Xiao Liu

Accurate extraction of temperature-induced structural responses from bridge monitoring data is the core prerequisite for constructing a more precise regression model linking the temperature field to temperature-induced responses, whose outputs can provide reliable baseline benchmarks for the safety evaluation of bridges. Traditional wavelet multiscale decomposition methods for temperature-induced response extraction typically rely on empirical parameter selection, leading to inadequate signal separation accuracy and limited model generalization. To eliminate the black-box effect and poor generalization caused by empirical parameter selection, physically meaningful indices can be established to quantitatively assess the physical consistency between temperature-induced responses obtained from wavelet analysis under different parameters and the temperature effect. This makes the parameter selection process physically interpretable rather than empirical. Accordingly, this study proposes an optimized wavelet multiscale decomposition method based on time–frequency similarity evaluation to physically extract temperature-induced response signals. Specifically, two quantitative evaluation indices are established to characterize the time-domain and frequency-domain similarity between extracted response signals and original temperature data, respectively. The optimal parameter combination for wavelet decomposition is automatically determined by integrating the dual time–frequency similarity results, which avoids the randomness and subjectivity of artificial parameter tuning. For method validation, deep learning regression models are constructed to map temperature fields to structural responses processed via different wavelet parameter schemes. The experimental results show that the regression model corresponding to the proposed optimized method achieves the optimal predictive performance, with a coefficient of determination (R²) reaching 0.90. This outcome verifies that the time–frequency similarity maximization strategy can significantly improve the extraction accuracy of temperature-induced responses in wavelet multiscale analysis. The proposed method provides a robust and reliable data processing framework for monitoring data purification, offering a positive basis for subsequent temperature-induced response extraction and baseline modelling.