Effects of carbon nanomaterial type and amount on self-sensing capacity of cement paste

Accepted Manuscript Effects of carbon nanomaterial type and amount on self-sensing capacity of cement paste Doo-Yeol Yoo, Ilhwan You, Goangseup Zi, Seung-Jung Lee PII: DOI: Reference:

S0263-2241(18)31078-9 https://doi.org/10.1016/j.measurement.2018.11.024 MEASUR 6067

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Measurement

Received Date: Revised Date: Accepted Date:

25 May 2017 18 October 2018 9 November 2018

Please cite this article as: D-Y. Yoo, I. You, G. Zi, S-J. Lee, Effects of carbon nanomaterial type and amount on self-sensing capacity of cement paste, Measurement (2018), doi: https://doi.org/10.1016/j.measurement. 2018.11.024

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1

Effects of carbon nanomaterial type and amount on self-sensing

2

capacity of cement paste

3 Doo-Yeol Yooa, Ilhwan Youb, Goangseup Zib, and Seung-Jung Leec*

4 5 6

ABSTRACT

7

This study investigates the implications of carbon nanomaterial type and amount on the electrical

8

properties of cement paste. For this, five different nanomaterials, i.e., carbon nanotube (CNT), carbon

9

fiber (CF), graphite nanofiber (GNF), graphene (G), and graphene oxide (GO), and two different volume

10

fractions of 0.5 and 1% were considered. In addition, the self-sensing capacity of the cement composites

11

with nanomaterials was evaluated under cyclic compressive loads. Test results indicate that the

12

conductivity of plain cement paste was improved by adding carbon nanomaterials. In most cases, the

13

conductivity of the composites was reduced by an increase in curing age and a decrease in nanomaterial

14

amount, except for CF. The composites with CNTs exhibited the best self-sensing capacity regardless of

15

volume fraction (vf), and the order of self-sensing capacity of the composites at a vf of 1% was CNT > GO

16

≈ GNF > G. The composites with 0.5 and 1 vol% CFs were determined to be not appropriate for a sensor

17

measuring compressive behaviors. The gauge factor of the composites incorporating 1 vol% CNTs was

18

obtained as 77.2‒95.5.

19 20

Keywords: Cement composites; electrical resistivity; self-sensing capacity; carbon nanomaterials; gauge

21

factor

22 23 24 25 26 27 28 29 30 31 32 33 34

––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– a Department of Architectural Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea. b School of Civil, Environmental and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea. c Advanced Railroad Civil Engineering Division, Korea Railroad Research Institute, 176 Cheoldo bangmulgwan-ro, Uiwang-si, Gyeonggi-do, 16105, Republic of Korea.

35 36

Corresponding author. Tel.: +82 31 460 5295, fax: +82 31 460 5022 E-mail address: [email protected] (D.-Y. Yoo), [email protected] (I. You), [email protected] (G. Zi), and [email protected] (S.-J. Lee) *

1

1. Introduction

2

In order to monitor the actual stress or strain state in reinforced concrete (RC) structures,

3

various structural health monitoring techniques have been introduced thus far [1-3]. Among

4

others, a cement-based piezoresistive sensor has attracted attention from researchers and

5

engineers in recent years [4-13], because the heterogeneity of other types of sensors

6

commercially available, i.e., electric strain gauge, fiber Bragg grating sensor, PZT-based

7

piezoelectric sensor, etc., included in concrete structures can be overcome by using a cement-

8

based sensor. The main idea of the cement-based sensor is to monitor stress/strain states in

9

concrete structures based on a fractional change in resistance, and consequently, the cement

10

composites must be conductive. However, plain cement pastes, mortars and concrete are

11

basically non-conductive materials, so to monitor the stress/strain states in RC structures,

12

conductive nanomaterials need to be included and continuously connected for electrical current

13

to flow. The electrical current only flows through continuous conductive pathways formed by

14

incorporating carbon-based nanomaterials in the cement composites.

15

For fabricating conductive cement composites, several types of nanomaterials have been used

16

by researchers [3,6,7,11,13,14-20]. Banthia et al. [6] compared the electrical properties of cement

17

composites reinforced with carbon fiber (CF) and steel fiber. In their study [6], the use of CFs

18

was much more effective at improving the conductivity of the cement composites than that of

19

steel fibers, because a more conductive fiber network was achieved in the composites with CFs

20

having a smaller diameter than that of steel fibers. Azhari and Banthia [7] also examined the

21

piezoresistive sensing capacity of cement composites with CFs and carbon nanotubes (CNTs).

22

The self-sensing capacities (i.e., signal quality, reliability, and sensitivity) of the cement

23

composites with single 15 vol% CFs were improved by hybrid use of 15 vol% CFs and 1 vol%

24

CNTs. Loamrat et al. [14] reported that CF is more appropriate for developing a cement-based

25

sensor than a graphite fiber. Even though the addition of graphite fiber into cement paste

26

decreased electrical resistivity, it deteriorated the compressive strength and exhibited a

27

fluctuating signal. In their study [14], the gauge factor of the cement composites with 2% CFs

28

was approximately 20. Kim et al. [10] investigated the piezoresistive capacity of cement

29

composites with CNTs. In their study [10], several useful findings were obtained as follows: (1)

30

low water-to-binder (W/B) ratio resulted in a better CNT’s dispersion and (2) piezoresistive

31

stability and sensitivity of cement composites was improved with decreasing the W/B ratio, due

32

to their better CNT networks. Han et al. [3] also investigated the self-sensing capacity of cement

33

composite including 0.1 wt% CNTs from laboratory and field road tests. Since the CNT/cement

34

composite provided sensitive and stable responses under repeated compressive loads, it was

35

considered to have great potential for traffic monitoring use. Li et al. [15] adopted carbon black

36

as a conductive material to make a cement-based sensor. The electrical resistivity of carbon

1

black-filled cement composites was stabilized when the amount of carbon black was beyond

2

11.39 vol%, and the gauge factors of the composites with 15‒25 vol% carbon black were found

3

to be between 37.71 and 55.28. On the other hand, the composites with 24 vol% nickel powder

4

exhibited much higher self-sensing sensitivity than that with carbon black, and their gauge

5

factors were obtained as 895.45‒1929.50, as reported by Han et al. [18]. From the experimental

6

results, Le et al. [17] also suggested the percolation threshold of two-dimensional (2-D)

7

graphene nanoplatelets (GNP) in cement composites to be 2.4 vol%, and they [17] tried to

8

quantify the extent of material damage based on a fractional change in resistance in a mode-I

9

load.

