Damage Detection and Localization from Dense Network of Strain Sensors

Laflamme, Simon; Cao, Liang; Chatzi, Eleni; Ubertini, Filippo

doi:10.1155/2016/2562949

Cited by 53 publications

(36 citation statements)

References 32 publications

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“…Structural health monitoring (SHM) is a topic of growing scientific interest in various fields of engineering, with potential to increase engineering systems' safety and lead to the optimization of repair, maintenance, and restoration activities [1,2]. Strategically designed monitoring systems can detect damage or variations of structural behavior during the service life of a structure [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Static and Dynamic Strain Monitoring of Reinforced Concrete Components through Embedded Carbon Nanotube Cement-Based Sensors

D’Alessandro

Ubertini

García‐Macías

et al. 2017

Shock and Vibration

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The paper presents a study on the use of cement-based sensors doped with carbon nanotubes as embedded smart sensors for static and dynamic strain monitoring of reinforced concrete (RC) elements. Such novel sensors can be used for the monitoring of civil infrastructures. Because they are fabricated from a structural material and are easy to utilize, these sensors can be integrated into structural elements for monitoring of different types of constructions during their service life. Despite the scientific attention that such sensors have received in recent years, further research is needed to understand (i) the repeatability and accuracy of sensors' behavior over a meaningful number of sensors, (ii) testing configurations and calibration methods, and (iii) the sensors' ability to provide static and dynamic strain measurements when actually embedded in RC elements. To address these research needs, this paper presents a preliminary characterization of the self-sensing capabilities and the dynamic properties of a meaningful number of cement-based sensors and studies their application as embedded sensors in a full-scale RC beam. Results from electrical and electromechanical tests conducted on small and full-scale specimens using different electrical measurement methods confirm that smart cement-based sensors show promise for both static and vibration-based structural health monitoring applications of concrete elements but that calibration of each sensor seems to be necessary.

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Section: Introductionmentioning

confidence: 99%

Static and Dynamic Strain Monitoring of Reinforced Concrete Components through Embedded Carbon Nanotube Cement-Based Sensors

D’Alessandro

Ubertini

García‐Macías

et al. 2017

Shock and Vibration

Self Cite

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“…The robust and static characterization of the sensors was demonstrated through testing [47]. Static characterization, dynamic monitoring, localization of fatigue cracks and the distribution of thin film sensor arrays on structures were studied using the referenced SECs [80][81][82][83]. The electrodes of these sensors were composed of SEBS containing carbon black particles, and the sensors were not transparent.…”

Section: Flexible and Large-surface Dielectric Elastomer Sensors Applmentioning

confidence: 99%

Dielectric Elastomer Sensors

Ni¹,

Zhang²

2017

Elastomers

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Dielectric elastomers (DEs) represent a class of electroactive polymers (EAPs) that exhibit a significant electromechanical effect, which has made them very attractive over the last several decades for use as soft actuators, sensors and generators. Based on the principle of a plane-parallel capacitor, dielectric elastomer sensors consist of a flexible and stretchable dielectric polymer sandwiched between two compliant electrodes. With the development of elastic polymers and stretchable conductors, flexible and sensitive dielectric elastomer tactile sensors, similar to human skin, have been used for measuring mechanical deformations, such as pressure, strain, shear and torsion. For high sensitivity and fast response, air gaps and microstructural dielectric layers are employed in pressure sensors or multiaxial force sensors. Multimodal dielectric elastomer sensors have been reported that can detect mechanical deformation but can also sense temperature, humidity, as well as chemical and biological stimulation in human-activity monitoring and personal healthcare. Hence, dielectric elastomer sensors have great potential for applications in soft robotics, wearable devices, medical diagnostic and structural health monitoring, because of their large deformation, low cost, ease of fabrication and ease of integration into monitored structures.

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“…Natural question arise if we can devise a simpler algorithm to accomplish what we have using the proposed diffusion map approach. In the following, we describe a relatively simple algorithm adopted from [41]. We show that algorithm can detect damage, the algorithm is not robust and is sensitive to noise.…”

Section: Different Damage Levelsmentioning

confidence: 99%

Damage detection on mesosurfaces using distributed sensor network and spectral diffusion maps

Venkatesh

Cao

Vaidya

et al. 2016

Meas. Sci. Technol.

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Abstract. In this work, we develop a data-driven method for the diagnosis of damage in mesoscale mechanical structures using an array of distributed sensor networks. The proposed approach relies on comparing intrinsic geometries of data sets corresponding to the undamage and damage states of the system. We use spectral diffusion map approach for identifying the intrinsic geometry of the data set. In particular, time series data from distributed sensors is used for the construction of diffusion maps. The low dimensional embedding of the data set corresponding to different damage levels is obtained using singular value decomposition of the diffusion map. We construct appropriate metrics in the diffusion space to compare the different data sets corresponding to different damage cases. The developed algorithm is applied for damage diagnosis of wind turbine blades. Towards this goal, we developed a detailed finite element-based model of CX-100 blade in ANSYS using shell elements. Typical damage, such as crack or delamination, will lead to a loss of stiffness, is modeled by altering the stiffness of the laminate layer. One of the main challenges in the development of health monitoring algorithms is the ability to use sensor data with relatively small signal-to-noise ratio. Our developed diffusion map-based algorithm is shown to be robust to the presence of sensor noise. The proposed diffusion map-based algorithm is advantageous by enabling the comparison of data from numerous sensors of similar or different types of data through data fusion, hereby making it attractive to exploit the distributed nature of sensor arrays. This distributed nature is further exploited for the purpose of damage localization. We perform extensive numerical simulations to demonstrate that the proposed method can successfully determine the extent of damage on the wind turbine blade and also localize the damage. We also present preliminary results for the application of the developed algorithm on the experimental data. These preliminary results obtained using experimental data are promising and is a topic of our ongoing investigation.

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Damage Detection and Localization from Dense Network of Strain Sensors

Cited by 53 publications

References 32 publications

Static and Dynamic Strain Monitoring of Reinforced Concrete Components through Embedded Carbon Nanotube Cement-Based Sensors

Static and Dynamic Strain Monitoring of Reinforced Concrete Components through Embedded Carbon Nanotube Cement-Based Sensors

Dielectric Elastomer Sensors

Damage detection on mesosurfaces using distributed sensor network and spectral diffusion maps

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