Development of Virtual Visual Sensor Applications for Wood Structural Health Monitoring

Inspection techniques for wood structures are typically based on visual observations of degradation, and often limited to localized observable surface damage on the structure, rather than holistic non-destructive evaluations. Hidden deterioration and overall impacts of distributed damage may not be included in the assessment. This research evaluates the implementation of Eulerian-based virtual visual sensors (VVS), applying commercially available digital video cameras, to characterize dynamic structural response of wood structures. Natural vibration frequencies are determined by monitoring the intensity value of a single fixed pixel coordinate over a few seconds of a video of structural vibration and then applying a fast Fourier transform to estimate signal frequencies. Changes in stiffness and mass of materials and structural systems that may relate to deterioration are reflected in the natural frequencies. The end goal is development and application of VVS to wood structures to obtain information relevant to objective structural health monitoring (SHM). In this development phase, the effects of moisture content and simulated damage on natural frequencies are observed on simply supported beams of dimensional lumber. Initial applications are also made to an in-place U.S. Forest Service pedestrian bridge. Results show comparable accuracy in determining vibrational frequencies with VVS and a commercially available transverse vibration measurement system, successful observation of vibrational frequencies in a timber bridge, and beneficial use of naturally occurring color gradients in wood structures in laboratory and field tests. Moisture content and simulated damage have measurable effects on natural frequencies. Eulerian-based VVS show potential as a tool for cost-effective SHM of wood structures, especially for quick, global screening of structures, with subsequent visual inspection and other means of evaluation.

[1]  Francesco Augelli,et al.  In situ assessment of structural timber using non-destructive techniques , 2014 .

[2]  Anil K. Chopra,et al.  Dynamics of Structures: Theory and Applications to Earthquake Engineering , 1995 .

[3]  W. F. Ranson,et al.  Determination of displacements using an improved digital correlation method , 1983, Image Vis. Comput..

[4]  Derek Nowrouzezahrai,et al.  Learning hatching for pen-and-ink illustration of surfaces , 2012, TOGS.

[5]  Prof. Voichita Bucur Nondestructive Characterization and Imaging of Wood , 2003, Springer Series in Wood Science.

[6]  Julian Padget,et al.  Virtual visual sensors and their application in structural health monitoring , 2014 .

[7]  Bohumil Kasal,et al.  Structural health assessment of in situ timber: An interface between service life planning and timber engineering , 2014 .

[8]  S. Choi,et al.  Measurement of deformations on concrete subjected to compression using image correlation , 1997 .

[9]  Frédo Durand,et al.  Eulerian video magnification for revealing subtle changes in the world , 2012, ACM Trans. Graph..

[10]  Charles R. Farrar,et al.  Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review , 1996 .

[11]  Christer Sjöström,et al.  State-of-the-art report , 1997 .

[12]  Thomas Schumacher,et al.  Monitoring of Structures and Mechanical Systems Using Virtual Visual Sensors for Video Analysis: Fundamental Concept and Proof of Feasibility , 2013, Sensors.