Shape sensing of variable cross-section beam using the inverse finite element method and isogeometric analysis

Zhao, Feifei; Xu, Libo; Bao, Hong; Du, Jingli

doi:10.1016/j.measurement.2020.107656

Cited by 48 publications

(14 citation statements)

References 18 publications

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“…This new framework simply merges the concepts of isogeometric analysis [61][62] with the iFEM methodology. The first method [60] was developed for thin doubly-curved shell structures, which was simplified later for shape sensing applications of straight beams with variable cross sections [63][64]. Additionally, robust displacement theories such as refined zigzag theory (RZT) [65] have been combined with iFEM to reconstruct zigzag deformation through the thickness of sandwich plate [66], shell [67], and beam [68] structures.…”

Section: Introductionmentioning

confidence: 99%

Coupling of peridynamics and inverse finite element method for shape sensing and crack propagation monitoring of plate structures

Kefal

Diyaroglu

Yıldız

et al. 2022

Computer Methods in Applied Mechanics and Engineering

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Section: Introductionmentioning

confidence: 99%

Coupling of peridynamics and inverse finite element method for shape sensing and crack propagation monitoring of plate structures

Kefal

Diyaroglu

Yıldız

et al. 2022

Computer Methods in Applied Mechanics and Engineering

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“…Several formulations of the method have been proposed, based on the definition of different inverse elements. Beam elements have been formulated for the monitoring of truss and beam structures [ 24 , 25 , 26 , 27 ]. Three-nodes inverse shell elements have been widely used for the analysis of thin plates [ 28 , 29 ] and thin walled structures [ 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

Experimental Shape Sensing and Load Identification on a Stiffened Panel: A Comparative Study

Esposito

Mattone

Gherlone

2022

Sensors

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The monitoring of loads and displacements during service life is proving to be crucial for developing a modern Structural Health Monitoring framework. The continuous monitoring of these physical quantities can provide fundamental information on the actual health status of the structure and can accurately guide pro-active condition-based maintenance operations, thus reducing the maintenance costs and extending the service life of the monitored structures. Pushed by these needs and by the simultaneous development in the field of strain sensing technologies, several displacement reconstruction and load identification methods have been developed that are based on discrete strain measurements. Among the different formulations, the inverse Finite Element Method (iFEM), the Modal Method (MM) and the 2-step method, the latter being the only one able to also compute the loads together with the displacements, have emerged as the most accurate and reliable ones. In this paper, the formulation of the three methods is summarized in order to set the numerical framework for a comparative study. The three methods are tested on the reconstruction of the external load and of the displacement field of a stiffened aluminium plate starting from experimentally measured strains. A fibre optic sensing system has been used to measure surface strains and an optimization procedure has been performed to provide the best fibre pattern, based on five lines running along the stiffeners’ direction and with a back-to-back measuring scheme. Additional sensors are used to measure the applied force and the plate’s deflection in some locations. The comparison of the results obtained by each method proves the extreme accuracy and reliability of the iFEM in the reconstruction of the deformed shape of the panel. On the other hand, the Modal Method leads to a good reconstruction of the displacements, but also exhibits a sensitivity to the choice of the modes considered for the specific application. Finally, the 2-step approach is able to correctly identify the loads and to reconstruct the displacements with an accuracy that depends on the modeling of the experimental setup.

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“…The Inverse finite element (iFEM) method, a novel approach of SHM proposed by Tessler and Spangler [ 24 ], originally employed a three-node inverse-shell element (iMIN3) for the shape sensing of plate structures. iFEM is a strain/displacement based SHM technique which, in contrast to other available SHM methods, is suitable to monitor any displacement of complex topologies and stress fields with intricate boundary conditions by using a network of in situ strain sensors and measured strains [ 25 , 26 , 27 , 28 , 29 ]. The advantages of iFEM have recently drawn significant attention since various scientists have attempted to improve the available iFEM equations to achieve better results in the last decade.…”

Section: Introductionmentioning

confidence: 99%

Sensor Placement Optimization for Shape Sensing of Plates and Shells Using Genetic Algorithm and Inverse Finite Element Method

Ghasemzadeh

Kefal

2022

Sensors

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This paper reports the first investigation of the inverse finite element method (iFEM) coupled with the genetic algorithm (GA) to optimize sensor placement models of plate/shell structures for their real-time and full-field deformation reconstruction. The primary goal was to reduce the number of sensors in the iFEM models while maintaining the high accuracy of the displacement results. Here, GA was combined with the four-node quadrilateral inverse-shell elements (iQS4) as the genes inherited through generations to define the optimum positions of a specified number of sensors. Initially, displacement monitoring of various plates with different boundary conditions under concentrated and distributed static/dynamic loads was conducted to investigate the performance of the coupled iFEM-GA method. One of these case studies was repeated for different initial populations and densities of sensors to evaluate their influence on the accuracy of the results. The results of the iFEM-GA algorithm indicate that an adequate number of individuals is essential to be assigned as the initial population during the optimization process to ensure diversity for the reproduction of the optimized sensor placement models and prevent the local optimum. In addition, practical optimization constraints were applied for each plate case study to demonstrate the realistic applicability of the implemented method by placing the available sensors at feasible sites. The iFEM-GA method’s capability in structural dynamics was also investigated by shape sensing the plate subjected to different dynamic loadings. Furthermore, a clamped stiffened plate and a curved shell were also considered to assess the applicability of the proposed method for the shape sensing of complex structures. Remarkably, the outcomes of the iFEM-GA approach with the reduced number of sensors agreed well with those of the full-sensor counterpart for all of the plate/shell case studies. Hence, this study reveals the superior performance of the iFEM-GA method as a viable sensor placement strategy for the accurate shape sensing of engineering structures with only a few sensors.

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Shape sensing of variable cross-section beam using the inverse finite element method and isogeometric analysis

Cited by 48 publications

References 18 publications

Coupling of peridynamics and inverse finite element method for shape sensing and crack propagation monitoring of plate structures

Coupling of peridynamics and inverse finite element method for shape sensing and crack propagation monitoring of plate structures

Experimental Shape Sensing and Load Identification on a Stiffened Panel: A Comparative Study

Sensor Placement Optimization for Shape Sensing of Plates and Shells Using Genetic Algorithm and Inverse Finite Element Method

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