This study advances Structural Health Monitoring (SHM) by leveraging infodynamics, a novel approach that measures the information extracted from SHM raw data. Unlike conventional methods that rely on distances or statistical moments, this approach uses the information theory to interpret the stochastic nature of data as a key metric for quantifying informational content. Redundant data lack informational value, whereas stochasticity from external events necessitates real-time monitoring to identify structural health changes. A central question in SHM is determining when sufficient information is acquired for reliable damage assessment. This research explores Shannon’s formulation for measuring information, termed “informature,” which is sensitive to the entire data distribution and lays the foundation for new infodynamic strategies in SHM. To validate this approach, we applied informational analysis to dynamic data from the Z24 Bridge in Switzerland before its demolition, part of the Brite EuRam BE-3157 project “System Identification to Monitor Civil Engineering Structures” (SIMCES). The data included accelerometer readings that reflected the bridge’s response to the induced damage. The results demonstrate the effectiveness of “informature” in capturing sudden and gradual changes in the accelerometer data, highlighting the potential for real-time assessment of the correlation between “informature” and structural damage indicators, marking a significant advancement in SHM.