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This book presents a non-destructive automated damage detection system for the Armored Vehicle Launched Bridge. The goal of the system is to automate the damage detection procedure and enable early detection of structural damage. The book covers both practical design issues and new theoretical developments in the area of non-destructive damage detection using strain energy mode shapes. Provided is a detailed description of the bridge testing in sinusoidal dwell and random burst modes of operation. The book also covers traditional and a novel non-baseline variation of the strain energy method.…mehr

Produktbeschreibung
This book presents a non-destructive automated damage detection system for the Armored Vehicle Launched Bridge. The goal of the system is to automate the damage detection procedure and enable early detection of structural damage. The book covers both practical design issues and new theoretical developments in the area of non-destructive damage detection using strain energy mode shapes. Provided is a detailed description of the bridge testing in sinusoidal dwell and random burst modes of operation. The book also covers traditional and a novel non-baseline variation of the strain energy method. Also described is a fuzzy expert system that detects and localizes damage on noisy data. Investigated theoretical issues present new developments in the area of damage detection using strain energy mode shapes. An analytical study of the numerical properties of the strain energy processing allows proper selection of the sampling interval during acquisition of the displacement mode shapes, minimizing the effects of measurement noise and providing maximum sensitivity to damage, while improving the accuracy of damage location.
Autorenporträt
Edward Sazonov is an Associate Professor in the department of Electrical and Computer Engineering, Clarkson University, Potsdam, NY and the head of the Laboratory of Ambient and Wearable Devices. His research interests are focused on wireless, ambient and wearable devices including self-powered sensors for structural health monitoring of bridges.