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Pan, Xiao

University of British Columbia

Civil engineering structures such as buildings and bridges inevitably experience damage due to aging effects and natural disasters such as earthquakes. Damage inspection of these structures is of vital importance to maintain their functionalities. Earlier damage identification before natural disasters can greatly alleviate or prevent catastrophic failure in the event of natural disasters. Traditional manual inspection is inefficient and highly reliant on the proper training and experience of inspectors, which may result in false conclusions and erroneous evaluation reports. In recent decades, structural health monitoring (SHM) methods such as vibration-based SHM and non-destructive testing and evaluation (NDTE) methods have been developed to automate the inspection process. These methods generally require relatively complicated and expensive instrumentation to evaluate the conditions of structures. More recently, computer vision-based (or vision-based) SHM has been established as a convenient, economical and efficient complementary approach to the other SHM methods for civil structures. In comparison to contact-type vibration sensors, vision-based methods use low-cost and non-contact sensors, and easy installation and operation. However, most existing vision-based SHM methods are built on 2D computer vision where the evaluation outcomes are sensitive to camera locations and poses. Besides, these 2D vision methods are limited to the evaluation of in-plane damages, while not directly capable of quantifying damages in 3D space. In short, existing 2D vision methods may not provide a reliable and comprehensive damage evaluation outcome. To address these limitations, this dissertation proposes a 3D vision-based SHM and loss estimation framework, which aims to provide a more rapid and comprehensive damage evaluation and loss assessment of civil structures. Within the framework, the dissertation is strongly focused on the development and application of advanced 2D and 3D vision-based SHM methods for civil structures. Experiments of the vision algorithms developed have been conducted on three prevalent structural types including reinforced concrete structures, steel structures and structural bolted components. Results show that the proposed 3D vision-based damage evaluation and loss quantification framework can achieve high accuracy and low cost in damage recognition, localization and quantification, and provide more comprehensive assessment results which can be more easily conveyed to owners and decision-makers.

Text; Still Image

2022-12-06

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/83361

Civil Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/