Damage prognosis for structural health monitoring is a challenging and complex research topic in civil engineering. Critical components of damage detection are identifying the location and severity of damage in a structure, as well as its global effect on the structure. Local damage can increase over time and have additional adverse effects on the entire structure. Traditional damage detection methods using sensor data are effective in recognizing the change in global properties of a structure. However, these methods are neither effective nor sensitive in identifying local damage. The use of dense clustered sensor networks provides promising applications in analysis of structural components and identifying local damage. In this study, a prototype beam-column connection was constructed and instrumented by a dense sensor network. The column ends of the test specimen have fixed connections, and the beam cantilevers from the centerline of the column. The beam was excited with an actuator at its free end, and accelerometer sensors measured the response of the members to dynamic excitations at several locations along the specimen. The response at each sensor location was compared to that of other locations and pair-wise influence coefficients were estimated. Damage is introduced to the system by replacing a portion of the beam element with a smaller section, and thus reducing its stiffness. New influence coefficients were calculated and compared to the undamaged values. By statistically comparing the change in influence coefficients, the damage is accurately and effectively identified.
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