The present book falls within the research field of Structural Health Monitoring (SHM), with specific reference to the civil engineering context. Nowadays, SHM-based approaches and the attached development of a consistent numerical modeling, with related model updating, may constitute fundamental tools to pursue the goal of structural safety, preventing possible causes of damage that may even lead to structural failure. In particular, this research work proposes complementary post-processing approaches to address the issue of noise cleaning on dynamic structural response signals typically encountered in structural engineering applications. Two approaches are mainly presented, especially aiming at enhancing displacement response signals. Heterogeneous Data Fusion (HDF) procedures, which involve a Kalman Filter (KF)-based implementation, are primarily investigated, by integrating data acquired from different types of sensors, so that the resulting information turns out to be characterized by a lower degree of uncertainty. A denoising approach is also inspected, as the process through which a source signal may be reconstructed, starting from a recorded, noise-affected one, by removing its noisy part, without losing the useful information incorporated within it. A HDF procedure and a denoising approach are then combined within an integrated strategy, in an effort to enhance the reliability of the monitoring process, for assessing the health conditions of bridges. Both synthetic and real response signals are considered, as well as signals displaying a different dynamical nature (non-stationary vs. stationary). These processed signals are finally employed toward modal identification purposes, for extracting the modal properties of the monitored structure.

(2022). Modal dynamic identification of civil structures via inverse analysis based on Heterogeneous Data Fusion and post-processing . Retrieved from http://hdl.handle.net/10446/227490

Modal dynamic identification of civil structures via inverse analysis based on Heterogeneous Data Fusion and post-processing

Ravizza, Gabriele
2022

Abstract

The present book falls within the research field of Structural Health Monitoring (SHM), with specific reference to the civil engineering context. Nowadays, SHM-based approaches and the attached development of a consistent numerical modeling, with related model updating, may constitute fundamental tools to pursue the goal of structural safety, preventing possible causes of damage that may even lead to structural failure. In particular, this research work proposes complementary post-processing approaches to address the issue of noise cleaning on dynamic structural response signals typically encountered in structural engineering applications. Two approaches are mainly presented, especially aiming at enhancing displacement response signals. Heterogeneous Data Fusion (HDF) procedures, which involve a Kalman Filter (KF)-based implementation, are primarily investigated, by integrating data acquired from different types of sensors, so that the resulting information turns out to be characterized by a lower degree of uncertainty. A denoising approach is also inspected, as the process through which a source signal may be reconstructed, starting from a recorded, noise-affected one, by removing its noisy part, without losing the useful information incorporated within it. A HDF procedure and a denoising approach are then combined within an integrated strategy, in an effort to enhance the reliability of the monitoring process, for assessing the health conditions of bridges. Both synthetic and real response signals are considered, as well as signals displaying a different dynamical nature (non-stationary vs. stationary). These processed signals are finally employed toward modal identification purposes, for extracting the modal properties of the monitored structure.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/227490
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