5 research outputs found
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Computer Vision Sensing Systems for Structural Health Monitoring in Challenging Field Conditions
Computer vision sensing techniques enable easy-to-install and remote non-contact monitoring of structures and have great potentials in field applications. This study will develop/implement novel computer vision techniques for two sensing systems for monitoring different aspects of infrastructures in challenging field conditions. The dissertation is therefore composed of two parts: robust measurement of global multi-point structural displacements, and accurate and robust monitoring of local surface displacements/strains.
Computer vision based displacement measurement has become popular in the recent decade. The first part presents InnoVision, a vision sensing system developed to address a number of challenging problems associated with applying vision sensors to the measurement of multi-point structural displacement in field conditions that are rarely comprehensively studied in the literature. The challenging problems include tracking low-contrast natural targets on the structural surface, insufficient resolution for long distance measurement, inevitable camera vibration, and image distortion due to heat haze in hot weather. Several techniques are developed in InnoVision to tackle these challenges. Laboratory and field tests are conducted to evaluate the performance of these techniques.
In the second part, another vision sensing system SurfaceVision is developed for accurate and robust monitoring two-dimensional (2D) structural surface displacements/strains. Important structures, such as nuclear power plants, need the continuous inspection of surface conditions. As an alternative to the human inspection, conventional digital-image-correlation (DIC) based methods have been applied to surfaces painted with speckle patterns in a controlled environment. However, it is highly challenging for DIC methods to accurately measure displacement on natural concrete surfaces in outdoor conditions with changing illumination and weather conditions. Additionally, common surface displacement measurement is based on segmenting the surface image into small subsets and tracking each subset individually through template matching, the surface displacement thus obtained has obvious discontinuity and low spatial resolution. Therefore, for applicability in the outdoor environment, SurfaceVision is proposed for accurate and robust monitoring of surface displacements/strains. Advanced computer vision techniques are developed/implemented to enable surface displacement measurement with high continuity, spatial resolution, accuracy, and robustness. An intuitive strain calculation method is also developed for converting surface displacements into surface strains. A numerical simulation is formulated based on four-point bending tests to validate the accuracy and robustness of SurfaceVision in surface displacements. Four-point bending experiments using reinforced concrete specimens are conducted to demonstrate the performance of SurfaceVision under different cases of optical noises and its effectiveness in predicting crack formations
Micro Displacement and Strain Detection for Crack Prediction on Concrete Surface Using Optical Nondestructive Evaluation Methods
Continuous inspection of the concrete structures is important for extending the operating life span of nuclear power plants. Restricted physical accessibility to the nuclear plant structures, due to concerns of radiation, presents a unique challenge to the conventional visual inspection and contact-type nondestructive evaluation (NDE) technologies. Digital image correlation (DIC) is an optical NDE method that can measure the structural parameters such as displacement and strain. However, it is highly challenging to accurately detect micro displacement on the concrete surface because of weathering and change in illumination conditions. Usually, an artificial speckle pattern with good contrast to the object surface is needed for calibration and tracking, but it is difficult to apply in the field. In order to be able to detect micro surface strain for crack prediction in outdoor environment, a DIC-based NDE technology is developed to significantly improve the measurement accuracy by incorporating the orientation code matching (OCM) technique, a robust and accurate template matching algorithm. Concrete specimens were built and tested under four-point bending. Using the remotely measured images, the OCM incorporated DIC method successfully predicted concrete cracks before they emerged on the surface. The experiments also demonstrated the robustness of the method against the optical noise including weathering and change in illumination conditions
Micro Displacement and Strain Detection for Crack Prediction on Concrete Surface Using Optical Nondestructive Evaluation Methods
Continuous inspection of the concrete structures is important for extending the operating life span of nuclear power plants. Restricted physical accessibility to the nuclear plant structures, due to concerns of radiation, presents a unique challenge to the conventional visual inspection and contact-type nondestructive evaluation (NDE) technologies. Digital image correlation (DIC) is an optical NDE method that can measure the structural parameters such as displacement and strain. However, it is highly challenging to accurately detect micro displacement on the concrete surface because of weathering and change in illumination conditions. Usually, an artificial speckle pattern with good contrast to the object surface is needed for calibration and tracking, but it is difficult to apply in the field. In order to be able to detect micro surface strain for crack prediction in outdoor environment, a DIC-based NDE technology is developed to significantly improve the measurement accuracy by incorporating the orientation code matching (OCM) technique, a robust and accurate template matching algorithm. Concrete specimens were built and tested under four-point bending. Using the remotely measured images, the OCM incorporated DIC method successfully predicted concrete cracks before they emerged on the surface. The experiments also demonstrated the robustness of the method against the optical noise including weathering and change in illumination conditions