Abstract
The concrete production processes including materials mixing, pumping, transportation, injection, pouring, moulding and compaction, are dependent on the rheological properties. Hence, in this research, the rheological properties of fresh cement paste with different content of graphene (0.03, 0.05 and 0.10% by weight of cement) were investigated. The parameters considered were test geometries (concentric cylinders and parallel plates), shear rate range (300–0.6, 200–0.6 and 100–0.6 s−1), resting time (0, 30 and 60 min) and superplasticizer dosage (0 and 0.1% by weight of cement). Four rheological prediction models such as Modified Bingham, Herschel–Bulkley, Bingham model and Casson model were chosen for the estimation of the yield stress, plastic viscosity and trend of the flow curves. The effectiveness of these rheological models in predicting the flow properties of cement paste was verified by considering the standard error method. Test results showed that the yield stress and the plastic viscosity increased with the increase in graphene content and resting time while the yield stress and the plastic viscosity decreased with the increase in the dosage of superplasticizer. At higher shear rate range, the yield stress increased while the plastic viscosities decreased. The Herschel–Bulkley model with the lowest average standard error and standard deviation value was found to best fit the experimental data, whereas, Casson model was found to be the most unfitted model. Graphene reduces the flow diameter and electrical resistivity up to 9.3 and 67.8% and enhances load carrying capacity and strain up to 16.7 and 70.1% of the composite specimen as compared with plain cement specimen. Moreover, it opened a new dimension for graphene-cement composite as smart sensing building construction material.
| Original language | English |
|---|---|
| Article number | 64 |
| Journal | International Journal of Concrete Structures and Materials |
| Volume | 12 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Dec 1 2018 |
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Keywords
- graphene nanoplatelets
- piezoresistive properties
- rheological models
- rheological properties
- self-sensing
- yield stress
ASJC Scopus subject areas
- Civil and Structural Engineering
- Ocean Engineering
Cite this
Assessment of Rheological and Piezoresistive Properties of Graphene based Cement Composites. / Rehman, Sardar Kashif Ur; Ibrahim, Zainah; Jameel, Mohammad; Memon, Shazim Ali; Javed, Muhammad Faisal; Aslam, Muhammad; Mehmood, Kashif; Nazar, Sohaib.
In: International Journal of Concrete Structures and Materials, Vol. 12, No. 1, 64, 01.12.2018.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Assessment of Rheological and Piezoresistive Properties of Graphene based Cement Composites
AU - Rehman, Sardar Kashif Ur
AU - Ibrahim, Zainah
AU - Jameel, Mohammad
AU - Memon, Shazim Ali
AU - Javed, Muhammad Faisal
AU - Aslam, Muhammad
AU - Mehmood, Kashif
AU - Nazar, Sohaib
PY - 2018/12/1
Y1 - 2018/12/1
N2 - The concrete production processes including materials mixing, pumping, transportation, injection, pouring, moulding and compaction, are dependent on the rheological properties. Hence, in this research, the rheological properties of fresh cement paste with different content of graphene (0.03, 0.05 and 0.10% by weight of cement) were investigated. The parameters considered were test geometries (concentric cylinders and parallel plates), shear rate range (300–0.6, 200–0.6 and 100–0.6 s−1), resting time (0, 30 and 60 min) and superplasticizer dosage (0 and 0.1% by weight of cement). Four rheological prediction models such as Modified Bingham, Herschel–Bulkley, Bingham model and Casson model were chosen for the estimation of the yield stress, plastic viscosity and trend of the flow curves. The effectiveness of these rheological models in predicting the flow properties of cement paste was verified by considering the standard error method. Test results showed that the yield stress and the plastic viscosity increased with the increase in graphene content and resting time while the yield stress and the plastic viscosity decreased with the increase in the dosage of superplasticizer. At higher shear rate range, the yield stress increased while the plastic viscosities decreased. The Herschel–Bulkley model with the lowest average standard error and standard deviation value was found to best fit the experimental data, whereas, Casson model was found to be the most unfitted model. Graphene reduces the flow diameter and electrical resistivity up to 9.3 and 67.8% and enhances load carrying capacity and strain up to 16.7 and 70.1% of the composite specimen as compared with plain cement specimen. Moreover, it opened a new dimension for graphene-cement composite as smart sensing building construction material.
AB - The concrete production processes including materials mixing, pumping, transportation, injection, pouring, moulding and compaction, are dependent on the rheological properties. Hence, in this research, the rheological properties of fresh cement paste with different content of graphene (0.03, 0.05 and 0.10% by weight of cement) were investigated. The parameters considered were test geometries (concentric cylinders and parallel plates), shear rate range (300–0.6, 200–0.6 and 100–0.6 s−1), resting time (0, 30 and 60 min) and superplasticizer dosage (0 and 0.1% by weight of cement). Four rheological prediction models such as Modified Bingham, Herschel–Bulkley, Bingham model and Casson model were chosen for the estimation of the yield stress, plastic viscosity and trend of the flow curves. The effectiveness of these rheological models in predicting the flow properties of cement paste was verified by considering the standard error method. Test results showed that the yield stress and the plastic viscosity increased with the increase in graphene content and resting time while the yield stress and the plastic viscosity decreased with the increase in the dosage of superplasticizer. At higher shear rate range, the yield stress increased while the plastic viscosities decreased. The Herschel–Bulkley model with the lowest average standard error and standard deviation value was found to best fit the experimental data, whereas, Casson model was found to be the most unfitted model. Graphene reduces the flow diameter and electrical resistivity up to 9.3 and 67.8% and enhances load carrying capacity and strain up to 16.7 and 70.1% of the composite specimen as compared with plain cement specimen. Moreover, it opened a new dimension for graphene-cement composite as smart sensing building construction material.
KW - graphene nanoplatelets
KW - piezoresistive properties
KW - rheological models
KW - rheological properties
KW - self-sensing
KW - yield stress
UR - http://www.scopus.com/inward/record.url?scp=85054318156&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054318156&partnerID=8YFLogxK
U2 - 10.1186/s40069-018-0293-0
DO - 10.1186/s40069-018-0293-0
M3 - Article
VL - 12
JO - International Journal of Concrete Structures and Materials
JF - International Journal of Concrete Structures and Materials
SN - 1976-0485
IS - 1
M1 - 64
ER -