Monitoring early age strength gain of SCC with different supplementary cementitious materials using acoustic emission sensors

Construction and Building Materials 229 (2019) 116858

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Monitoring early age strength gain of SCC with different supplementary cementitious materials using acoustic emission sensors Ahmed A. Abouhussien a,⇑, Assem A.A. Hassan b a b

Department of Civil & Mineral Engineering, University of Toronto, Toronto, Ontario M5S 1A4, Canada Department of Civil Engineering, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, Newfoundland and Labrador A1B 3X5, Canada

h i g h l i g h t s
AE analysis was utilized to monitor strength development at early age of SCC mixtures.
Different supplementary cementitious materials were incorporated in SCC development.
AE parameters including the b-value were related to the rate of strength gain.
A direct correlation between the rate of strength gain and AE parameters was obtained.

a r t i c l e

i n f o

Article history: Received 26 January 2019 Received in revised form 5 August 2019 Accepted 1 September 2019

Keywords: Nondestructive testing Acoustic emission Self-consolidating concrete b-value analysis Supplementary cementitious materials Strength gain

a b s t r a c t This investigation employed acoustic emission (AE) technique to monitor the strength development at an early age of self-consolidating concrete (SCC) mixtures with different supplementary cementitious materials (SCM’s). Four types of SCM’s including fly ash (FA), metakaolin (MK), silica fume (SF) and slag (SG) were investigated in this study. A normal concrete mixture with higher coarse-to-fine aggregate ratio was also investigated for the comparison. Three prisms from each tested mixture were continuously monitored by means of attached AE sensors up to a total period of 3 days. Cylindrical samples were tested to evaluate the compressive strength gain along the tested period. The emissions resulting from the changes in the microstructure of the specimens were recorded along with the temperature variations (using thermocouples) for all mixtures. Different AE parameters were analyzed including the signal amplitude, absolute energy, peak frequency, number of AE hits and cumulative signal strength (CSS) to relate them to the rate of strength gain. Furthermore, b-value analysis was conducted on the collected AE signals to evaluate the strength development by means of the variations in the b-value. The results of the AE parameters including the number of AE hits and CSS showed a direct correlation between the rate of strength gain and these studied AE parameters. Meanwhile, the b-value analysis was found to be a useful tool for capturing the changes in the internal properties of the different tested mixtures at early ages. Ó 2019 Elsevier Ltd. All rights reserved.

1. Introduction The strength gain is a significant property of concrete at its early age, since it has a direct impact on the strength and long term performance of concrete in structures. The rate of strength development of concrete is influenced by various factors such as the mixture proportions and curing conditions. It has also been reported that various supplementary cementitious materials (SCM’s) including fly ash, metakaolin, silica fume and slag have different effects on the strength development of concrete at an early ⇑ Corresponding author. E-mail address: [email protected] (A.A. Abouhussien). https://doi.org/10.1016/j.conbuildmat.2019.116858 0950-0618/Ó 2019 Elsevier Ltd. All rights reserved.

age [1]. Besides, the recent advances in concrete technology allow the development of new types of concretes which mandate further investigation on their strength development. As a result, numerous research studies have been implemented in the literature to develop nondestructive testing (NDT) methods to evaluate the cement hydration process and strength gain of concrete with various types. The most commonly used NDT techniques for monitoring early age properties of concrete are based on ultrasonic testing [2–5]. For example, embedded piezoelectric transducers acting as smart aggregates were used to monitor the early age strength of concrete [6]. Zhu et al. [7] used similar embedded piezoceramic bender elements to measure ultrasonic shear waves for monitoring concrete

