reduce such delays. Indeed, a vast amount of research has been conducted toward developing automated vision-based methods to assess the health of infrastructure at the component and structure level. Most proposed methods typically rely on images of the damaged structure, but seldom consider how the images were acquired. To achieve autonomous inspections, methods must be evaluated in a comprehensive end-to-end manner, incorporating both data acquisition and data processing. In this paper, we leverage recent advances in computer generated imagery (CGI) to construct a 3D synthetic environment with a digital twin for simulation of post-earthquake inspections that allows for comprehensive evaluation and validation of autonomous inspection strategies. A critical issue is how to simulate and subsequently render the damage in the structure after an earthquake. To this end, a high-fidelity nonlinear finite element model is incorporated in the synthetic environment to provide a representation of earthquake-induced damage; this finite element model, combined with photo-realistic rendering of the damage, is termed herein a physics-based graphics models (PBGM). The 3D synthetic environment with PBGM as a digital twin provides a comprehensive end-to-end approach for development and validation of autonomous post-earthquake strategies using UAVs.

Original languageEnglish (US)
Title of host publicationEuropean Workshop on Structural Health Monitoring - EWSHM 2022 - Volume 2
EditorsPiervincenzo Rizzo, Alberto Milazzo
Number of pages11
ISBN (Print)9783031072574
StatePublished - 2023
Event10th European Workshop on Structural Health Monitoring, EWSHM 2022 - Palermo, Italy
Duration: Jul 4 2022Jul 7 2022

Publication series

NameLecture Notes in Civil Engineering
Volume254 LNCE
ISSN (Print)2366-2557
ISSN (Electronic)2366-2565


Conference10th European Workshop on Structural Health Monitoring, EWSHM 2022


  • Autonomous inspections
  • Computer vision
  • Deep learning
  • Digital twins
  • Physics-based graphics models

ASJC Scopus subject areas

  • Civil and Structural Engineering


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