10

As explained above, several researchers have examined the feasibility of using various carbon

11

nanomaterials in cement composites for piezoresistive sensing or structural health monitoring.

12

However, to the best of the authors’ knowledge, there is no published study regarding the self-

13

sensing electrical properties of cement composites that include graphite nanofibers (GNFs). In

14

addition, only a few studies [17] have been performed on the self-sensing performance of

15

cement composites with graphene based materials, such as graphene (G), graphene oxide (GO),

16

and GNP. Therefore, a comprehensive study, which investigates the electrical properties and

17

self-sensing capacity of cement composites, including various types of carbon nanomaterials,

18

needs to be conducted.

19

Accordingly, this study comprehensively evaluated the effects of carbon nanomaterial type

20

and amount on the electrical properties and self-sensing capability of cement composites. For

21

this, five different types of nanomaterials, i.e., CNT, CF, GNF, G, and GO, and two different

22

volume fractions of 0.5 and 1% were considered. The detailed objectives were to examine (1) the

23

electrical resistivity of cement composites according to curing age, nanomaterial type and

24

amount and (2) self-sensing capacity of cement composites with various nanomaterials under

25

cyclic compression. Furthermore, a gauge factor indicating the sensitivity of a sensor was

26

suggested for the composites exhibiting the best self-sensing performance.

27 28

2. Experimental program

29

2.1 Mixture proportions

30

In order to make cement paste, Type 1 Portland cement and silica fume (SF) were first

31

included as cementitious materials. The chemical composition and physical properties of the

32

cementitious materials are summarized in Table 1. Thirty percent of the cement was replaced

33

with SF due to the following two reasons. According to the test results performed by Kim et al.

34

[21], the addition of SF can reduce the amount and size of large-sized pores, leading to an

35

improvement in the continuity level of conductive pathways in hydrated cement composites

36

including CNTs. In addition, Chiarello and Zinno [22] achieved a good dispersion of CFs in

1

concrete by including SF. The better fiber dispersion by SF added might be caused by increased

2

viscosity of fresh concrete mixture. The water-to-cementitious material ratio (w/cm) used was

3

0.35, and five different carbon-based nanomaterials, such as CNT, CF, GNF, G, and GO, were

4

incorporated into the mixture at volume fractions of 0.5 and 1%. It is well known that good

5

dispersion of CNTs is difficult to obtain due to their strong van der Waals forces [16]. Therefore,

6

to disperse CNT as well as other types of nanomaterials, a sonication process was used, based

7

on a Q500 Sonicator with a frequency of 20 kHz. In addition, the dispersion of nanomaterial is

8

strongly affected by the fluidity of the cement paste. Chung [23] has reported that the addition

9

of water reducing agent and accelerating admixture is effective for improving a dispersion of

10

short carbon fiber. Thus, the fluidity of both plain cement paste and cement composites with

11

nanomaterials was controlled by using a superplasticizer (SP) to ensure a similar value of 150 ±

12

10 mm, as per ASTM C1437 [24]. The detailed mixture proportions are given in Table 2.

13

The physical properties and geometrical shapes of the nanomaterials used are summarized in

14

Table 3 and Fig. 1. The CNT, CF, and GNF have similar cylindrical shapes with aspect ratios of

15

about 667, 857, and > 50, respectively. On the other hand, the G and GO were formed by

16

randomly aggregated, thin, crumpled sheets. The CNT used in this study has a carbon content

17

greater than 90%. Since the electrical resistance and sensing capacity of the cement composites

18

are influenced by the amount of conductive materials included, two volume fractions of 0.5 and

19

1% were considered. In particular, according to Chen and Chung [25], the percolation threshold

20

of CFs is between 0.5 and 1% by volume. Also, Wen and Chung [26] reported that the damage

21

self-sensing of carbon fiber reinforced cement from the measured strain and electrical resistance

22

is effective below the percolation threshold under uniaxial compression, and Wen and Chung

23

[27] denoted that the contents of CFs beyond the percolation threshold rather reduces the

24

piezoresistive effect of concrete. Thus, it was considered to be meaningful to adopt two different

25

volume fractions, nearby the percolation threshold values, so that the volume contents of

26

carbon-based materials including CF were determined to be 0.5 and 1% in this study.

27

The mixing sequence adopted in this study is as follows. The cementitious materials were

28

first included in a Hobart type mixer with a maximum capacity of about 10 L and premixed for

29

5 min to disperse those materials well. After that, a sonication process was applied to disperse

30

the nanomaterials. Then, sonicated water including both nanomaterials and SP were

31

incorporated into the mixer with dry cementitious materials, and the mixture was mixed for an

32

additional 5 min. Finally, the fluidity of the cement paste or composite was measured using a

33

flow table test by ASTM C1437 [24], and once the fluidity requirement was satisfied, the cube

34

specimens were fabricated.

35 36

2.2 Preparation of cube specimens

1

In order to evaluate the compressive behaviors of the composites, cube specimens with a

2

cross section of 50 × 50 mm2 and a height of 50 mm were fabricated and tested, as shown in Fig.

3

2. Four thin copper plates with width and height of 20 mm and 75 mm, respectively, were

4

embedded into cube specimens at intervals of 10 mm to measure resistance, which is a four-

5

electrode method. Since the cube specimens had a 50-mm height, the actual contact area

6

between the copper plate and cement composites was 20 × 50 mm2. A short vibration was

7

applied for all cube specimens to give good compactness. All of the specimens were cured in a

8

water tank with a temperature of 20 ± 1°C for 28 days and stored in a constant temperature and

9

relative humidity room with 20 ± 1 °C and 60 ± 5% for additional several days according to KS F

10

2424 [28]. The additional curing of the specimens was conducted in the chamber to have

11

adequate pore water contents as recommended by the KS standard because the electrical

12

conductivity of concrete is influenced by the pore water content.

13 14

2.3 Test setups

15

The electrical properties of the cement paste with and without nanomaterials were first

16

examined as a function of curing age (3, 7, 14, 21, and 28 days). For this, the two outer copper

17

plates were used to provide electrical current, and the two inner copper plates were used to

18

measure the voltage. The electrical data were obtained by using a GWINTEK 819 LCR meter.

19

Baoguo et al. [29] reported that a direct current increases resistance over time because of a

20

polarization effect. In order to overcome the problems of polarization, alternative current was

21

used at a frequency of 100 kHz, as suggested by Banthia et al. [6].

