Optimal Sensor Placement for Spatial Structure Based on Importance Coefficient and Randomness

He, Haoxiang; Xu, Honggang; Wang, Xiaobing; Zhang, Xiaofu; Fan, Shengjun

doi:10.1155/2018/7540129

Cited by 10 publications

(12 citation statements)

References 25 publications

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“…As revealed in Table 5, excellent sensing parameters and stable linear responses render our CNT/PVA/ZnO composite feasible as a stress/strain sensor. An excessively high sensitivity results in failure during large deformation of the structure, and a sensitivity that is too low greatly increases the monitoring difficulty of the system 44 . The CNT/PVA/ZnO composite in our study and ref.…”

Section: Resultsmentioning

confidence: 99%

Fabrication and Piezoresistive/Piezoelectric Sensing Characteristics of Carbon Nanotube/PVA/Nano-ZnO Flexible Composite

Chen

Luo

Wang

et al. 2020

Sci Rep

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Flexible sensors with a high sensitivity and wide-frequency response are essential for structural health monitoring (SHM) while they are attached. Here, carbon nanotube (CNT) films doped with various PVA fractions (CNT/PVA) and ZnO nanowires (nano-ZnO) on zinc sheets were first fabricated by functionalized self-assembly and hydrothermal synthesis processes. A CNT/PVA/nano-ZnO flexible composite (CNT/PVA/ZnO) sandwiched with a zinc wafer was then prepared by the spin-coating method. The piezoresistive and/or piezoelectric capabilities of the CNT/PVA/ZnO composite were comprehensively investigated under cyclic bending and impact loading after it was firmly adhered to a substrate (polypropylene sheet or mortar plate). The results show that the piezoresistive sensitivity and linear stability of the CNT/PVA films doped with 20%, 50%, and 100% PVA during bending are 5.47%/mm, 11.082%/mm, and 11.95%/mm and 2.3%, 3.42%, and 4.78%, respectively. The piezoelectric sensitivity, linear stability, and response accuracy of the CNT/PVA/ZnO composite under impulse loading are 4.87 mV/lbf, 3.42%, and 1.496 ms, respectively. These merits support the use of CNT/PVA/ ZnO as a piezoresistive and/or piezoelectric compound sensor to monitor the static/dynamic loads on concrete structures while it is attached. Structural health monitoring (SHM) of the structural integrity of concrete structures under external static/ dynamic loading has become an important tool to ensure the safety of various types of infrastructure. SHM aims to design, verify and optimize structures and also provide real-time structural damage detection and assessment by comprehensively utilizing signal processing, sensing, communications, and other cross-subject technologies. Sensors with accurate and fast-response sensing capabilities are fundamental and essential to SHM, and sensor development has attracted considerable worldwide attention 1. Over the last several decades, fiber Bragg grating (FBG) sensors have increasingly been developed for SHM owing to their advantages of high linearity, accurate sensing, corrosion resistance, small size, and long-distance distribution 2. However, FBG has some disadvantages that restrict its wider application in SHM, such as high brittleness if there is no packaging process, incompatibility when embedded in concrete structures, and sophisticated synthesis, substrate selection and packaging 3,4 processes.; in addition, the FBG needs to be equipped with a signal demodulator 5. Recently, Chung et al. 6 , Sun et al. 7 , Li et al. 8 , Han et al. 9 , Luo et al. 10 , Dong et al. 11 , and Huang et al. 12 introduced conductive fillers, such as steel fibers, carbon fibers, carbon black, nickel powder, carbon nanotubes, carbon nanofibers, and lead zirconium titanate powder, into cement/mortar to develop intrinsic piezoresistive or piezoelectric sensors for the SHM of concrete structures and achieved good compatibility, a relatively high sensitivity, and high linearity. However, most of the

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Section: Resultsmentioning

confidence: 99%

Fabrication and Piezoresistive/Piezoelectric Sensing Characteristics of Carbon Nanotube/PVA/Nano-ZnO Flexible Composite

Chen

Luo

Wang

et al. 2020

Sci Rep

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“…The closer the information collected from two measuring points is, the more similar the information matrix is. In this paper, the information independence of two measuring points or degrees of freedom is evaluated by introducing European distance, defining (He et al, 2013):…”

Section: Theorymentioning

confidence: 99%

A distance coefficient-multi objective information fusion algorithm for optimal sensor placement in structural health monitoring

Cao

Chen

et al. 2020

Advances in Structural Engineering

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Optimal sensor placement (OSP) plays a key role in the construction and implementation of an effective structural health monitoring system (SHM). In this study, a novel and effective method named the distance coefficient-multi objective information fusion algorithm (D-MOIF), which is different from the conventional method and easier to be implemented, is developed to select the best sensor location for large-scale structures. An integrated information matrix including mode independence, damage sensitivity and modal strain energy is deduced from the structural motion equation to meet multiple needs of SHM. A European distance derived from the analytic geometry is proposed to overcome the information redundancy between sensors. Based on the principle of information entropy, an optimized objective function is constructed, which could balance the sensitivity and robustness of the algorithm. A computational case of a high arch dam is implemented to demonstrate the effectiveness of the modified algorithm, and three classical evaluation criteria are used to estimate the comparison between the D-MOIF algorithm and four traditional OSP methods. Finally, the optimization of the number of sensors based on different algorithms is discussed in detail. Results indicate that the proposed D-MOIF algorithm could generate more applicable sensor configurations for large-scale structures.

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“…Using static displacement and static strain measurements, Sanayei et al 12 estimated structural parameters by displacement equation error function, displacement output error function, and strain output error function. Importing the concept of generalized equivalent stiffness, He et al 13 determined the sensor placement scheme according to the statistical data. Li and Yang 14 presented an algorithm divided into two stages for optimizing sensor locations.…”

Section: Introductionmentioning

confidence: 99%

Optimal sensor placement through expansion of static strain measurements to static displacements

Song

Lee

Eun

2021

International Journal of Distributed Sensor Networks

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Optimal sensor placement is used to establish the optimal sensor quantity and layout. In this study, the minimum quantity and locations of measurement sensors were assumed to satisfy the constraint conditions of the optimal sensor placement. A set of strain data in a truss structure was expanded to another set of displacements corresponding to the entire degrees of freedom from the relationship between the strain and displacement. It indicates to reduce the number of sensors because the strain depends on the displacements in a finite element model. The damaged truss element was traced using the expanded data that satisfied the prescribed constraints. The proposed optimal sensor placement method has a merit to explicitly determine the optimal sensor locations without any numerical scheme and statistical methods. The method was applied to the damage detection of a single-damaged truss structure. It was shown that the optimal sensor placement method depended on the sensor layout irrespective of the same quantity of sensors. In addition, a numerical example was used to compare sensitivities to damage detection based on the sensor placement and the existence of external noise contained in the measurement data.

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Optimal Sensor Placement for Spatial Structure Based on Importance Coefficient and Randomness

Cited by 10 publications

References 25 publications

Fabrication and Piezoresistive/Piezoelectric Sensing Characteristics of Carbon Nanotube/PVA/Nano-ZnO Flexible Composite

Fabrication and Piezoresistive/Piezoelectric Sensing Characteristics of Carbon Nanotube/PVA/Nano-ZnO Flexible Composite

A distance coefficient-multi objective information fusion algorithm for optimal sensor placement in structural health monitoring

Optimal sensor placement through expansion of static strain measurements to static displacements

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