This paper presents a computational methodology for detecting coherent structural transitions in gravitational-wave signals using the Scalar Drag Emergence Framework (SDEF). The approach transforms raw strain data into a persistence-based representation and identifies localized regions of structural dynamics, referred to as corridors, through analysis of coherence and temporal persistence. A six-layer signal-processing pipeline is introduced, combining time-delay embedding, covariance-based coherence measures, persistence estimation, and flow-based event detection. Structural transitions are identified as peaks in the corridor dynamics and grouped using desnity-based clustering algorithm (DBSCAN), producing a compact representation of temporal organization through cluster count. Applied to a set of gravitational-wave events, the method reveals multi-phase structural behaviour, with cluster counts ranging from single-stage to highly segmented evolution. A strong positive correlation between cluster count and chirp mass is observed, indicating that higher-mass systems exhibit more complex sequences of structural transitions. The methodology operates without reliance on waveform templates or assumed merger timing, providng a model-independent approach to extracting structure directly from the signal. The full implementation is available as a versioned code release, eneabling reproducibility and independent validation. The work establishes an operational link between structural dynamics and observable signal features, complementing interpretive frameworks that describe evolution in terms of transport pathway reconfiguration.
Pej Evan Bartolo (Sun,) studied this question.