22

In order to investigate the implications of nanomaterial type and amount on the self-sensing

23

capacity of the cement composites, a cyclic compressive loading test was carried out. The

24

detailed test setup for the compressive test is shown in Fig. 3 and can be also found elsewhere

25

[30]. A cyclic uniaxial load was applied from a universal testing machine (UTM) with a

26

maximum load capacity of 250 ton. In order to measure the applied load, a load cell with a

27

maximum capacity of 2000 kN and a sensitivity of about 0.67 kN was adopted. A compressive

28

strain was measured from 10-mm foil strain gauges attached to both side surfaces of the cube

29

specimens. The load and strain data were acquired by using a static data logger. The rate of

30

loading was adapted to the specimens, as follows: LR = ± 0.333 × n (kN/s), where LR is the

31

loading rate and n is the loading stage. The detailed loading protocol is given in Fig. 4.

32

According to ASTM C109 [31], the loading rate is recommended to be a range of 0.9 and 1.8

33

kN/s. So, the highest loading rate of about 1 kN/s was determined in the stage III (Fig. 4). Due to

34

a limitation of instrumentation used, we couldn’t keep the loading rate constant at all loading

35

stages. It is known that the compressive strength of concrete increases with increasing the

36

loading rate in general [32], and also, several previous studies [33,34] have conducted the

1

compressive tests of cubic mortar specimens with a loading rate of about 0.3 kN/s. Therefore,

2

the loading rate of about 0.33 kN/s was determined for the stage I and it increased up to about 1

3

kN/s [31]. Even though the loading rate was changed, the difference of loading rate was small

4

enough to limit its effect on the compressive strength and electrical resistance variances. A 10

5

kN force was determined as the minimum compressive load to prevent unstable initial testing

6

data, mainly caused by the settling between the surface of the cube specimen and the steel plate.

7

For simulating the cyclic compressive stress and strain behaviors based on changes in electrical

8

resistance, the resistance in all of the cube specimens was continuously measured based on the

9

four-electrode method during testing. Since the variation of the resistance is influenced by the

10

direction between the load and the current [35], all the cube specimens were identically

11

designed to have the direction of current parallel to the loading direction.

12

In order to evaluate the pore size distributions, the tested cubic samples were first crushed.

13

Then, the crushed samples were dried at 50 °C for 7 days using oven to eliminate pore water.

14

After preparation of the dried samples, they were initially evacuated to approximately 50 𝜇m

15

mercury and a low pressure of 0.21 MPa was applied by nitrogen gas. After that, the pressure

16

gradually increased up to about 117 GPa with a rate of 9.1 × 103 kPa/s. Based on the measured

17

pressure, the pore diameter was calculated by Washburn equation, as given by d = -4γcosθ/P,

18

where d is the pore diameter, γ is the surface tension, θ is the contact angle, and P is the pressure,

19

and subsequently, the distribution of pore volume was calculated.

20 21

3. Experimental results and discussion

22

3.1 Effects of nanomaterial type, amount, and curing age on the electrical resistivity of cement

23

composites

24

The electrical resistivity of the cement composites incorporating various carbon

25

nanomaterials with curing age is shown in Fig. 5. The electrical resistivity was calculated based

26

on the measured resistance, as follows: ρ = R×A/l, where R is the resistance, A is the cross-

27

sectional area of the cement composites in contact with the electrode, and l is the space between

28

the two voltage poles. The electrical resistance of cement composites with carbon nanomaterials

29

is generated from the intrinsic resistance of the nanomaterials and the contact resistance [36]. In

30

particular, electrical current occurs via electrical tunneling between adjacent carbon

31

nanomaterials. Its resistance is dependent on several factors, i.e., tunneling gap, conductivity of

32

cement matrix, etc. [36]. As shown in Fig. 5, it was obvious that the plain cement paste exhibited

33

the highest electrical resistivity, and it was reduced with the addition of carbon nanomaterials.

34

This means that the conductivity of the cement paste can be improved by incorporating various

35

types of carbon nanomaterials. When the volume fraction of carbon nanomaterial was equal to

36

0.5%, the cement composites with CFs exhibited the lowest resistivity, while the composites

1

with CNTs exhibited the lowest resistivity at the volume fraction of 1%. This indicates that the

2

composites with 0.5 vol% CFs and 1 vol% CNTs were most conductive at each volume fraction.

3

It is interesting to note that the composites with CFs exhibited a higher resistivity at 3 days for

4

all volume fractions (0.5 and 1%) compared to other curing ages, and it was reduced as the

5

curing time increased from 3 days to 7 days. Thereafter, there was no obvious change in the

6

resistivity with age. This is exactly contrary to other cement composites, which exhibit a clear

7

increase in resistivity with curing age, due to the evaporation of pore water. As shown in Fig. 6

8

[8], electrical current effectively flows when the pores are saturated. However, due to the

9

different relative humidity between the interior of the composites and atmosphere, the pore

10

water continuously evaporates, and as a result, the pores dry out. Once the pores are dried, the

11

current can not flow if the conductive nanomaterials are disconnected, as shown in Fig. 6b. This

12

is consistent with the findings from Song and Choi [37]. Thus, in most cases, the electrical

13

resistivity of the composites with carbon nanomaterials is reduced with increasing age. In

14

addition, the electrical resistivity of the composites with CFs increased with increasing amounts

15

of CF, which is inconsistent with the other nanomaterials. This is because of the increased

16

porosity in the hydrated cement paste and fiber ball phenomenon. Li [38] reported that the total

17

porosity of hydrated cement paste increased after including the CFs. When 0.5% (by weight of

18

cement) CFs were incorporated, approximately 31% higher total porosity was obtained

19

compared to the plain cement paste. In order to support this explanation, the porosities of the

20

plain cement paste and composites with 0.5 and 1 vol% CFs were compared, as shown in Fig. 7.

21

The cumulative pore volume was obviously increased by including CFs and increasing their

22

amount. This might be caused by the fact that CFs were not perfectly dispersed in the matrix

23

and the insufficient dispersion of fibers was more pronounced at a higher volume fraction. Thus,

24

much higher volume of large pores (d > 100 nm) was obtained in the composites with CFs as

25

compared with the plain paste. The maximum cumulative pore volume for CF-1.0% was found

26

to be 0.2 mL/g, approximately 22% and 50% higher than those for CF-0.5% and plain cement

27

paste.

