Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/182486
Title: Bayesian dynamic programming approach for tracking time-varying properties in structural health monitoring
Authors: Yang, Yanping
Keywords: Engineering
Issue Date: 2025
Publisher: Nanyang Technological University
Source: Yang, Y. (2025). Bayesian dynamic programming approach for tracking time-varying properties in structural health monitoring. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/182486
Project: RG68/22 
Abstract: Structural health monitoring (SHM) aims at monitoring the condition of structures such as buildings, bridges, and dams, using in-situ information extracted from measured data. Structural properties may change due to factors such as material aging and damage after extreme events (e.g., earthquakes and storms). Tracking changes under ambient excitations (e.g., wind, traffic, and tremor) is typical in SHM as it does not disturb normal operations. In principle, tracking results can aid in anomaly detection, supporting decisions such as early warning and maintenance strategies. How to do it properly and efficiently, however, is still challenging. The timing, frequency, and magnitude of changes are unknown and can occur in an apparently random manner. The properties extracted from SHM data under ambient excitations can have large estimation errors due to the lack of information arising from data (limited and noisy) and modeling (unknown excitation but modeled as a stochastic process). These can lead to spurious changes or even mask the actual changes in structural properties. Motivated by the aforementioned issues, this thesis develops a Bayesian probabilistic theory with an efficient algorithm for tracking model properties from SHM data and applies the methodology to SHM based on operational modal analysis. In the proposed context, SHM data in the time domain is divided into non-overlapping segments of potentially varying sizes, over which model properties are assumed to be piecewise constant. Using a Bayesian model selection approach, statistically significant changes are then detected naturally from the change points. Exploiting the causality nature of the tracking problem, a dynamic programming approach is proposed for determining the best partitioning of data, which is otherwise computationally prohibitive by exhaustive search. A ‘pruning’ strategy is developed to further suppress the growth of computational effort with monitoring data size that keeps growing. Beyond conventional models where all parameters are either constant or varying in a particular time window for analysis, a ‘quasi time-invariant’ model is proposed that allows a mix of such variables, providing flexibility to achieve target monitoring objectives balancing statistical Type I (false alarm) and Type II (missed alarm) errors. The proposed methodology is investigated using synthetic and laboratory data, and applied to field data during a typhoon event and a full-scale ambient vibration test where practical challenges in modal property tracking are discussed.
URI: https://hdl.handle.net/10356/182486
Schools: School of Civil and Environmental Engineering 
Rights: This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:CEE Theses

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