Damage causes the dynamic structural responses of civil engineering structures to change from linear to nonlinear. It can be challenging to break down signals and identify features, mainly when the data is generated by a nonlinear system and is nonstationary. Under heavy loads and during routine operations, civil structures have been seen to exhibit nonlinear dynamic characteristics. To assess progressive damage, it is necessary to characterize the time-varying attribute of the structure’s nonlinearity and consider how the frequency and amplitude contents of nonstationary vibration responses change over time. The properties of a nonstationary signal cannot be properly described by either time analysis or frequency analysis alone. When measured data include structural damage occurrences, it is critical to extract as much information about the damage as possible from the data. To create a reliable damage detection method that captures damage progression using vibration data gathered by sensors, this work examines the instantaneous frequency (IF) representation utilizing time-frequency distributions of the energy density domain based on short-time Fourier transform, wavelet transform, Hilbert-Huang transform, Wigner-Ville distribution, and synchrosqueezed transform. Each technique capability is validated using various experimental data. It is found that both the synchrosqueezed transform and Wigner distribution proved to be the best performance in terms of IF tracking and showed particular promise due to their spectral energy concentration with the synchrosqueezing transform outdoing other techniques in terms of computation precision.