28

The electrical resistivity was most significantly reduced by increasing the volume fraction

29

from 0.5% to 1% in the case of CNT. For example, the electrical resistivity of the composites

30

with 1 vol% CNTs was found to be 117.7 Ω·cm at 28 days, which is approximately 94% lower

31

than that of the composites with 0.5 vol% CNTs at the identical age. This is caused by the fact

32

that more continuous conductive pathways were formed due to the increased amount of CNT

33

and the porosity in the composites was reduced due to the filling effect of nanotubes, which

34

was noted by Li et al. [38]. In addition, Yoo et al. [8] and D’Alessandro et al. [11] have suggested

35

percolation threshold values of the composites with CNTs as 1 vol% and 1.15 vol%, respectively.

36

Thus, the amount of CNTs (0.5 vol%) added in the composites was insufficient to form enough

1

continuous conductive pathways to allow electrical current to flow effectively. For the above

2

reasons, the resistivity of the composites with 0.5 vol% CNTs was obviously increased with age

3

due to pore water evaporation, whereas the composites with 1 vol% CNTs exhibited non-

4

significant changes in resistivity with age.

5

At a volume fraction of 0.5%, similar electrical resistivity was observed in the composites

6

with GNF, G, and GO regardless of age, which was slightly lower than that of the plain cement

7

paste. For instance, the resistivity of the plain cement paste was obtained as 62997 Ω·cm at 28

8

days, about 32%, 48%, and 49% lower than those with 0.5 vol% GNF, G, and GO, respectively.

9

However, their electrical resistivity was steeply increased with age, similar to the case of plain

10

cement paste. The resistivity was noticeably reduced by increasing the amount of G and GO

11

from 0.5 to 1 vol%. Approximately 50% lower resistivity was obtained in the composites at 28

12

days by increasing the amount of G and GO from 0.5 to 1 vol%. On the contrary, similar or

13

slightly higher electrical resistivity was observed in the composites when the amount of GNF

14

was increased from 0.5 to 1 vol%. Gong et al. [39] reported that a smaller porosity was obtained

15

in the composites with GO than the plain cement paste. Based on their study [39], the G and GO

16

might effectively fill the pores in the hydrated cement paste, and as a result, the reduced

17

porosity in the cement paste and conductive pathway formed by G and GO effectively reduced

18

the electrical resistivity. However, in the case of GNF, due to the pros and cons of increasing the

19

amount of GNF, such as improved connectivity of GNFs (pros) and increased porosity by

20

insufficient dispersion of GNF like CF (cons), no significant changes in electrical resistivity with

21

increased GNF were observed.

22 23

3.2 Comparative cyclic compressive load and FCR behaviors of the composites with age

24

To investigate the self-sensing capability of the composites with various carbon

25

nanomaterials, cyclic compressive tests were performed. The applied cyclic compressive force

26

versus time curve is shown in Fig. 4. Due to the unstable data for compressive load and

27

electrical resistance at the initial stage of loading, Stage II and III in Fig. 4 were used to analyze

28

the sensing capacity. The initial point of Stage II was thus considered as a ‘time-zero’ point, as

29

shown in Fig. 8. In addition, to quantitatively evaluate the self-sensing capacity of the

30

composites including nanomaterials, a fractional change in resistance was adopted, and it was

31

calculated by

32 33

ΔR/R0 = (Rx – R0)/R0

(1)

34 35

where R0 is the initial resistance of the cement composites and Rx is the resistance of the cement

36

composites under compressive load. If the dimensional changes of the specimens are negligible

1

during loading, the fractional change in resistivity (FCR) (Δρ/ρ0, where ρ0 is the initial resistivity)

2

is identical with the fractional change in resistance, and thus, Eq. (1) was used to evaluate the

3

self-sensing capability of the composites for simplification.

4

Figure 8 shows the comparison of load-time and FCR-time curves for all test specimens. By

5

increasing the compressive load, a reduced resistivity was obtained, which is attributed to the

6

fact that the nanomaterials are getting closer under compression, and as a result, more

7

conductive pathways are formed. Due to this, a minus value of FCR was initially measured.

8

However, in order to directly compare the behavior of FCR with the compressive load, -1 was

9

multiplied by the measured FCR, leading to a positive value for FCR (Fig. 8). Although a similar

10

cyclic compressive load was applied, different FCR behaviors were obtained in the composites

11

according to the type and amount of the nanomaterials. This means that the self-sensing

12

capacity of the composites is influenced by the type and amount of the nanomaterials. It was

13

obvious that the composites with CNTs exhibited the least noise and the most similar FCR

14

behaviors with the cyclic compressive loads regardless of the volume fraction. The FCR

15

increased upon loading, while it decreased upon unloading. This is caused by the fact that the

16

external load changes the shape and distance of CNTs, as reported by Wen and Chung [4]. This

17

denotes that the resistivity decreases with loading, while it increases with unloading. On the

18

other hand, other composites with CF, GNF, G, and GO showed unintended fluctuations in the

19

FCR curves, indicating data noise, even though the magnitude of the fluctuations differed

20

according to the type of nanomaterials. In the case of the composites with CFs (Fig. 8a and b),

21

the cyclic compressive load versus time curves were better simulated with the measured FCR

22

when a higher amount (1 vol%) of CFs was included. This is inconsistent with the findings of

23

electrical resistivity in the composites including CFs without external load, as shown in Fig. 5;

24

the composites with a higher amount of CFs exhibited lower conductivity than those with a

25

smaller amount of CFs. This indicates that the resistivity (or conductivity) of the cement

26

composites with CFs is not directly related to the self-sensing capacity under cyclic compression.

27

In particular, the composites with 0.5 vol% CFs exhibited unrealistic FCR behaviors from 130 s

28

to 350 s. A fluctuated but flat FCR-time curve was obtained in this range although a cyclic

29

compressive load was continuous applied. The cyclic compressive load in the composites with 1

30

vol% CFs was better simulated with the FCR as compared with those having a smaller amount

31

(0.5 vol%) of CFs. In general, the FCR in the composites with CFs continuously decreased with

32

increasing time, even if the minimum load was fixed and the loading and unloading behaviors

33

were well reflected by the FCR. This might be attributed to the rearrangement of the CFs with

34

formation of microcracks and residual deformation under cyclic compressive loads. In order to

35

more clearly understand this observation, however, further study is required.

