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Structural health monitoring

About: Structural health monitoring is a research topic. Over the lifetime, 11727 publications have been published within this topic receiving 186231 citations.


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Journal ArticleDOI
TL;DR: Despite the low levels of vibration in operational conditions, the system is seen able to track the time evolution of five natural frequencies of the structure and successfully use such information for detecting anomalous deviations from normal conditions.
Abstract: This article presents the development and the results of 1 year of implementation of a simple vibration-based structural health monitoring system for preventive conservation and condition-based mai...

113 citations

Journal ArticleDOI
TL;DR: An integrated multi-channel PZT array scanning system (ISS) is developed for the purpose of SHM and an integrated software system which is based on the LabVIEW software platform is developed to manage the hardware, and perform signal processing and damage estimation.
Abstract: Piezoelectric sensor (PZT) based structural health monitoring (SHM) methods can efficiently estimate the health condition of aircraft structures. To monitor large-scale structures, dense PZT arrays are usually needed. How to scan different PZT actuator–sensor channels in the PZT array to achieve a real time and stable SHM task is an important issue in the application of these methods. In this paper, an integrated multi-channel PZT array scanning system (ISS) is developed for the purpose of SHM. A type of gain programmable charge amplifier and a low crosstalk scanning module are discussed. An integrated software system which is based on the LabVIEW software platform is developed to manage the hardware, and perform signal processing and damage estimation. To validate the functions of this system, an evaluation is performed on a carbon fiber composite wing box of an unmanned aerial vehicle (UAV). The application results show the promising performance of this system.

113 citations

Journal ArticleDOI
TL;DR: The proposed approach has been applied to a 7-degree of freedom building model for structural damage detection, and proves to be feasible, efficient and satisfactory, and shows that the identification accuracy can be boosted with the proposed approach instead of FNN models alone.
Abstract: It is proposed in this paper a novel two-stage structural damage detection approach using fuzzy neural networks (FNNs) and data fusion techniques. The method is used for structural health monitoring and damage detection, particularly for cases where the measurement data is enormous and with uncertainties. In the first stage of structural damage detection, structural modal parameters derived from structural vibration responses are fed into an FNN as the input. The output values from the FNN are defuzzified to produce a rough structural damage assessment. Later, in the second stage, the values output from three different FNN models are input directly to the data fusion center where fusion computation is performed. The final fusion decision is made by filtering the result with a threshold function, hence a refined structural damage assessment of superior reliability. The proposed approach has been applied to a 7-degree of freedom building model for structural damage detection, and proves to be feasible, efficient and satisfactory. Furthermore, the simulation result also shows that the identification accuracy can be boosted with the proposed approach instead of FNN models alone.

113 citations

Journal ArticleDOI
TL;DR: In this paper, a finite element model was developed for bridge management and calibration using nondestructive test data, and the model calibration technique was evaluated on the Vernon Avenue Bridge over the Ware River in Barre, Massachusetts.
Abstract: A baseline finite element model was developed for bridge management and calibration using nondestructive test data. The model calibration technique was evaluated on the Vernon Avenue Bridge over the Ware River in Barre, Massachusetts. This newly constructed bridgewas instrumented throughout its construction phases in preparation for a static truck load test performed before the bridge opening. The strain data collected during the load test was used to calibrate a detailed baseline finite element model in an effort to represent the 3D system behavior of the bridge. Three methods of load ratings were used and compared: (1) conventional method, (2) conventional method updated by using NDT data, and (3) finite element model calibrated with NDT data. DOI: 10.1061/(ASCE)BE.1943-5592.0000228. © 2012 American Society of Civil Engineers. CE Database subject headings: Bridges; Superstructures; Full-scale tests; Field tests; Nondestructive tests; Strain; Measurement; Finite element method; Load tests; Structural health monitoring. Author keywords: Bridge; Superstructure; Full scale; Field testing; Strain measurements; Finite elements; Model calibration; Load testing; Load rating; Structural health monitoring; SHM.

113 citations

Journal ArticleDOI
TL;DR: In this paper, the authors used Autoregressive (AR) models to fit the acceleration time histories obtained from two experimental structures: a 3-storey bookshelf structure and the ASCE Phase II Experimental SHM Benchmark Structure, in undamaged and limited number of damaged states.

112 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023600
20221,374
2021776
2020746
2019803
2018708