Analytical Mode Decomposition of Dynamic Structural Response for The Detection of Damage

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Damage detection is the process of identifying the occurrence of changes to the material and/or geometric properties of a structural system that adversely affect performance. The goal is to transform subjective and inconsistent visual inspections into objective and reliable condition screening of degrading structures, in particular during extreme events such as earthquakes and blasts. Most techniques currently available depend on the establishment of a structural model with no damage progression over time, which is often unrealistic during extreme events. This award supports fundamental research for the development of a model-free and adaptive data analysis technique. The new technique will enable an accurate and robust evaluation of time-dependent structural conditions, which has been increasingly sought for applications in civil infrastructure immediately after a disastrous event. Therefore, results from this study will greatly benefit post-event response and recovery, leading to enhanced resiliency and stable economy. The technique will be made available through a Wikipedia page and may be applicable to multiple disciplines in science, engineering, healthcare and finance industries. These multi-disciplinary applications will help broaden the participation of underrepresented groups in research and positively impact engineering education.Empirical mode decomposition is a non-parametric and adaptive damage detection technique that has since 1996 been demonstrated powerful to deal with nonstationary and nonlinear dynamic responses. However, it has never been mathematically proven for guaranteed success in various applications and thus faced challenges in analyzing noisy data, weak intermittent fluctuations, closely-spaced modes of vibration, and temporal changes in vibration mode. This research is to provide a mathematical foundation to the decomposition of structural dynamic response and explore analytical mode decomposition for damage detection of time-varying structures. The research team will establish a sampling rate and finite data length criterion that guarantees an accurate decomposition of a discrete time sequence, develop a new adaptive wavelet transform for the optimal determination of time-dependent bisecting frequencies, derive a computationally efficient solution to a new system-response characteristics relationship recently discovered for nonlinear structures, and experimentally validate the robustness, accuracy, and efficiency of the analytical decomposition in system identification and damage detection of complex structures with different nonlinearities.