A wireless sensing system is designed for application to structural monitoring and damagedetection applications. Embedded in the wireless monitoring module is a two-tier predictionmodel, the auto-regressive (AR) and the autoregressive model with exogenous inputs (ARX),used to obtain damage sensitive features of a structure. To validate the performance of theproposed wireless monitoring and damage detection system, two near full scale single-storyRC-frames, with and without brick wall system, are instrumented with the wirelessmonitoring system for real time damage detection during shaking table tests. White noise andseismic ground motion records are applied to the base of the structure using a shaking table.Pattern classification methods are then adopted to classify the structure as damaged orundamaged using time series coefficients as entities of a damage-sensitive feature vector. Thedemonstration of the damage detection methodology is shown to be capable of identifyingdamage using a wireless structural monitoring system. The accuracy and sensitivity of theMEMS-based wireless sensors employed are also verified through comparison to datarecorded using a traditional wired monitoring system
A wireless sensing system is designed for application to structural monitoring and damagedetection applications. Embedded in the wireless monitoring module is a two-tier predictionmodel, the auto-regressive (AR) and the autoregressive model with exogenous inputs (ARX),used to obtain damage sensitive features of a structure. To validate the performance of theproposed wireless monitoring and damage detection system, two near full scale single-storyRC-frames, with and without brick wall system, are instrumented with the wirelessmonitoring system for real time damage detection during shaking table tests. White noise andseismic ground motion records are applied to the base of the structure using a shaking table.Pattern classification methods are then adopted to classify the structure as damaged orundamaged using time series coefficients as entities of a damage-sensitive feature vector. Thedemonstration of the damage detection methodology is shown to be capable of identifyingdamage using a wireless structural monitoring system. The accuracy and sensitivity of theMEMS-based wireless sensors employed are also verified through comparison to datarecorded using a traditional wired monitoring system