Comparison Metrics for Time-histories: Application to Bridge Aerodynamics

Wind effects can be critical for the design of lifelines such as long-span bridges. The existence of a significant number of aerodynamic force models, used to assess the performance of bridges, poses an important question regarding their comparison and validation. This study utilizes a unified set of metrics for a quantitative comparison of time-histories in bridge aerodynamics with a host of characteristics. Accordingly, nine comparison metrics are included to quantify the discrepancies in local and global signal features such as phase, time-varying frequency and magnitude content, probability density, nonstationarity and nonlinearity. Among these, seven metrics available in the literature are introduced after recasting them for time-histories associated with bridge aerodynamics. Two additional metrics are established to overcome the shortcomings of the existing metrics. The performance of the comparison metrics is first assessed using generic signals with prescribed signal features. Subsequently, the metrics are applied to a practical example from bridge aerodynamics to quantify the discrepancies in the aerodynamic forces and response based on numerical and semi-analytical aerodynamic models. In this context, it is demonstrated how a discussion based on the set of comparison metrics presented here can aid a model evaluation by offering deeper insight. The outcome of the study is intended to provide a framework for quantitative comparison and validation of aerodynamic models based on the underlying physics of fluid-structure interaction. Immediate further applications are expected for the comparison of time-histories that are simulated by data-driven approaches

Defining the Wavelet Bispectrum

Bispectral analysis is an eective signal processing tool for analysing interactions between oscillations, and has been adapted to the continuous wavelet transform for time-evolving analysis of open systems. However, one unaddressed question for the wavelet bispectrum is quantication of the bispectral content of an area of scale-scale space. This makes the capacity for quantitative rather than merely qualitative interpretation of wavelet bispectrum computations very limited. In this paper, we overcome this limitation by providing suitable normalisations of the wavelet bispectrum formula that enable it to be treated as a density to be integrated. These are roughly analogous to the normalisation for second-order wavelet spectral densities. We prove that our denition of the wavelet bispectrum matches the traditional bispectrum of sums of sinusoids, in the limit as the frequency resolution tends to innity. We illustrate the improved quantitative power of our denition with numerical and experimental data