Complex engineered systems manifest in mechanical, aerospace, civil, earthquake and bio engineering as well as in related engineering disciplines. Complexities are linked to associated uncertainties, arising from several assumptions and compromises, including those on material constitutive laws, description of loads, influence of operational and environmental factors, energy dissipation mechanisms, motion constraints or large displacements of system components. The propagation of these uncertainties adversely affects simulation accuracy and, consequently, the design, operation and maintenance decisions for meeting desirable system performance and safety requirements. Measured data from component tests or field monitoring may be employed to narrow these uncertainties and produce system models, or surrogates of the true systems, of higher fidelity. The objective of this Research Topic is to present recent advances and emerging cross-disciplinary approaches in the broad field of structural health monitoring with a focus on novel techniques for a) uncertainty modeling and quantification, and for b) robust diagnostic tools for operation and maintenance.
Specific contributions related both to fundamental research and to engineering applications of advanced signal processing techniques and health monitoring algorithms for condition assessment, damage detection and reliability prognosis are welcome. A non-exhaustive list includes data-driven models for dynamics and vibrations of linear & nonlinear structural systems; robust outlier detection methods; probabilistic (e.g., Bayesian) and non-probabilistic methods with related computational tools for UQ and inverse analysis; surrogate models for reducing the substantial computational effort; uncertainty propagation methods for data-informed predictions of performance; theoretical and experimental modal identification; linear and nonlinear system identification; statistical system identification methods (maximum-likelihood, Bayesian inference) for parameter and state estimation; reliability and safety of dynamical systems. Papers dealing with experimental investigation and verification of theories are especially welcome.
Complex engineered systems manifest in mechanical, aerospace, civil, earthquake and bio engineering as well as in related engineering disciplines. Complexities are linked to associated uncertainties, arising from several assumptions and compromises, including those on material constitutive laws, description of loads, influence of operational and environmental factors, energy dissipation mechanisms, motion constraints or large displacements of system components. The propagation of these uncertainties adversely affects simulation accuracy and, consequently, the design, operation and maintenance decisions for meeting desirable system performance and safety requirements. Measured data from component tests or field monitoring may be employed to narrow these uncertainties and produce system models, or surrogates of the true systems, of higher fidelity. The objective of this Research Topic is to present recent advances and emerging cross-disciplinary approaches in the broad field of structural health monitoring with a focus on novel techniques for a) uncertainty modeling and quantification, and for b) robust diagnostic tools for operation and maintenance.
Specific contributions related both to fundamental research and to engineering applications of advanced signal processing techniques and health monitoring algorithms for condition assessment, damage detection and reliability prognosis are welcome. A non-exhaustive list includes data-driven models for dynamics and vibrations of linear & nonlinear structural systems; robust outlier detection methods; probabilistic (e.g., Bayesian) and non-probabilistic methods with related computational tools for UQ and inverse analysis; surrogate models for reducing the substantial computational effort; uncertainty propagation methods for data-informed predictions of performance; theoretical and experimental modal identification; linear and nonlinear system identification; statistical system identification methods (maximum-likelihood, Bayesian inference) for parameter and state estimation; reliability and safety of dynamical systems. Papers dealing with experimental investigation and verification of theories are especially welcome.
