Robust Mitigation of Eovs Using Multivariate Nonlinear Regression within a Vibration-Based Shm Methodology

33 Pages Posted: 13 Mar 2023

See all articles by Callum Roberts

Callum Roberts

affiliation not provided to SSRN

Luis David Avendaño-Valencia

University of Southern Denmark

David García Cava

University of Edinburgh - School of Engineering

Abstract

A significant issue that has plagued data-driven Vibration-based Structural Health Monitoring (VSHM) is the mitigation of Environmental and Operational Variations (EOVs). The Damage Sensitive Features (DSFs) that are obtained from the vibration response of the structure are influenced by EOVs. Regression analysis, such as multivariate nonlinear regression, can be used to create relationships between Environmental and Operational Parameters (EOPs) and the DSFs, with EOV-insensitive DSFs created by taking the regression residuals. Inherent issues, originating from nuances in their design, exist within the design of the regression models, following from the overall uncertainty and redundancy in predictors and explained variables, leading to poor performance. To overcome this, a comprehensive nonlinear stepwise regression methodology has been developed to scour the regression models of as much uncertainty as possible. The proposed methodology addresses a number of crucial ideas: removing co-nonlinear variables, identifying the most influential EOPs, facilitating the selection of compact regression bases and determining which DSFs should be regressed. Robust DSFs are created by combining non-regressed DSFs with critically thought-out regressed DSFs. Ultimately, reducing the uncertainty within the models will lead to more confidence in the decision making within a VSHM methodology.

Keywords: Multivariate nonlinear regression, structural health monitoring, environmental and operational variations, nonlinear stepwise regression

Suggested Citation

Roberts, Callum and Avendaño-Valencia, Luis David and García Cava, David, Robust Mitigation of Eovs Using Multivariate Nonlinear Regression within a Vibration-Based Shm Methodology. Available at SSRN: https://ssrn.com/abstract=4387099 or http://dx.doi.org/10.2139/ssrn.4387099

Callum Roberts

affiliation not provided to SSRN ( email )

No Address Available

Luis David Avendaño-Valencia

University of Southern Denmark ( email )

David García Cava (Contact Author)

University of Edinburgh - School of Engineering ( email )

Robert Stevenson Road
The King's Buildings
Edinburgh, Scotland EH9 3FB
United Kingdom

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