Advanced structural health monitoring and damage detection using inverse methods and perturbation stiff-ness matrix analysis
1 Department of Civil Engineering, Islamic Azad University, Rudehen, Tehran, Iran.
2 Federal Board of Intermediate and Secondary Education, Islamabad, Pakistan.
3 Osmani & Company Pvt. Ltd, Karachi, Pakistan.
4 Institute of sanitary engineering and waste management Leibniz university of Hanover, Hanover, Germany.
Research Article
World Journal of Advanced Research and Reviews, 2023, 20(01), 001–009
Article DOI: 10.30574/wjarr.2023.20.1.2002
Publication history:
Received on 14 August 2023; revised on 27 September 2023; accepted on 29 September 2023
Abstract:
Sensor technology advancements have the health monitoring task for modern intricate structures introduce many challenges, primarily centered around the augmentation of safety margins and operational dependabil-ity. The demand for diverse systems or methodologies is, therefore, increasing. These systems or methodolo-gies should be capable of detecting damage, monitoring remotely, and conducting continuous evaluations in real-time. As part of this study, inverse techniques rooted in wave propagation analysis within structural frameworks are utilized to identify damage response vectors. Additionally, mathematical formulations grounded in minimal rank perturbation theory facilitate determining a perturbed stiffness matrix for a com-promised structure. Various excitations are included in the analysis, which is based on intricate non-linear structural modeling. Using streamlined procedures, these findings confirm the inherent versatility of a range of methodologies capable of meeting both rapid monitoring needs and exhaustive analytical needs. In addition, the proposed approach promotes the use of intelligent structures embedded with a variety of sensors that are not restricted by limitations related to sensor type or spatial deployment. Monitoring structural health and detecting damage could be significantly improved by this strategic deployment.
Keywords:
Machine Learning; Structural Health Monitoring; Damage Detection; Inverse Methods; Perturbation Stiffness Matrix Analysis; Wave Propagation Analysis
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