<p>The natural and accidental deterioration of civil structures, combined with the increasing complexity of engineering projects, demands continuous monitoring strategies to ensure structural safety and durability. In this context, Structural Health Monitoring (SHM) techniques based on variations in modal properties have been widely investigated due to their capability for global assessment. However, practical limitations—such as information loss during modal identification and the low sensitivity of certain modal parameters to damage—have motivated the development of alternative approaches. Among these approaches, methodologies based on statistical features extracted directly from acceleration time series have gained prominence, particularly when combined with Machine Learning (ML) classifiers. Although several studies have successfully employed statistical indicators for damage detection, the relative relevance of these indicators and the potential benefits of dimensionality reduction can still be explored through a systematic evaluation, particularly under Sim-to-Real conditions. This study investigates the performance of ten statistical indicators as input features for ML-based damage detection, applied to an experimentally tested frame structure, using numerically obtained data for training. A structured variable analysis was conducted to assess the individual and combined impact of the indicators, supported by visualization techniques and classifier performance metrics. The results showed some possible redundancies between certain indicators and demonstrated that a reduced set of attributes can achieve comparable or superior classification performance, while simultaneously improving training stability and computational efficiency. These findings highlight the importance of systematic feature selection and the effectiveness of reduced-dimensional statistical representations for SHM applications in Sim-to-Real scenarios.</p>