Abstract Summary
The present contribution outlines a general methodological procedure for a consistent signal processing analysis of vibration response data, combining both classic and advanced techniques, toward structural monitoring and identification scopes. A specific case study is considered, for testing and validation, regarding the structural assessment of a historical infrastructure, a road three-span reinforced concrete arch bridge. The innovative contributions of the present research highlight four main points, in an integrated approach: the adoption of a Time Domain Compression technique, the application of a low-pass filter, the development of a Wavelet Analysis and the employment of an ARMA modelling approach. The analysis of acquired response signals through a Time Domain Compression technique allows, for set filtering parameter and threshold, to remove lower quality sub-samples from the full data. After such a step, the modified signal, to be employed in subsequent analyses, displays a higher quality, though being possibly endowed by a non-stationarity character. The information content of such signals can further be improved in quality by the adoption of a low-pass filter, with an intrinsic denoising effect, with respect to the characteristic natural frequencies expected for the structure under consideration. An advantageous contribution is provided by a Wavelet Analysis applied to the response data signals, offering, through a Wavelet transform, a further filtering effect based on frequency localisation within the time domain. A proper managing of the Wavelet family choice and leakage filtering issue may lead to a refined, higher quality, reconstructed signal, specifically useful in sub-sample identification and stationarity characterisation. A further deepening in response signal analysis and understanding is obtained by an ARMA modelling approach, with unknown source input (consistently with unknown loading configuration provided by operational traffic conditions on the bridge of the case study). In particular, the polynomial function applied to a white noise source in the model is interpreted as a filtering term apt to transform the source in a non-white noise configuration, relevant to the application case, therefore allowing for effective deciphering of the transfer function features as properties of the investigated structure. The considered approaches and whole methodology are extensively validated on the specific case study at hand and the presented results allow to derive effective observations regarding the current structural behaviour of the reinforced concrete bridge, while the study of the devised methodology shall highlight its general applicability with reference also to other structural setups. The combination of consolidated statistical signal processing analysis with advanced techniques provides a devoted methodological procedure to extract reliable structural properties of current condition assessment toward Structural Health Monitoring and intervention purposes, outlining a robust and efficient management monitoring platform.
