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

D’Alessandro, Antonella; Ubertini, Filippo; García‐Macías, Enrique; Castro-Triguero, Rafael; Downey, Austin; Laflamme, Simon; Meoni, Andrea; Materazzi, Annibale Luigi

doi:10.1155/2017/3648403

Cited by 44 publications

(28 citation statements)

References 34 publications

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“…CNT fillers have showed good performance at the self-sensing of material deformations when deployed in a cementitious matrix [31][32][33]. The use of CNTs is also known to increase the ultimate strength and the ductility of the host material [34,35], but their environmental compatibility is not well understood [36] and result in high fabrication costs and a difficult dispersion process due to their hydrophobic characteristic [37,38]. Graphite fillers have been shown to be a good alternative due to their lower costs and easier dispersion relative to CNTs, but with the trade-off of a lower sensitivity to strain [38,39].…”

Section: Introductionmentioning

confidence: 99%

A Weigh-in-Motion Characterization Algorithm for Smart Pavements Based on Conductive Cementitious Materials

Birgin

Laflamme

D’Alessandro

et al. 2020

Sensors

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Smart materials are promising technologies for reducing the instrumentation cost required to continuously monitor road infrastructures, by transforming roadways into multifunctional elements capable of self-sensing. This study investigates a novel algorithm empowering smart pavements with weigh-in-motion (WIM) characterization capabilities. The application domain of interest is a cementitious-based smart pavement installed on a bridge over separate sections. Each section transduces axial strain provoked by the passage of a vehicle into a measurable change in electrical resistance arising from the piezoresistive effect of the smart material. The WIM characterization algorithm is as follows. First, basis signals from axles are generated from a finite element model of the structure equipped with the smart pavement and subjected to given vehicle loads. Second, the measured signal is matched by finding the number and weights of appropriate basis signals that would minimize the error between the numerical and measured signals, yielding information on the vehicle’s number of axles and weight per axle, therefore enabling vehicle classification capabilities. Third, the temporal correlation of the measured signals are compared across smart pavement sections to determine the vehicle weight. The proposed algorithm is validated numerically using three types of trucks defined by the Eurocodes. Results demonstrate the capability of the algorithm at conducting WIM characterization, even when two different trucks are driving in different directions across the same pavement sections. Then, a noise study is conducted, and the results conclude that a given smart pavement section operating with less than 5% noise on measurements could yield good WIM characterization results.

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Section: Introductionmentioning

confidence: 99%

A Weigh-in-Motion Characterization Algorithm for Smart Pavements Based on Conductive Cementitious Materials

Birgin

Laflamme

D’Alessandro

et al. 2020

Sensors

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“…Carbon‐based nanofillers, in particular carbon nanofibers and carbon nanotubes, have been widely used to enhance electrical properties and piezoresistive capability of cement‐based materials, manufacturing sensors capable to provide a measurable change in their electrical output, when subjected to a deformation . This promising technology can be used in automated SHM systems for concrete structures, in which these new self‐sensing technologies can provide real‐time information regarding the global health state of the structure, allowing to detect development and propagation of local damages . One of the most attractive features of this technique is the use of sensors made of a base material similar to that used for the structure being monitored.…”

Section: Introductionmentioning

confidence: 99%

Shaking table tests on a masonry building monitored using smart bricks: Damage detection and localization

Meoni

D’Alessandro

Cavalagli

et al. 2019

Earthq Engng Struct Dyn

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Summary The seismic behavior of unreinforced masonry buildings is typically characterized by premature brittle collapse mechanisms that can cause serious consequences for the protection of human lives and for the preservation of historical and cultural heritage. Structural health monitoring can be a powerful tool enabling a quick post‐earthquake assessment of the structure's performance, but its applications are still scarce as a consequence of the severe limitations affecting off‐the‐shelf sensing technologies, in terms of local nature of the measurements, costs, as well as long‐term behavior, installation, and maintenance. To overcome some of these limitations, the authors have recently proposed a new sensing technology, called “smart brick,” that is a durable clay brick doped with stainless steel microfibers, working as a smart strain sensor for masonry buildings. This paper presents the first full‐scale application of smart bricks, used for detecting and localizing progressive earthquake‐induced damage in an unreinforced masonry building subjected to shaking table tests. Smart bricks are employed to detect changes in load paths on masonry walls, comparing strain measurements acquired after each step of the seismic sequence with those referring to the undamaged structure. Experimental results are interpreted using a 3D finite element model built to reproduce the shaking table tests. Overall, the results demonstrate that the smart bricks can effectively reveal local permanent changes in structural conditions following a progressive damage, therefore being apt for earthquake‐induced damage detection and localization.

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“…Strains can be measured by sensors that rely on the piezoresistive effect [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ], the frequency shift of a resonator’s fundamental mode [ 34 , 35 , 36 ], the piezoelectric effect [ 37 , 38 ], the capacitance change [ 39 , 40 , 41 , 42 , 43 ], the optical properties changes [ 44 , 45 , 46 , 47 , 48 , 49 ], and other effects [ 50 , 51 , 52 ]. Piezoresistive effects consist of changes in the electrical resistance of a material when subjected to a mechanical strain.…”

Section: Introductionmentioning

confidence: 99%

“…Other strain sensors rely on the piezoresistive nature of carbon nanotubes that are dispersed in polymeric matrices, forming nanocomposites, and exhibiting a quasi-linear resistance change-strain response with gauge factors varying between −200 and 500 [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. More recently, strain gauges using carbon nanotubes, graphene and other carbon nanomaterials are being developed and offer the promise of high gauge factors [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

Foil Strain Gauges Using Piezoresistive Carbon Nanotube Yarn: Fabrication and Calibration

Abot

Gongora-Rubio²,

Anike

et al. 2018

Sensors

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Carbon nanotube yarns are micron-scale fibers comprised by tens of thousands of carbon nanotubes in their cross section and exhibiting piezoresistive characteristics that can be tapped to sense strain. This paper presents the details of novel foil strain gauge sensor configurations comprising carbon nanotube yarn as the piezoresistive sensing element. The foil strain gauge sensors are designed using the results of parametric studies that maximize the sensitivity of the sensors to mechanical loading. The fabrication details of the strain gauge sensors that exhibit the highest sensitivity, based on the modeling results, are described including the materials and procedures used in the first prototypes. Details of the calibration of the foil strain gauge sensors are also provided and discussed in the context of their electromechanical characterization when bonded to metallic specimens. This characterization included studying their response under monotonic and cyclic mechanical loading. It was shown that these foil strain gauge sensors comprising carbon nanotube yarn are sensitive enough to capture strain and can replicate the loading and unloading cycles. It was also observed that the loading rate affects their piezoresistive response and that the gauge factors were all above one order of magnitude higher than those of typical metallic foil strain gauges. Based on these calibration results on the initial sensor configurations, new foil strain gauge configurations will be designed and fabricated, to increase the strain gauge factors even more.

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

Cited by 44 publications

References 34 publications

A Weigh-in-Motion Characterization Algorithm for Smart Pavements Based on Conductive Cementitious Materials

A Weigh-in-Motion Characterization Algorithm for Smart Pavements Based on Conductive Cementitious Materials

Shaking table tests on a masonry building monitored using smart bricks: Damage detection and localization

Foil Strain Gauges Using Piezoresistive Carbon Nanotube Yarn: Fabrication and Calibration

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