Baseline-free damage identification strategy based on FRF-WPPE-LOF framework for multi-source information fusion and sparse field inversion

The structural health monitoring (SHM) of large steel box girders often lacks baseline data, making traditional damage detection methods unsuitable for structures in long-term service. To overcome this problem, this study proposes a baseline-free frequency response function (FRF)-wavelet packet permutation entropy (WPPE)-local outlier factor (LOF) damage identification framework that integrates multi-source information fusion theory and sparse field inversion. Firstly, a composite damage indicator was constructed by integrating FRF, WPPE, and LOF, which can highlight non-stationary, frequency sensitive, and edge localization damage characteristics. In order to improve engineering interpretability and spatial robustness, an engineering prior weighting scheme based on stress distribution was introduced in the damage mapping stage, especially for the bending dominant region. Subsequently, a sparse field inversion method was developed by linking the indicators of the sensor domain with the stiffness attenuation at the unit level through weighted optimization. This makes the damage vector interpretable, thereby further deriving quantitative damage depth and severity. The proposed method was validated using a steel box girder finite element model and a triangular impact load induced broadband vibration test under healthy and three types of damage conditions. The results show that this method achieves accurate identification of damage locations, enhances sensitivity to slight and boundary damage, has strong robustness to noise and uncertainty of excitations, and does not require any baseline measurements. Due to these advantages, the proposed framework has great potential for application in large-span bridges or other large civil structures where baselines are difficult to obtain.