Major concerns for implementing a practical built-in structural health monitoring system are prediction accuracy and data reliability. It is proposed to develop robust state-of-the-art structural health management (SHM) technologies to overcome these concerns. The proposed solution will be capable of detecting and quantifying damage with a high probability of detection (POD), accurately predicting the residual strength and remaining life of the structures with confidence, and providing information which will allow appropriate preventative actions on the monitored structure. To achieve the objectives the proposed technology will first optimize the sensor network configuration for the SHM system to achieve the highest probability of detection. Next, robust diagnostic techniques will be developed to achieve quantifiable damage location and size estimation that account for the uncertainties induced by the environments or the system itself continuously during flight or at scheduled maintenance intervals. Finally, efficient probabilistic prognostic methods will be integrated with diagnostic outputs to provide real time estimation of residual strength and remaining life of the damaged structure. Both metallic and composite stiffened aircraft panels will be instrumented and tested under simulated flight conditions to validate the proposed technology. The work will be performed collaboratively between Acellent and Stanford University.