Tracking of Stiffness Variation in Structural Members Using Input Error Function Observers
Source abstract
Abstract
This study evaluates input error function observers for tracking of stiffness variation in real-time. The input error function is an Analytical Redundancy (AR)-based diagnosis method and necessitates a mathematical model of the system and system identification techniques. In practice, mathematical models used during numerical simulations differ from the actual status of the structure, and thus, accurate mathematical models are rarely available for reference. Noise is an unwanted signal in the input–output measurements but unavoidable in real-world applications (as in long span bridge trusses) and hard to imitate during numerical simulations. Simulation data from the truss system clearly indicates the effectiveness of the proposed structural damage detection method for estimating the severity of the damage. Optimization of the input error function can further automate the stiffness estimation in structural members and address critical aspects such as system uncertainties and the presence of noise in input–output measurements. Stiffness tracking in one of the planar truss members indicates the potential of optimization of the input error function for online structural health monitoring and implementing condition-based maintenance.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Structural Health Monitoring Techniques · Concrete Corrosion and Durability · Probabilistic and Robust Engineering Design
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Complementary Visiting Scholar, Department of Civil and Environmental Engineering, Rice University, Houston, TX 77005, USA
Professor, Department of Civil and Environmental Engineering, Department of Mechanical Engineering, Department of Material Science and Nano Engineering, Rice University, Houston, TX 77005, USA
Funding
National Aeronautics and Space Administration
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