Damage detection, localization and quantification in conductive smart concrete structures using a resistor mesh model

D’Alessandro

Ubertini

et al. 2018

Meas. Sci. Technol.

Self Cite

Various nondestructive evaluation techniques are currently used to automatically detect and monitor cracks in concrete infrastructure. However, these methods often lack the scalability and cost-effectiveness over large geometries. A solution is the use of self-sensing carbon-doped cementitious materials. These self-sensing materials are capable of providing a measurable change in electrical output that can be related to their damage state. Previous work by the authors showed that a resistor mesh model could be used to track damage in structural components fabricated from electrically conductive concrete, where damage was located through the identification of high resistance value resistors in a resistor mesh model. In this work, an automated damage detection strategy that works through placing high value resistors into the previously developed resistor mesh model using a sequential Monte Carlo method is introduced. Here, high value resistors are used to mimic the internal condition of damaged cementitious specimens. The proposed automated damage detection method is experimentally validated using a $500 x 500 x 50 $ mm reinforced cement paste plate doped with multi-walled carbon nanotubes exposed to 100 identical impact tests. Results demonstrate that the proposed Monte Carlo method is capable of detecting and localizing the most prominent damage in a structure, demonstrating that automated damage detection in smart-concrete structures is a promising strategy for real-time structural health monitoring of civil infrastructure. Disciplines

Section: Resistor Mesh Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated crack detection in conductive smart-concrete structures using a resistor mesh model

D’Alessandro

Ubertini

et al. 2018

Meas. Sci. Technol.

Self Cite

“…Despite high 57 spatial resolution capabilities, the requirements for repeated 58 measurements using a variety of contacts and for solving 59 the inverse mapping problem make the EIT technique 60 not well suited for every application. Another electrical 61 tomography technique uses a resistor mesh model to detect 62 and localize damage-induced strain changes in cement 63 doped with multi-walled carbon nanotubes [17]. However, 64 this model-assisted approach requires that damage be 65 located through the use of a searching method that updates 66 the resistor mesh model associated with the structure, thus 67 adding a relatively high computational cost to the approach 68 [18].…”

Section: Introductionmentioning

confidence: 99%

Fusion of sensor geometry into additive strain fields measured with sensing skin

Sadoughi

Laflamme

et al. 2018

Smart Mater. Struct.

Self Cite

Recently, numerous studies have been conducted on flexible skin-like membranes for the cost effective monitoring of large-scale structures. The authors have proposed a large-area electronic consisting of a soft elastomeric capacitor (SEC) that transduces a structure's strain into a measurable change in capacitance. Arranged in a network configuration, SECs deployed onto the surface of a structure could be used to reconstruct strain maps. Several regression methods have been recently developed with the purpose of reconstructing such maps, but all these algorithms assumed that each SEC measured strain located at its geometric center. This assumption may not be realistic since an SEC measures the average strain value of the whole area covered by the sensor. One solution is to reduce the size of each SEC, but this would also increase the number of required sensors needed to cover the large-scale structure, therefore increasing the need for the power and data acquisition capabilities. Instead, this study proposes an algorithm that accounts for the sensor's strain averaging feature by adjusting the strain measurements and constructing a full-field strain map using the kriging interpolation method. The proposed algorithm fuses the geometry of an SEC sensor into the strain map reconstruction in order to adaptively adjust the average kriging-estimated strain of the area monitored by the sensor to the signal. Results show that by considering the sensor geometry, in addition to the sensor signal and location, the proposed strain map adjustment algorithm is capable of producing more accurate full-field strain maps than the traditional spatial interpolation method that considered only signal and location

“…20 Also, a model-assisted approach for detecting and localizing cracks using a resistor mesh model was developed and demonstrated on an RC beam. 21 The use of the resistor mesh model was then ameliorated through the development of an automated damage detection scheme. 22 In addition to the previously discussed self-sensing cementitious materials, sensing skins have seen considerable research and show great potential over traditional NDE systems for the continuous and automated detection of cracks in RC structures.…”

Section: Introductionmentioning

confidence: 99%

Crack detection in RC structural components using a collaborative data fusion approach based on smart concrete and large-area sensors

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018

D’Alessandro

Ubertini

et al. 2018

Self Cite

Recent advances in the fields of nanocomposite technologies have enabled the development of highly scalable, low-cost sensing solution for civil infrastructures. This includes two sensing technologies, recently proposed by the authors, engineered for their high scalability, low-cost and mechanical simplicity. The first sensor consists of a smart-cementitious material doped with multi-wall carbon nanotubes, which has been demonstrated to be suitable for monitoring its own deformations (strain) and damage state (cracks). Integrated to a structure, this smart cementitious material can be used for detecting damage or strain through the monitoring of its electrical properties. The second sensing technology consists of a sensing skin developed from a flexible capacitor that is mounted externally onto the structure. When deployed in a dense sensor network configuration, these large area sensors are capable of covering large surfaces at low cost and can monitor both strain-and crack-induced damages. This work first presents a comparison of the capabilities of both technologies for crack detection in a concrete plate, followed by a fusion of sensor data for increased damage detection performance. Experimental results are conducted on a 50 50 5 cm3 plate fabricated with smart concrete and equipped with a dense sensor network of 20 large area sensors. Results show that both novel technologies are capable of increased damage localization when used concurrently.