Date of Award
2026
Document Type
Open Access Dissertation
Degree Name
Doctor of Philosophy in Civil Engineering (PhD)
Administrative Home Department
Department of Civil, Environmental, and Geospatial Engineering
Advisor 1
Raymond A. Swartz
Committee Member 1
Daniel M. Dowden
Committee Member 2
Vijaya Venkata Narasimha Sriram Malladi
Committee Member 3
Yousef Mohammadi Darestani
Abstract
Structural health monitoring (SHM) detects and characterizes damage to predict failure, but failures such as buckling, which are not predicated on traditional damage modalities, are harder to detect. Direct load measurements are costly and difficult to implement (especially for dead loads) as they require copious numbers of sensors, which must be installed before loading is present. Vibration-based detection is a good alternative because it can infer global structural characteristics with relatively few sensors. This dissertation presents an SHM approach for detecting incipient buckling in structures under excessive loads based on non-linear models fit from measurements of small lateral vibrations due to ambient excitations. Existing nonlinear modeling algorithms in commercial software platforms, such as MATLAB and other open-source toolboxes, are leveraged to make this method conceivably accessible to practitioners. The proposed method attempts to infer the presence of incipient buckling behavior in a steel frame structure from the linearity of relatively low-order system identification models fitted to global vibration signals measured at low levels of ambient-scale vibration. Three readily available nonlinear system identification models are investigated in this research: 1) a nonlinear auto-regressive with exogenous input (NLARX) algorithm with a wavelet function, 2) an NLARX algorithm with tree ensembles, and 3) a physics-informed dynamic mode decomposition for detecting incipient buckling. The most suitable algorithm is determined using synthesized ambient vibration data obtained from numerical models of small-scale frame structures under varying gravity loads, incorporating nonlinearity via P-Delta and large-displacement effects. The average norm of the nonlinear component of the output is chosen as the feature and compared at incremental loadings up to the incipient buckling load. The incipient buckling condition is determined based on models formed using multiple redundant data sets to provide a statistical basis for detection. Results are obtained in a simulation environment, then verified using data from a small-scale physical structure and by replicating the behavior in numerical models. Finally, the applicability of the proposed method to a numerical model of a three-dimensional steel frame structure is studied to explore the effects of mixed frame and gravity columns as well as sensor placement. A noise-adjusted threshold is proposed to account for varying input levels likely encountered during ambient conditions. Scaling issues, the effects of unmeasured disturbances, and unidentified sensor noise are also examined to highlight the limitations of the proposed methods with respect to excitation scale.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Desai, Padmanabh Shridhar, "SHM FOR INCIPIENT BUCKLING DETECTION IN OVERLOADED STRUCTURES USING NONLINEAR SYSTEM IDENTIFICATION ALGORITHMS ON REDUNDANT DATA SETS", Open Access Dissertation, Michigan Technological University, 2026.