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setting and strength development. Chen et al. [8] utilized an ultrasound P-wave propagation test to evaluate the changes in microstructure of hydrating Portland cement paste at early ages. Another technique using electro-mechanical impedance coupled with artificial neural networks was employed to estimate concrete strength [9]. Meanwhile, the ultrasonic guided waves method was also successfully used to assess the mechanical properties and hardening process of concrete [10]. However, all the previously mentioned methods are active excitation NDT methods, which require an external source to generate ultrasound waves making it not practical for some specific applications [11]. Acoustic emission (AE) is a passive NDT technique that proved its effectiveness for damage detection and evaluation of concrete materials/structures. Using this method, AE sensors can identify any changes in the microstructure and/or the occurrence of micro-damage when attached or embedded in concrete [12–14]. AE monitoring has successfully been employed by a number of applications to detect a variety of types of damage, such as cracking of reinforced concrete beams [15–16]; cracks and initial yielding of CFRP-confined circular concrete-filled steel tubular columns [17]; corrosion of prestressing strands [11,18–19] and alkali-silica reaction in concrete [20]. On the other hand, limited studies have investigated the use of AE monitoring of early age properties of concrete. Van Den Abeele et al. [21] compared both active and passive monitoring techniques based on ultrasonic wave spectroscopy and AE technique for early hydration process in concrete. Their results indicated a good correlation between the acoustic parameters and the phase changes due to chemical reactions and mechanical setting of concrete. Similarly, Lura et al. [22] utilized the AE measurements from hydrating cement pastes to identify the fluid to solid transition time of different cement pastes. AE embedded sensors were also employed to assess the behaviour of early age concrete in terms of temperature changes and cracking through the hydration process [23]. More recently, AE monitoring of fresh concrete with variable water and aggregate contents was conducted to relate between early age AE activity and the final mechanical properties of concrete [24]. The results from this preliminary study indicated that AE activities at an early age were well correlated with the final strength and stiffness of concrete [24]. This study characterized different mechanisms occurring during hydration and eventually related them to AE waveform parameters such as the rise time over the amplitude and average frequency [24]. Nevertheless, further research is needed to examine the sensitivity of AE parameters to various concrete types such as selfconsolidating concrete (SCC) and/or different SCM’s at their early age. The main goal of this experimental study was to investigate the feasibility of using the AE technique to capture the microstructural variations due to hardening and strength development of different SCC mixtures, in comparison with normal concrete. The effect of using low coarse-to-fine aggregate ratio and incorporating different SCM’s (fly ash, metakaolin, silica fume and slag) in SCC production on different AE signal parameters was also examined in this paper. Eventually, an extensive analysis of the acquired AE signals, including b-value analysis, was further performed so as to correlate between different AE parameters and the rate of strength gain (as a function of compressive strength tested at early ages).

unique ability of the AE technique to identify the type and/or extent of different damage mechanisms or any sort of microstructure change in concrete. For example, the use of the AE technique has been expanded to monitor the setting behavior of calcium aluminate cement [33]. The outcomes of this specific investigation showed that a significant increase of AE activity was found during the first 24 h after casting. Chotard et al. [33] proposed a theory that related the duration of the collected AE signals to the internal processes resulting from the cement hydration. This theory indicated that the onset of AE signals is attributable to the processes of removal of water from the capillary network due to water consumption (dissolution of cement grains), formation of hydrates and creation of porosity [33]. Furthermore, other researchers attributed the measured AE activities in the hydrating cement paste to the cavitation events occurring in the pores of the cement paste as a result of self-desiccation, thus creating gas-filled bubbles in the pores [22,24]. After the conclusion of the hydration process, the AE events can then be associated to the growth of the hydrated phases and development of micro-cracking due to the shrinkage of the paste. These processes lead to the liberation of energy that generates elastic waves during the drying period, thus producing additional AE activities [33]. Other studies also confirmed that the onset of mechanical setting and shrinkage of concrete during the hardening process is generally associated with the accumulation of AE events [21,23]. The results of these previous investigations showed that the analysis of the AE data was sensitive to the internal changes in the cement paste and concrete. Similar AE analyses in addition to the b-value analysis described below were employed in this study to assess the early age strength gain of SCC mixtures. 3. Research significance The AE technique has successfully been utilized in few studies for monitoring early age changes in normal concrete mixtures. This methodology has the advantage of continuous and non-intrusive real-time passive monitoring of the microstructural changes due to strength gain in concrete without any destructive measurements compared with other methods. However, there is a lack of research concerning the correlation between the rate of strength gain of SCC (especially when different SCM’s are incorporated in the mixture) and various AE parameters such as cumulative signal strength on a quantitative basis. This experimental study examines the effectiveness of using attached AE sensors to evaluate the strength development of SCC mixtures containing different SCM’s. In addition, this paper investigates the effectiveness of the wellestablished b-value analysis of the AE data for the assessment of the early age strength development of SCC. The b-value analysis is yet to be implemented on AE data attained from monitoring early age strength development. The outcomes of this study contribute to further applications of the AE method as a continuous NDT tool for concrete structures. Meanwhile, this investigation represents an effort towards using AE technique to nondestructively assess both the short- and long-term performance of concrete in structures. 4. Experimental program 4.1. Materials properties and mixtures design

