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• A novel Digital Clone Platform is developed for the assessment of SHM systems. • Uncertainties are quantified and propagated into the platform based on SHM data. • A damage model is calibrated based on recorded SHM data within a Bayesian framework. • The proposed platform is used to estimate the MAPOD of piezo-sensorized composites. The performance of a Structural Health Monitoring (SHM) system can be assessed using Probability of Detection (PoD) curves, which is a common tool for the evaluation of Non-Destructive Testing (NDT) methods. This study presents a novel digital clone platform to quantify and account for uncertainties that can be detrimental to the reliability of a SHM system. Uncertainties relating to experimental measurement noise and Environmental and Operational Conditions (EOC) are considered during the definition of a threshold value that aims at reliably distinguishing between pristine and damaged signals. At the same time, the variability of impact damage characteristics and uncertainties associated with Lamb waves interaction in composites are captured though the Bayesian calibration of a Finite Element (FE) model using experimental observations. The FE model is integrated within the digital clone testing platform to substitute the experimental testing and generate a statistical sample of distributed impact events at different locations on a composite plate and compute the Model Assisted Probability of Detection (MAPOD). This approach allows the estimation of the system's performance under different EOC that can be used during the selection and operation of a specific SHM configuration. [ABSTRACT FROM AUTHOR]