Effect of compressive strain on electrical resistivity of carbon black-filled cement-based composites

Li, Hui; Xiao, Huigang; Ou, Jinping

doi:10.1016/j.cemconcomp.2006.05.004

Cited by 268 publications

(140 citation statements)

References 14 publications

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“…During testing of specimen III a maximum compression of 65 µε was recorded with an externally mounted strain gauge. The linearity of strain sensitivity for compressive loading has been documented well passed 330 this loading level and is assumed to remain linear for the purpose of this study [17,19,37,38]. As expected, the specimen resistance decreases with the increasing compressive force.…”

Section: Strain Sensing Characterizationmentioning

confidence: 76%

“…Fabrication of self-sensing ce-70 mentitious materials through the doping of carbonbased particles into traditional admixtures of cement has been achieved [14]. Various carbon-based materials have been mixed with cementitious materials, including carbon fibers [15,16], nano-carbon 75 black [17] and, more recently, multi-walled carbon nanotubes (MWCNT) [4,18,19]. MWCNTs offer great potential due to their excellent electrical and mechanical properties [20,21].…”

mentioning

confidence: 99%

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

Downey

D’Alessandro

Baquera

et al. 2017

Engineering Structures

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Interest in self-sensing structural materials has grown in recent years due to their potential to enable continuous low-cost monitoring of next-generation smart-structures. The development of cement-based smart sensors appears particularly well suited for structural health monitoring due to their numerous possible field applications, ease of use, and long-term stability. Additionally, cement-based sensors offer a unique opportunity for monitoring of civil concrete structures because of their compatibility with new and existing infrastructure. In this paper, we propose the use of a computationally efficient resistor mesh model to detect, localize and quantify damage in structures constructed from conductive cement composites. The proposed approach is experimentally validated on non-reinforced and reinforced specimens made of nanocomposite cement paste doped with multi-walled carbon nanotubes under a variety of static loads and damage conditions. Results show that the proposed approach is capable of leveraging the strain-sensing and damagesensitive properties of conductive cement composites for real-time distributed structural health monitoring of smart concrete structures, using simple and inexpensive electrical hardware and with very limited computational effort. AbstractInterest in self-sensing structural materials has grown in recent years due to their potential to enable continuous low-cost monitoring of next-generation smart-structures. The development of cement-based smart sensors appears particularly well suited for structural health monitoring due to their numerous possible field applications, ease of use, and long-term stability. Additionally, cement-based sensors offer a unique opportunity for monitoring of civil concrete structures because of their compatibility with new and existing infrastructure. In this paper, we propose the use of a computationally efficient resistor mesh model to detect, localize and quantify damage in structures constructed from conductive cement composites. The proposed approach is experimentally validated on non-reinforced and reinforced specimens made of nanocomposite cement paste doped with multi-walled carbon nanotubes under a variety of static loads and damage conditions. Results show that the proposed approach is capable of leveraging the strain-sensing and damage-sensitive properties of conductive cement composites for real-time distributed structural health monitoring of smart concrete structures, using simple and inexpensive electrical hardware and with very limited computational effort.

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Section: Strain Sensing Characterizationmentioning

confidence: 76%

mentioning

confidence: 99%

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

Downey

D’Alessandro

Baquera

et al. 2017

Engineering Structures

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“…Other researchers have also begun to study the piezoresistivity (i.e., strain induced resistivity changes) of FRCC materials with the aim of using them as their own sensors (Chung, 2001;Reza et al, 2003;Hou and Lynch, 2005b;Li et al, 2006). The addition of sensing functionality with mechanical functionality has led to FRCC being classified as a 'multifunctional' material (Chung, 2003).…”

Section: Self-sensing Fiber Reinforced Cementitious Compositesmentioning

confidence: 99%

Electrical Impedance Tomographic Methods for Sensing Strain Fields and Crack Damage in Cementitious Structures

Hou

Lynch

2008

Journal of Intelligent Material Systems and Structures

131

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Cement-based composites (for example, concrete) are brittle materials that crack when loaded in tension. Current strategies for crack detection are primarily based upon visual inspection by an inspector; such approaches are labor-intensive and expensive. Direly needed are sensors that can be included within a structural health monitoring (SHM) system for automated quantification of crack damage. This study explores the use of cementitious materials as their own sensor platform capable of measuring mechanical behavior under loading. Fundamentally, this self-sensing functionality will be based upon electro-mechanical properties. First, the piezoresistivity of cementitious composites is quantified so as to establish the material as a multifunctional system capable of self-sensing. Second, electrical impedance tomography (EIT) is proposed for measuring internal strain fields using only electrical measurements taken along the boundary of the structural element. An inherent advantage of EIT is that it is a distributed sensing approach offering measurement of strain fields across 2D or 3D. Furthermore, the approach is well suited for imaging cracks which appear as conductivity reductions in EIT-derived conductivity maps. Finally, to validate the accuracy of the EIT technique, it is applied to fiber reinforced cementitious composite elements loaded by axial tension-compression cycles and 3-point bending.

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“…DC conductivity of 1.7 S m -1 was attained for specimen LF-M1.5. This value was greater than the electrical conductivity of 5 mm long carbon fiber-incorporated cement composites and carbon black-filled cement composites (Wen and Chung 2007;Li et al 2006). The high DC conductivity of the composites in the present section and the image analysis conducted in Sect.…”

Section: Conductivity Of the Composites Incorporating Various Mwntmentioning

confidence: 73%

Image Analysis and DC Conductivity Measurement for the Evaluation of Carbon Nanotube Distribution in Cement Matrix

Nam

Lee

2015

International Journal of Concrete Structures and Materials

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The present work proposes a new image analysis method for the evaluation of the multi-walled carbon nanotube (MWNT) distribution in a cement matrix. In this method, white cement was used instead of ordinary Portland cement with MWNT in an effort to differentiate MWNT from the cement matrix. In addition, MWNT-embedded cement composites were fabricated under different flows of fresh composite mixtures, incorporating a constant MWNT content (0.6 wt%) to verify correlation between the MWNT distribution and flow. The image analysis demonstrated that the MWNT distribution was significantly enhanced in the composites fabricated under a low flow condition, and DC conductivity results revealed the dramatic increase in the conductivity of the composites fabricated under the same condition, which supported the image analysis results. The composites were also prepared under the low flow condition (114 mm \ flow \ 126 mm), incorporating various MWNT contents. The image analysis of the composites revealed an increase in the planar occupation ratio of MWNT, and DC conductivity results exhibited dramatic increase in the conductivity (percolation phenomena) as the MWNT content increased. The image analysis and DC conductivity results indicated that fabrication of the composites under the low flow condition was an effective way to enhance the MWNT distribution.

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Effect of compressive strain on electrical resistivity of carbon black-filled cement-based composites

Cited by 268 publications

References 14 publications

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

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

Electrical Impedance Tomographic Methods for Sensing Strain Fields and Crack Damage in Cementitious Structures

Image Analysis and DC Conductivity Measurement for the Evaluation of Carbon Nanotube Distribution in Cement Matrix

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