2. Mechanisms of AE activities during the Hydration, setting and hardening of concrete The phenomenon of AE occurs due to the release of energy in the form of acoustic waves as a result of strain energy generated in materials/structures. Previous studies focused on exploiting this

Five SCC mixtures were developed and tested in this investigation. Four of those SCC mixtures contained four SCM’s (one SCM per mixture) including FA, MK, SF and SG. These four SCC mixtures involved the replacement of the weight of cement by 30% FA, 20% MK, 8% SF and 30% SG. Those selected replacement levels are the

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optimum dosage of each SCM based on a previous study conducted by the authors [25]. One of those five SCC mixtures was cast without using any SCM to act as a control SCC mixture. All SCC mixtures were designed with a total binder content of 500 kg/m3, water to binder (W/B) ratio of 0.4 and coarse to fine aggregates (C/F) ratio of 0.7. A high range water reducer admixture (HRWRA) was added to SCC mixtures to reach a target slump flow of 700 ± 50 mm, as per ASTM C1611 [26]. The HRWRA used in all SCC mixtures is a polycarboxylate-based admixture typically incorporated for the development of SCC owing to its effectiveness in improving the workability and slump retention. The required dosage of the HRWRA was determined based on testing trial mixtures prior to completing this study. For the comparison with the control SCC mixture (without SCM’s), an additional normal concrete (NC) mixture was also included in this investigation. This NC mixture contained a total binder content of 500 kg/m3, W/B ratio of 0.4 and C/F ratio of 2.0. The differences between the NC and SCC were the C/F ratio and the utilization of HRWRA in SCC to achieve the target slump flow (since NC did not contain any HRWRA). These two mixtures were used to assess the effect of changing concrete type (NC versus SCC) on the strength development as well as the resulting AE parameters. All six mixtures contained type GU Canadian Portland cement conforming to ASTM Type I [27], with a specific gravity of 3.15. The fine and coarse aggregates used in all mixtures were natural sand and 10 mm maximum aggregate size stone, respectively. The fine and coarse aggregates both have a specific gravity of 2.60 and water absorption of 1%. The HRWRA used in SCC mixtures is similar to ASTM Type F [28] with a specific gravity, volatile weight and pH of 1.2, 62% and 9.5, respectively. Also, the specific gravities of FA, MK, SF and SG are 2.38, 2.5, 2.27 and 2.9, respectively. Table 1 presents the mixture proportions and the dosage of HRWRA of the NC and SCC mixtures tested in this study.

Fig. 1. Experimental setup.

