Computer Vision for Structural Dynamics and Health Monitoring. Dongming Feng

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Computer Vision for Structural Dynamics and Health Monitoring - Dongming Feng


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      1 In contrast to a contact‐type sensor (such as an LVDT or a string potentiometer), which requires time‐consuming, costly installation on the structure and physical connections to a stationary reference point, a computer vision sensor requires no physical access to the structure, and the camera can be set up at a convenient remote location. This represents significant savings of both time and cost. For monitoring bridges, for example, no traffic control is required. In addition, each contact‐type sensor measures one‐dimensional (1D) displacement, but a single computer vision camera can measure two‐dimensional (2D) displacements simultaneously.Figure 1.2 Vision‐based remote displacement sensor.

      2 Compared with a noncontact GPS, which requires installation on the structure (but not a stationary reference point), a vision‐based sensor is far more accurate and less expensive. Depending on the cost, the GPS measurement error is typically in the range of 5–10 mm: more than an order of magnitude larger than that of a vision sensor.

      3 Unlike a noncontact laser vibrometer, which must be placed very close to the measurement target due to the limited allowable laser power, a vision sensor can be placed hundreds of meters away (with the help of an appropriate zoom lens) and still achieve satisfactory measurement accuracy.

      4 In contrast to conventional displacement sensors, almost all of which are point‐wise sensors, a single vision sensor can simultaneously track structural displacements at multiple points. More importantly, one can easily alter the measurement points after video images are taken, offering unique flexibility for achieving better SHM results.A comparison between commonly used vibration sensors and vision‐based displacement sensors is summarized in Table 1.1.

Sensors Measure Pros Cons
Wired or wireless accelerometer Acceleration Suitable for continuous monitoringHardware easily availableSensitive to high‐frequency vibrations High cost of sensor systemHigh cost of installation and maintenanceContact sensorSingle‐point measurementAdditional mass on the structure may affect output
LVDT Displacement Hardware easily available Difficult and costly to installContact sensorOne‐dimensional measurementSingle‐point measurement
Laser vibrometer Velocity or displacement NoncontactAccurate High cost of sensor systemNot suitable for continuous monitoringLimited measurement distance
Computer vision sensor Displacement Noncontact, continuous monitoringLow‐cost industrial or consumer‐grade video camerasTwo‐ or three‐dimensional measurementMultiple flexible measurement points on the visible object surface Accuracy affected by weather, light, and camera motion

      About 10 years ago, the research community started to develop computer vision–based sensor technology for displacement measurement of large‐size structures in controlled laboratory and challenging field environments. Modal analysis can be performed on the displacement data to extract natural frequencies and the mode shapes of a structure. Moreover, by analyzing the measured displacement time histories and modal analysis results, analytical models and parameters of the structure can be updated, damage detected, and structural integrity assessed. The adoption of vision sensors can significantly reduce the testing cost and time associated with conventional instrumentations. For example, Poozesh et al. [17] pointed out that testing a typical 50 m utility‐scale wind turbine blade requires approximately 200 gages (costing $35 000–$50 000) and about three weeks to set up a conventional strain gauge system, while by contrast, a multicamera system could streamline the blade‐testing process by eliminating the sensor instrumentation and reducing the setup time to two days.

      It should be noted that computer vision sensing has been attracting attention and gaining popularity in two major areas of structural engineering: (i) vision‐based sensors for displacement measurement and their SHM applications for modal/parameter identification, damage detection, force estimation, and model validation and updating; and (ii) visual monitoring of structural surface for defect detection and condition assessment, including the use of unmanned aerial vehicles (UAVs) and machine learning techniques. The emphasis of this book is on the former application.

      The goal of this book is to encourage the application of the emerging computer vision–based sensing technology not only in scientific research but also in engineering practice such as field condition assessment of civil engineering structures and infrastructure systems. This book may serve as a textbook for graduate students, researchers, and practicing engineers. Thus much emphasis has been placed on making computer vision algorithms and their applications in structural dynamics and SHM easily accessible and understandable. To achieve this goal, throughout the book, MATLAB computer code is provided for most of the problems that are discussed. Even though the book is conceived as an entity, its chapters


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