FILTERED ACCELERATION MEASUREMENTS AS INDICATORS OF BRIDGE CONDITION

Abstract eng:
There has been increasing interest in recent years in the development of Structural Health Monitoring (SHM) approaches and systems of sensor measurement that give an indication of the health of a bridge. Many sensor types have been used in test installations, with varying degrees of success. Strain gauges and transducers are a well established technology and sufficiently accurate sensors are available at a reasonable cost. However, strain transducers can only detect damage if it occurs at (or very close to) the point of measurement. Deflection at any point on a bridge is a function of the flexural stiffness at all points. As such, it has the potential to provide an indication of damage at any point. However, deflection can be difficult and/or expensive to measure. Laser vibrometers measure the derivative of deflection with respect to time, i.e., velocity, but they are expensive and it seems impractical to deploy large numbers on smaller bridges in particular. Acceleration is the 2nd derivative of deflection so it is, in theory at least, damage sensitive. Furthermore, accelerometers are widely available and can provide accurate measurements at a reasonable cost. This paper reports on the use of acceleration measurements for bridge monitoring and damage detection. Traffic induced acceleration on a bridge is the result of a range of excitations. The signal is influenced by a number of vehicle-related factors such as speed, inter-axle spacing and tyre and suspension properties. In this project, the portion of the acceleration signal in the region of the bridge first natural frequency is filtered from the raw input in order to amplify the portion of the signal most likely to be influenced by bridge damage. Vehicle/bridge dynamic interaction simulations are used to show the nature of the response, before and after filtering. It is shown that the filtered signal is considerably more sensitive to bridge damage than the original raw signal and has good potential for bridge health monitoring.

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