The sensors used in this study were resonant frequency piezoelectric AE sensors with an integral preamplifier (R6I-AST) with an operating frequency range of 40–100 kHz [30]. These sensors were selected for this study owing to their high sensitivity and low resonant frequency making them suitable for various applications; for example, metal, FRP, and concrete structures [31]. The acquisition of AE during these tests was performed by means of an AE data acquisition system (PCI-2 based AE system) and AEwin signal processing software provided by Mistras Group [31]. An amplitude threshold of 40 dB was used in order to collect the emitted AE signals during the tests. Other parameters used in setting up the AE hardware are summarized in Table 2. In addition, the hit definition parameters (peak definition time, hit definition time, hit lockout time and maximum duration) were chosen based on the recommended values by the manufacturer [31] and previous studies performed by the authors [45–47]. The AE data acquisition system was setup to acquire the following AE signal parameters: amplitude, energy, duration, signal strength, absolute energy, rise time, counts, average frequency and peak frequency. The explanations of these aforesaid AE parameters along with other AE terminology of nondestructive testing and evaluation can be found elsewhere [32]. The whole monitoring process was conducted in a laboratory environment (air curing) with a controlled temperature. The companion cylinders were also air cured in the laboratory and tested at

4.2. Details of the tested samples and AE monitoring setup Prism specimens (100  100  400 mm) were selected for this study to be monitored using AE sensors during the first 3 days. Three prisms were cast from each mixture along with 9 cylindrical samples (100 mm diameter  200 mm high) for testing the compressive strength at 1, 2 and 3 days, according to ASTM C39 [29]. After mixing, concrete was cast in the formwork shown in Fig. 1. The sensors were then attached to the prisms surface right after concrete casting. A two-part epoxy adhesive and Kapton tape were both used to attach all sensors at the center of prism, as seen in Fig. 1. More specifically, a layer of the Kapton tape was placed between the sensor and the surface of fresh concrete to facilitate the application of the epoxy adhesive on the specimens. The samples were continuously monitored throughout the first 3 days while being cured in air in the laboratory. The 3 days monitoring period was selected based on a trial testing lasted for more than 7 days, which indicated no significant AE activity recorded after 3 days [23]. Therefore, all samples tested in this study were monitored up to 3 days on a comparative basis.

Table 2 AE data acquisition system setup. AE hardware setup Threshold Sample rate Pre-trigger Length Preamp gain Preamp voltage Peak definition time Hit definition time Hit lockout time Maximum duration

40 dBAE 1 MSPS 256 ls 1 k points 40 dB 28 200 ls 800 ls 1000 ls 1000 ls

Table 1 Mixture proportions of all tested mixtures. Mixture Type

Cement

SCM (%)

SCM (kg/m3)

C/F

W/B

C.A. (kg/m3)

F.A. (kg/m3)

Water (kg/m3)

HRWRA (l/m3)

NC SCC FA MK SF SG

500 500 350 400 460 350

0 0 30% FA 20% MK 8% SF 30% SG

0 0 150 100 40 150

2.0 0.7 0.7 0.7 0.7 0.7

0.4 0.4 0.4 0.4 0.4 0.4

1111.5 686.5 670.0 677.7 681.3 682.1

555.8 980.8 957.2 968.1 973.2 974.5

200 200 200 200 200 200

0 2.37 2.08 5.42 4.17 2.25

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1, 2 and 3 days to measure their compressive strength gain, as per ASTM C39 standard test method [29]. Also, a thermocouple temperature data logger (Fig. 1) was used to monitor the internal changes in the heat of hydration of the prism samples throughout the testing period. The thermocouples were embedded in all concrete prisms right after casting and connected to the data logger using electric wires. The temperature data was recorded starting at the same time of beginning the AE acquisition for all tested prisms during the first 3 days right after the concrete casting.