36

The composites with CNTs can be simulated the cyclic loading/unloading behaviors based on

1

the measured FCR, as shown in Fig. 8c and d. However, as the compressive load increased, a

2

residual value of FCR was obtained, indicating partial reversibility. The partial reversibility of

3

FCR in the composites with CNTs was also obtained in the previous studies [9]. The irreversible

4

FCR might be attributed to the rearrangement of CNTs under cyclic loads, formation of

5

microcracks, and movement of water in pores. It was clear that the residual FCR was reduced

6

with increasing levels of CNTs. In addition, the cyclic compressive load versus time relationship

7

was better simulated with the FCR for composites with more CNTs: particularly, the sharp load

8

change at the peak points was obviously better simulated with the composites containing 1 vol%

9

CNTs than those with 0.5 vol% CNTs.

10

In the case of GNF, the cyclic compressive load in the composites with 1 vol% GNFs was

11

relatively well predicted with the FCR, compared to the composites with 0.5 vol% GNFs.

12

Similar to the case with CF, this also denotes that the self-sensing capacity of the composites

13

with GNF is not directly dependent on their conductivity. The electrical resistivity of the

14

composites with GNF was not changed according to its volume content, whereas a better

15

simulation of the cyclic compressive load was obtained in the composites with a higher amount

16

(1 vol%) of GNFs. The FCR obtained in the composites with 0.5 vol% GNFs was continuously

17

decreased with time, indicating a continuous increase in the resistivity, which is inconsistent

18

with the behavior of the applied compressive load. A similar observation was also obtained in

19

the composites with 1 vol% CFs in Fig. 8b.

20

As shown in Fig. 8g and i, the FCR in the composites with 0.5 vol% G and GO was obviously

21

increased and decreased with loading and unloading behaviors. This means that the resistivity

22

varies successfully with the loading condition. However, the FCR was also continuously

23

reduced with time, although the minimum cyclic compressive load was not decreased but fixed,

24

which is consistent with the findings of the composites with 1 vol% CF and 0.5 vol% GNF. The

25

continuous decrease of the FCR was prevented by increasing the amount of G and GO from 0.5

26

to 1 vol%, as shown in Fig. 8h and j. The increase and decrease in the compressive loads were

27

well simulated with the FCR measured in the composites with 1 vol% G (Fig. 8h). However,

28

severe data noise was obtained in the FCR case. The data noise in the FCR was significantly

29

mitigated with the use of GO (Fig. 8j). In addition, a higher increase in the FCR at similar

30

compressive load was obtained in the composites with 1 vol% GO than those with 1 vol% G.

31

This means that the sensor made of the composites with GO is more sensitive to the external

32

load than its counterpart including G.

33

From the above observations, several important conclusions may be drawn.

34

(1) The cement composites with 0.5 vol% CFs are not appropriate for a sensor measuring

35 36

cyclic compressive stress. (2) Unintended continuous increase in resistivity under a cyclic compressive load was

1

observed in the composites with 1 vol% CF and 0.5 vol% GNF, G, and GO. This was

2

prevented by increasing the amount of GNF, G, and GO to 1% by volume.

3 4 5 6

(3) The composites with CNT exhibited the best self-sensing capacity for the cyclic compressive load regardless of the volume content, for both 0.5 and 1 vol%. (4) The self-sensing capacity of the composites with CF and GNF was not directly related to their conductivity.

7 8

3.3 Compressive stress vs. FCR relationship

9

Figures 9 and 10 exhibit the relationship between the cyclic compressive stress and FCR for

10

composites with various carbon nanomaterials. The slope in the relationship between the FCR

11

and compressive stress indicates the sensitivity of the composites as sensors for measuring

12

compressive behavior. For the composites with 0.5 vol% CF, the relationship is severely

13

scattered due to the unrealistic FCR behaviors from 130 s to 350 s (Fig. 8a), and thus, the linear

14

relationships between stress and FCR were not analyzed. It was obvious that the composites

15

with 0.5 vol% GNF, G and GO and 1 vol% CF provided smaller values of FCR in Stage III

16

compared to those in Stage II, because of the increased resistivity under the cyclic compressive

17

load. However, there was no clear trend on the slope of the FCR-stress curve according to the

18

magnitude of the load. Thus, the composites with 0.5 vol% GNF and 1 vol% CF showed a

19

higher slope in Stage III, whereas the composites with 0.5 vol% G exhibited a similar value and

20

the composites with 0.5 vol% GO exhibited a smaller value in Stage III compared to those in

21

Stage II. At a volume fraction of 0.5%, the composites with CNT provided the highest slope

22

values, 0.004–0.0063/MPa, followed by the composites with G, GO, and GNF. In addition, the

23

highest coefficient of determination (R2) was obtained in the composites with CNT, meaning

24

that the FCR-stress curve was least scattered.

25

At the volume fraction of 1%, the highest slope in the FCR-stress curve was obtained in the

26

composites with CF. Even though they were most sensitive to the stress, they were not

27

considered to be appropriate cement composites for a sensor measuring compressive behavior

28

due to the continuous decrease in FCR. On the other hand, the composites with CNT have

29

slightly lower slopes between 0.0074 and 0.0085 /MPa than the previous one, but an unintended

30

continuous decrease in the FCR was not observed and the highest average value of R2 was

31

obtained. The sensing sensitivity of the composites for compressive stress increased with CNT

32

concentration. The composites with G and GO exhibited similar conductivity in Fig. 5b.

33

However, the one with GO exhibited better self-sensing capacity, including higher values of the

34

slope and R2, compared to that with G. In addition, the self-sensing capacity of the composites

35

with GNF was significantly improved by increasing its amount, and as a result, they exhibited

36

better sensing behavior than the one with G. Syntactically, the self-sensing capacity of the

1

composites at vf of 1%, considering the magnitudes of the slope and R2, was as follows: CNT >

2

GO ≈ GNF > G. In addition, the addition of 1 vol% CF was insufficient for the cement sensor

3

measuring the compressive behavior. Chen and Chung [5] reported that the percolation

4

threshold of the CF is between 0.5 and 1 vol%. However, based on the test results performed by

5

Banthia et al. [6], the electrical resistivity of the composites including CF was significantly

6

decreased from 1 vol% to 3 vol%, which means that the percolation threshold of the CF is larger

7

than 1 vol%. Thus, Azhari and Banthia [7] added 15 vol% CFs into the cement paste for

8

evaluating the compressive behaviors based on the FCR parameter. In accordance with

9

Banthia’s research [6,7], the 1 vol% CF used in this study was judged to be insufficient for

10

simulating the cyclic compressive behavior of the composites.

11 12

3.4 Gauge factor of the composites with CNTs

13

Based on the test results mentioned above, the best self-sensing capacity was achieved in the

14

composites with 1 vol% CNTs. Therefore, its feasibility for use as a sensor for estimating the

15

strain state in the composites under cyclic compression was investigated, as shown in Fig. 11.

