Abstract. A wireless sensing system is designed for application to structural monitoring and damagedetection applications. Embedded in the wireless monitoring module is a two-tier prediction model, the autoregressive(AR) and the autoregressive model with exogenous inputs (ARX), used to obtain damage sensitivefeatures of a structure. To validate the performance of the proposed wireless monitoring and damage detectionsystem, two near full scale single-story RC-frames, with and without brick wall system, are instrumented withthe wireless monitoring 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. Patternclassification methods are then adopted to classify the structure as damaged or undamaged using time seriescoefficients as entities of a damage-sensitive feature vector. The demonstration of the damage detectionmethodology is shown to be capable of identifying damage using a wireless structural monitoring system. Theaccuracy and sensitivity of the MEMS-based wireless sensors employed are also verified through comparisonto data recorded using a traditional wired monitoring system
Abstract. A wireless sensing system is designed for application to structural monitoring and damagedetection applications. Embedded in the wireless monitoring module is a two-tier prediction model, the autoregressive(AR) and the autoregressive model with exogenous inputs (ARX), used to obtain damage sensitivefeatures of a structure. To validate the performance of the proposed wireless monitoring and damage detectionsystem, two near full scale single-story RC-frames, with and without brick wall system, are instrumented withthe wireless monitoring 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. Patternclassification methods are then adopted to classify the structure as damaged or undamaged using time seriescoefficients as entities of a damage-sensitive feature vector. The demonstration of the damage detectionmethodology is shown to be capable of identifying damage using a wireless structural monitoring system. Theaccuracy and sensitivity of the MEMS-based wireless sensors employed are also verified through comparisonto data recorded using a traditional wired monitoring system