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Development of a building information model-guided post-earthquake building inspection framework using 3D synthetic environments

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Earthquake Engineering and Engineering Vibration 2023年第2期22卷 279-307页

作者： Nathaniel M.Levine yasutaka narazaki Billie F.Spencer Department of Civil and Environmental Engineering University of Illinois at Urbana-ChampaignUrbanaIllinois 61801USA Zhejiang University-University of Illinois at Urbana-Champaign Institute Zhejiang UniversityHaining 314400China

Computer vision-based inspection methods show promise for automating post-earthquake building *** methods survey a building with unmanned aerial vehicles and automatically detect damage in the collected ***,assessing the damage′s impact on structural safety requires localizing damage to specific building components with known design and *** paper proposes a BIM-based automated inspection framework to provide context for visual surveys.A deep learning-based semantic segmentation algorithm is trained to automatically identify damage in *** BIM automatically associates any identified damage with specific building ***,components are classified into damage states consistent with component fragility models for integration with a structural *** demonstrate the framework,methods are developed to photorealistically simulate severe structural damage in a synthetic computer graphics environment.A graphics model of a real building in Urbana,Illinois,is generated to test the framework;the model is integrated with a structural analysis to apply earthquake damage in a physically realistic manner.A simulated UAV survey is flown of the graphics model and the framework is *** method achieves high accuracy in assigning damage states to visible structural *** assignment enables integration with a performance-based earthquake assessment to classify building safety.

关键词： computer vision earthquake engineering building information model physics-based graphics model synthetic environment

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Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring

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Engineering 2019年第2期5卷 199-222页

作者： Billie F. Spencer Jr. Vedhus Hoskere yasutaka narazaki Department of Civil and Environmental Engineering University of Illinois at Urbana-Champaign Urbana IL 61801-2352 USA Department of Computer Science University of Illinois at Urbana-Champaign Urbana IL 61801-2302 USA

Computer vision techniques, in conjunction with acquisition through remote cameras and unmanned aerial vehicles (UAVs), offer promising non-contact solutions to civil infrastructure condition assessment. The ultimate goal of such a system is to automatically and robustly convert the image or video data into actionable information. This paper provides an overview of recent advances in computer vision techniques as they apply to the problem of civil infrastructure condition assessment. In particular, relevant research in the fields of computer vision, machine learning, and structural engineering is presented. The work reviewed is classified into two types: inspection applications and monitoring applications. The inspection applications reviewed include identifying context such as structural components, characterizing local and global visible damage, and detecting changes from a reference image. The monitoring applications discussed include static measurement of strain and displacement, as well as dynamic measurement of displacement for modal analysis. Subsequently, some of the key challenges that persist toward the goal of automated vision-based civil infrastructure and monitoring are presented. The paper concludes with ongoing work aimed at addressing some of these stated challenges.

关键词： Structural inspection and monitoring Artificial intelligence Computer vision Machine learning Optical flow

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Fusion of thermal and RGB images for automated deep learning based crack detection in civil infrastructure

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AI in Civil Engineering 2022年第1期1卷 21-30页

作者： Quincy G.Alexander Vedhus Hoskere yasutaka narazaki Andrew Maxwell Billie F.Spencer Jr US Army Engineer Research and Development Center 3909 Halls Ferry RdVicksburgMS 39180USA Department of Civil and Environmental Engineering University of Houston4726 Calhoun RoadHoustonTX 77204USA Zhejiang University/University of Illinois at Urbana-Champaign Institute Room C323Engineering Building718 East Haizhou RoadHaining 314400ZhejiangChina Department of Civil and Environmental Engineering University of Illinois-Urbana Champaign205 N Mathews AveUrbanaIL 61801USA

Research has been continually growing toward the development of image-based structural health monitoring tools that can leverage deep learning models to automate damage detection in civil ***,these tools are typically based on RGB images,which work well under ideal lighting conditions,but often have degrading performance in poor and low-light *** the other hand,thermal images,while lacking in crispness of details,do not show the same degradation of performance in changing lighting *** potential to enhance automated damage detection by fusing RGB and thermal images together within a deep learning network has yet to be *** this paper,RGB and thermal images are fused in a ResNET-based semantic segmentation model for vision-based inspections.A convolutional neural network is then employed to automatically identify damage defects in *** model uses a thermal and RGB encoder to combine the features detected from both spectrums to improve its performance of the model,and a single decoder to predict the *** results suggest that this RGB-thermal fusion network outperforms the RGB-only network in the detection of cracks using the Intersection Over Union(IOU)performance *** RGB-thermal fusion model not only detected damage at a higher performance rate,but it also performed much better in differentiating the types of damage.

关键词： Infrared thermography Structural health monitoring Image fusion Automated crack detection

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