16

The cyclic compressive strains were measured from two strain gauges attached to the side

17

surfaces of cube specimens in Fig. 3, and an average value was used in Fig. 11. The cyclic

18

compressive strains were well simulated with the FCR in the composites with 1 vol% CNTs,

19

similar to the case of previous compressive stress. In order to quantitatively evaluate the

20

feasibility of using the composites as a sensor, a gauge factor (GF), which indicates the

21

sensitivity of the sensor, was calculated based on the following equation.

22 23

𝐺𝐹 =

∆𝜌/𝜌0 𝜀

=

𝐹𝐶𝑅 𝜀

(2)

24 25

where Δρ is the variation in resistivity, ρ0 is the initial resistivity, and ε is the compressive strain.

26

Thus, GF denotes a slope of the FCR-strain curve.

27

Figure 12 shows the relationship between the FCR and cyclic compressive strain of the CNT-

28

based composites. The FCR exhibited almost a linear relationship with the strain, and the data

29

exhibited minimal scatter. The GFs in Stage II and III were calculated by simple linear

30

regression analysis based on a least square error method. As shown in Fig. 12, the GF of the

31

composites with 1 vol% CNTs ranged from 77.2 to 95.5 with a minimum R2 of 0.9382. The GF of

32

the copper-nickel alloy-based strain gauge was approximately 2 [40]. This indicates that the

33

cement composites with 1 vol% CNTs have a much higher value of GF than that of the strain

34

gauge commercially available, and the composites are considered to be much more sensitive to

35

compressive strain than the strain gauge. The GF of the composites with 1 wt% (by weight of

1

cement) CNTs was reported to be 130 [11]. If it is assumed that the CNT used in their study [11]

2

has density identical to that of those in this study, 1 wt% is converted to 1.15 vol%, which is

3

higher than the amount (1 vol%) of CNT adopted. Therefore, a higher amount of CNT can be

4

considered to result in a higher GF in the cement composites. Loamrat et al. [14] reported that

5

the GF of the composites with 2 vol% CFs was 20, and Li et al. [15] noted that the GF of the

6

carbon black-filled composites is between 33.71 and 55.28. Even though a higher amount of CF

7

was included in the cement composites, it exhibited a much smaller value of GF compared to

8

those with CNT. This is consistent with the findings from the cyclic compressive stress test

9

results discussed above. For these comparisons, it can be concluded that the use of CNT is more

10

effective at improving the sensitivity of the cement composites to compressive strain, as

11

compared to the effects of CF and carbon black on compressive strain.

12 13

4. Conclusions

14

In this study, the effects of carbon nanomaterial type and amount on the electrical properties

15

of cement paste were examined. The self-sensing capacity of the cement composites with five

16

different carbon nanomaterials, i.e., carbon nanotube (CNT), carbon fiber (CF), graphite

17

nanofiber (GNF), graphene (G), and graphene oxide (GO), was also investigated under cyclic

18

compression. From the above discussions, the following conclusions are drawn:

19

1) Electrical resistivity of plain cement paste was reduced by including the nanomaterials. The

20

use of CF was most effective at improving the conductivity of cement composites at low

21

volume fraction, while the addition of CNT was most efficient at improving the

22

conductivity at high volume fraction.

23 24 25 26 27 28

2) The electrical resistivity of the composites increased with curing age and by decreasing the amount of the nanomaterials, except for CF. 3) The self-sensing capacity of cement composites including CF and GNF under compression was not directly affected by their conductivity. 4) The composites with CNTs provided the best self-sensing capacity under cyclic compressive force at both 0.5 and 1 vol%.

29

5) The order of self-sensing capacity in the cement composites at volume fraction of 1% was as

30

follows: CNT > GO ≈ GNF > G. The amounts of CFs such as 0.5 and 1 vol% were insufficient

31

for obtaining self-sensing capacity under compression.

32

6) The gauge factor of the composites with 1 vol% CNTs was found to be from 77.2 to 95.5.

33

The use of CNT was more effective at improving the sensing sensitivity of the cement

34

composites to compressive strain compared to those of CF and carbon black.

35 36

Acknowledgements

1

This research was supported by a grant (16CTAP-C117247-01) from Technology Advancement

2

Research Program funded by Ministry of Land, Infrastructure and Transport of Korean

3

government.

4 5

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[32] Bischoff PH, Perry SH. Compressive behaviour of concrete at high strain rates. Mater Struct 1991;24(6):425–450. [33] Geng J, Sun Q, Zhang W, Lü C. Effect of high temperature on mechanical and acoustic emission properties of calcareous-aggregate concrete. Appl Therm Eng 2016;106:1200–1208. [34] Feng P, Meng X, Chen JF, Ye L. Mechanical properties of structures 3D printed with cementitious powders. Constr Build Mater 2015;93:486–497. [35] Teomete E. Transverse strain sensitivity of steel fiber reinforced cement composites tested by compression and split tensile tests. Constr Build Mater 2014;55:136–145. [36] Han B, Yu X, Ou J. Effect of water content on the piezoresistivity of MWNT/cement composites. J Mater Sci 2010;45(14):3714–3719. [37] Song C, Choi S. Moisture-dependent piezoresistive responses of CNT-embedded cementitious composites. Compos Struct 2017;170:103–110. [38] Li GY, Wang PM, Zhao X. Mechanical behavior and microstructure of cement composites incorporating surface-treated multi-walled carbon nanotubes. Carbon 2005;43(6):1239–1245. [39] Gong K, Pan Z, Korayem AH, Qiu L, Li D, Collins F, Wang CM, Duan WH. Reinforcing effects of graphene oxide on portland cement paste. J Mater Civil Eng 2014;27(2):A4014010. [40] http://www.kyowa-ei.com/eng/download/technical/strain_gages/pdf_index_001_eng.pdf. List of Figures Fig. 1 Geometrical shape of nanomaterials; (a) CNT, (b) CF, (c) GNF, (d) G, (e) GO Fig. 2 Cube specimens (unit: mm) Fig. 3 Test setup for cyclic compressive tests Fig. 4 Loading protocol Fig. 5 Electrical resistivity of cement composites; (a) vf of 0.5%, (b) vf of 1.0% Fig. 6 Effect of pore water on electrical current flow; (a) saturated, (b) non-saturated (CNM = carbon nanomaterial) Fig. 7 Pore volume distribution for plain cement paste and composites with CFs; (a) increment, (b) accumulation Fig. 8 Comparative load- and FCR-time curves Fig. 9 Relationship between cyclic compressive stress and FCR (vf of 0.5%) Fig. 10 Relationship between cyclic compressive stress and FCR (vf of 1.0%) Fig. 11 Comparative compressive strain- and FCR-time curves for composites with 1 vol% CNT Fig. 12 Relationship between cyclic compressive strain and FCR for composites with 1 vol% CNT

