Eliminating Temperature Effect in Vibration-Based Structural Damage Detection

Abstract

False-positive or false-negative damage may be signaled by vibration-based structural damage detection methods when the environmental effects on the changes of dynamic characteristics of a structure are not accounted for appropriately. In this paper, a parametric approach for eliminating the temperature effect in vibration-based structural damage detection is proposed that is applicable to structures where dynamic properties and temperature are measured. First, a correlation model between damage-sensitive modal features and temperature is formulated with the back-propagation neural network (BPNN) technique. With the correlation model, the modal features measured under different temperature conditions are normalized to an identical reference status of temperature to eliminate the temperature effect. The normalized modal features are then applied for structural damage identification. The proposed approach is examined in the instrumented Ting Kau Bridge in Hong Kong. Using the long-term monitoring data of both modal frequencies and temperatures, a BPNN correlation model with validated generalization capability is formulated, and the normalized modal frequencies before and after damage are derived and applied for the structural damage alarm using the autoassociative neural network (AANN)–based novelty detection technique. The proposed approach is competent for eliminating the temperature effect and eschewing the false-positive damage alarm that originally occurred when using the measured modal frequencies directly. Case studies assuming damage at different structural components of the bridge are carried out to verify the proposed approach and the detectability of damage using the AANN-based novelty detection technique. The results show that the approach can detect damage when the damage-induced frequency change is as small as 1%. Nevertheless, it is worth mentioning that the frequency-based approach is most effective for detecting damage of a certain severity rather than detecting the onset of damage.