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

Carbon fibers

Carbon nanotubes

26 27

(a)

(b)

Graphite nanofiber filament

1 2

Graphene particle

(c)

(d)

Graphene oxide particle 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

(e) Fig. 1 Geometrical shape of nanomaterials; (a) CNT, (b) CF, (c) GNF, (d) G, (e) GO

Copper plates 50 50

50

Cement-based composites

18 19

Fig. 2 Cube specimens (unit: mm)

1 2 3 4

Compressive force

Strain gauge

LCR meter

Data logger

V AC

Computer

Computer

5

P Strain gauge

Copper plate

Compressive force Probes for V

Load cell

Probes for AC

6 7 8 9 10 11

Fig. 3 Test setup for cyclic compressive tests

50 Stage III

Load (kN)

40 Stage II

30 Stage I

20 10 0 0

400

600

800

1000

Time(s) Fig. 4 Loading protocol

Resistivity (Ω·cm)

12 13 14 15 16 17 18 19 20 21

200

100000

Plai n paste CF CNT GNF G GO

10000 1000 100 10 0

22

5

10

15

20

Age (days)

25

30

(a) Resistivity (Ω·cm)

1

100000

Plai n paste CF CNT GNF G GO

10000 1000 100 10 0

2 3 4 5 6 7 8

5

10

15

20

25

30

Age (days)

(b) Fig. 5 Electrical resistivity of cement composites; (a) vf of 0.5%, (b) vf of 1.0%

Cement paste

: Current

CNM

9 10

Pore filled with water

(a) Cement paste

: Current Disconnected

CNM

11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Pore without water

(b) Fig. 6 Effect of pore water on electrical current flow; (a) saturated, (b) non-saturated (CNM = carbon nanomaterial)

Incremental pore volume (mL/ g)

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 34

Cement paste CF-0.5% CF-1.0%

0.01 0.008 0.006 0.004 0.002 0 1

10

100

1000

10000

100000 1000000

Pore diameter (nm)

(a) Cumulative pore volume (mL/ g)

1 2

0.012

0.25

Cement paste CF-0.5% CF-1.0%

0.2 0.15 0.1 0.05 0 1

10

100

1000

10000

100000 1000000

Pore diameter (nm)

(b) Fig. 7 Pore volume distribution for plain cement paste and composites with CFs; (a) increment, (b) accumulation

15

40

10 5 0

0.3

20

0.2

10

0.1

0

0

15

40

10 5

250

300

350

400

450

500

550

600

650

0.2 0.15 0.1 0.05 0 -0.05 -0.1

30 20 10 0 50

100

150

200

250

300

350

400

450

500

550

600

650

Time (s) 50

15

40

10 5

0.24 0.22 0.2 0.18 0.16 0.14 0.12

Comp. load FCR

30 20 10 0 50

100

150

200

250

300

350

400

450

500

550

600

FCR

20

Load (kN)

Stress (MPa)

(b) CF-1.0%

650

Time (s) 50

15

40

10 5

Comp. load FCR

0.26 0.22

30 20

0.18

10

0.14

0

FCR

20

Load (kN)

Stress (MPa)

(c) CNT-0.5%

0

0.1 0

50

100

150

200

250

300

350

400

450

500

550

600

650

Time (s) 50

15

40

10 5

0.06

Comp. load FCR

0.055

30

0.05

20

FCR

20

Load (kN)

Stress (MPa)

(d) CNT-1.0%

0

0.045

10 0

0.04 0

50

100

150

200

250

300

350

400

450

500

550

600

650

Time (s) 50

15

40

10 5

Comp. load FCR

0.085 0.075

30 20

0.065

10

0.055

0

0.045 0

50

100

150

200

250

300

350

Time (s)

400

450

500

550

600

650

FCR

20

Load (kN)

Stress (MPa)

(e) GNF-0.5%

0

11

200

Comp. load FCR

0

9 10

150

FCR

50

Load (kN)

Stress (MPa)

20

0

7 8

100

Time (s)

0

5 6

50

(a) CF-0.5%

0

3 4

0.4

30

0

1 2

0.5

Comp. load FCR

FCR

50

Load (kN)

Stress (MPa)

20

(f) GNF-1.0% 50

15

40

10 5

0.04

20

0.02

10 0

0 0

150

200

250

300

350

400

450

500

550

600

650

15

40

10 5

Comp. load FCR

0.06

30

0.05

20

FCR

50

Load (kN)

Stress (MPa)

20

0.04

10 0

0.03 50

100

150

200

250

300

350

400

450

500

550

600

650

Time (s) 50

15

40

10 5

0.07 0.06 0.05 0.04 0.03 0.02 0.01

Comp. load FCR

30 20 10 0 0

50

100

150

200

250

300

350

400

450

500

550

600

FCR

20

Load (kN)

Stress (MPa)

(h) G-1.0%

0

650

Time (s) 50

15

40

10 5

0.07

Comp. load FCR

0.06

30

0.05

20

0.04

10 0

0.03 0

50

100

150

200

250

300

350

Time (s)

(j) GO-1.0% Fig. 8 Comparative load- and FCR-time curves

400

450

500

550

600

650

FCR

20

Load (kN)

Stress (MPa)

(i) GO-0.5%

0

8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

100

(g) G-0.5%

0

6 7

50

Time (s)

0

4 5

0.06

30

0

2 3

0.08

Comp. load FCR

FCR

20

Load (kN)

Stress (MPa)

1

1 2 0.5

FCR

0.4 0.3 0.2 Stage II Stage II I

0.1 0 0

3

9

12

15

18

Compressivestress(MPa)

FCR

3

0.24 0.22 0.2 0.18 0.16 0.14 0.12

y = 0.004x + 0.1534 R² = 0.5896

y = 0.0063x + 0.118 R² = 0.8526

0

4 5

6

3

6

9

12

15

18

Compressivestress(MPa) (a)

(b) 0.06 y = 0.0004x + 0.0467 R² = 0.3608

FCR

0.055 0.05 0.045

y = 0.0008x + 0.0398 R² = 0.4437

0.04 0

3

6

9

12

15

18

Compressivestress(MPa)

6

0.08 y = 0.0025x + 0.0257 R² = 0.61

FCR

0.06 0.04

y = 0.0025x + 0.0096 R² = 0.7446

0.02 0 0

9

12

15

18

Compressivestress(MPa) (d) 0.07 0.06 0.05 0.04 0.03 0.02 0.01

y = 0.0019x + 0.0255 R² = 0.5023

y = 0.0015x + 0.0184 R² = 0.5407

0

9 10 11 12

6

(c)

FCR

7 8

3

3

6

9

12

15

18

Compressivestress(MPa) (e) Fig. 9 Relationship between cyclic compressive stress and FCR (vf of 0.5%); (a) CF, (b) CNT, (c) GNF, (d) G, (e) GO

FCR

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 0.2 0.15 0.1 0.05 0 -0.05 -0.1

Stage II Stage II I

y = 0.0088x - 0.0177 R² = 0.7714

y = 0.0118x - 0.1141 R² = 0.7736

0

3

6

9

12

15

18

Compressivestress(MPa)

22

y = 0.0074x + 0.1312 R² = 0.827

FCR

0.26 0.22 0.18

y = 0.0085x + 0.1056 R² = 0.9276

0.14 0.1 0

23 24

3

6

9

15

18

(a)

(b) y = 0.0015x + 0.0526 R² = 0.5951

FCR

0.085 0.075 0.065 0.055

y = 0.0015x + 0.0504 R² = 0.672

0.045 0

25

12

Compressivestress(MPa)

3

6

9

12

Compressivestress(MPa)

15

18

0.07 y = 0.001x + 0.0378 R² = 0.4746

FCR

0.06 0.05 0.04

y = 0.001x + 0.0355 R² = 0.4555

0.03 0

1 2

3

6

9

15

18

Compressivestress(MPa) (c)

(d) 0.07

y = 0.0025x + 0.0304 R² = 0.9179

FCR

0.06 0.05 0.04

y = 0.0011x + 0.0331 R² = 0.7468

0.03 0

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

12

3

6

9

12

15

18

Compressivestress(MPa) (e) Fig. 10 Relationship between cyclic compressive stress and FCR (vf of 1.0%); (a) CF, (b) CNT, (c) GNF, (d) G, (e) GO

0.26

Comp. strain FCR

0.0012

0.22

0.0008

0.18

0.0004

0.14

0

0.1 0

1 2 3 4 5 6

50

100

150

200

300

350

400

450

500

550

600

Fig. 11 Comparative compressive strain- and FCR-time curves for composites with 1 vol% CNT

y = 77.234x + 0.1356

FCR

R² = 0.9382

0.22 0.18

y = 95.536x + 0.1128

0.14

R² = 0.9712

0.1 0

0.0004

0.0008

0.0012

Stage II Stage II I

0.0016

0.002

Compressivestrain (ε) Fig. 12 Relationship between cyclic compressive strain and FCR for composites with 1 vol% CNT

9 10

List of Tables

11

Table 1 Chemical compositions and physical properties of cementitious materials

12

Table 2 Mixture proportions

13 14 15 16 17 18

Table 3 Properties of carbon nanomaterials

Table 1 Chemical compositions and physical properties of cement and silica fume Composition % (mass) Cement Silica fume CaO Al2O3 SiO2 Fe2O3 MgO SO3 Specific surface area (cm2/g) Density (g/cm3) Ig. loss (%)

19 20 21 22 23

650

Time (s)

0.26

7 8

250

Table 2 Mixture proportions vf w/cm*

61.33 6.40 21.01 3.12 3.02 2.30 3,413 3.15 1.40

0.38 0.25 96.00 0.12 0.10 200,000 2.10 1.50

Unit weight (kg/m3)

FCR

Strain (ε)

0.0016

1 2 3 4 5 6 7 8 9 10 11 12 13

(%) Water Cement SF CNT CF GNF G GO Plain paste 0.5 8 CNT 1 16 0.5 12 CF 1 24 0.5 13 0.35 708 1,416 607 GNF 1 26 0.5 14 G 1 27 0.5 15 GO 1 30 [Note] CNT = carbon nanotube, CF = carbon fiber, GNF = graphite nanofiber, G = graphene, GO = graphene oxide, vf = volume fraction, w/cm = water-to-cementitious material ratio, and SF = silica fume

1

Table 3 Properties of carbon nanomaterials Diameter, df (nm)

2 3 4 5 6 7 8 9 10 11 12 13

Length, Lf (mm)

Thickness (mm)

Layer

Carbon content (%)

Aspect Ratio (Lf/df)

CNT 15 0.01 3.4-7 > 90 667 CF 7,000 6 ≈93 857 GNF 200 0.01-0.03 > 90 > 50 G 3-6 3-10 > 99 GO 3.4-7 5-10 >90 [Note] CNT = carbon nanotube, CF = carbon fiber, GNF = graphite nanofiber, G = graphene, and GO = graphene oxide 0): Ruoff R.S., Qian D., Liu W.K., Mechanical properties of carbon nanotubes: theoretical predictions and experimental measurements. C. R. Phys. 2003, 4(9), 993–1008. 1): Arshad S.N., Naraghi M., Chasiotis I. Strong carbon nanofibers from electrospun polyacrylonitrile. Carbon. 2011, 49(5), 1710–1719. 2): Lee C., Wei X., Kysar J.W., Hone J. Measurement of the elastic properties and intrinsic strength of monolayer graphene. Sci. 2008, 321(5887), 385–388. 3): Cao C., Daly M., Singh C.V., Sun, Y., Filleter, T. High strength measurement of monolayer graphene oxide. Carbon. 2015, 81, 497-504. 4): Suk, J. W., Piner, R. D., An, J., & Ruoff, R. S.. Mechanical properties of monolayer graphene oxide. ACS nano, 2010, 4.11: 6557-6564.

14 15

Research highlights:

16 17

• Best self-sensing capacity of cement composites is achieved by using CNTs.

18 19

• Electrical resistivity increases with curing age and reduction of nanomaterial amounts.

20 21

• There is no direct relationship between conductivity and piezoresistive sensing capacity.

22 23

• A gauge factor of cement composites with 1 vol% CNTs is obtained as 77.2‒95.5.

24 25

• CNT-based cement composites is more sensitive to compressive stress than commercial

26

strain gauges.

27

29

ft (GPa) 11-630) 4.9 1.86-3.521) 1302) 24.73)