5. Results and discussions The AE data obtained from monitoring the prism samples of all mixtures are analyzed in the following sections in conjunction with the results of the compressive strength of the companion cylinders and heat of hydration of the prisms. 5.1. AE data filtering Prior to analysis, the raw AE data recorded from all samples were filtered using an amplitude-duration-based filter (Swansong II filter) to minimize any noise-related signals and/or irrelevant wave reflections within the boundaries of each prism sample [34]. It should be noted that, the size of the specimens utilized in this study is not too small, thus the risk of the generation of noise due to wave reflections is not significant. This filter has successfully been adopted in several experimental investigations dealing with AE monitoring of concrete structures [11,20,35–36]. The idea of this filter is that real AE signals with high amplitudes are mostly associated with long durations, and vice versa [20]. The range of the rejection limits of this filter were then determined following the visual inspection of the AE signals, as described in Table 3. The remaining signals after applying this filter are then considered to be real AE signals resulting from the internal changes of all tested prisms. The adopted filtering process resulted in a percentage of filtered signals within the range of nearly 15–25% of the raw signals obtained from all tested mixtures. After filtering, the AE data were analyzed and discussed in the following sections. 5.2. b-value analysis In addition to the recorded AE parameters throughout the tests, an additional analysis on the amplitude/number of hits defined as b-value analysis was performed to generate an additional parameter. This analysis uses seismic magnitude-frequency equations and has extensively been utilized to evaluate the development of cracking in concrete structures [11,17,37–39]. The b-value parameter reflects the frequency-magnitude distribution of AE activities for the purpose of evaluating the level of damage. To date, this analysis was not adopted in evaluating the early age properties of concrete such as the strength gain. The conventional b-value was calculated for all tested prisms throughout the monitoring period by Eq. (1) [11,17,37–39]. The same conventional b-value analysis on all the

AE events throughout the tests was performed to be able to compare the b-value among different mixtures.

log N ¼ a  b log A

ð1Þ

where: N = the number of hits having amplitudes larger than A of all events throughout the test; A = the signal amplitude (dB); a = an empirically derived constant; and b = the b-value [11,17,37–39]. The b-value was calculated using Eq. (1) based on the slope between log N and log A measured continuously starting from the beginning of the test (taking into account all previous AE events). First, the values of log N were graphed against log A for the entire monitoring period to determine the value of the constant ‘‘a”. The one value of the constant ‘‘a” used for the entire monitoring period was selected owing to the relatively small range of the values of the number of hits in this study. Then, the b-value corresponding to each hit from the beginning of the monitoring period was continuously attained by substituting the magnitudes of the constant ‘‘a” as well as log N and log A for each time step in Eq. (1). This procedure was applied to calculate the variations in the b-value of all tested mixtures during the tests to assess the effects of using variable concrete and SCM’s types on the rate of strength development. Further analysis such as the improved b-value (Ibvalue) approach is then recommended for future research for the comparison with the outcomes of this investigation. 5.3. Effect of strength gain on AE parameters Table 4 and Fig. 2 present the compressive strength results of all tested mixtures at the ages of 1, 2 and 3 days. Meanwhile, the filtered AE data was analyzed in order to be correlated to the development of strength of all tested mixtures. For example, the number of cumulative AE hits recorded throughout the test using the three sensors of the SG mixture is shown in Fig. 3, as a representative of all tested mixtures. This figure also compares the number of AE hits to the values of average compressive strength at 1, 2 and 3 days of the SG mixture. It can be observed from this figure that, the number of hits generally increased as a result of the strength development till 3 days. The increase in the AE activity during this period may be attributed to the processes of water consumption, development of hydrates, cavitation and porosity and lastly micro-crack initiation as a result of drying shrinkage. These aforementioned processes are the typical stages of the compressive strength gain of concrete and have been found to generate elastic waves leading to increased AE activities [21–24,33]. In particular, the water consumption due to the chemical reaction between cement and water during the hydration process leads to a progressive emptying of the capillary network. As a result, a release of energy during this process induces a generation of an elastic wave in the form of AE event. The development of hydrates also generates a release of energy due to the precipitation of small germs of hydrates as well as the growth of hydrated phases [33]. Similarly, the results of cumulative signal strength (CSS) of the three prism samples of the SG mixture are demonstrated in Fig. 4, in conjunction with the average compressive strength data at 1, 2 and 3 days curve. The variations in CSS were found to be similar to

Table 3 Rejection limits for AE amplitude-duration filter. Amplitude range (dB)

40  A < 45 45  A < 48 48  A < 52 52  A < 56 56  A < 60

Duration (ls) Lower

Upper

0 0 0 0 100

400 500 600 700 800

Amplitude range (dB)

Duration (ls) Lower

Upper

60  A < 65 65  A < 70 70  A < 80 80  A < 90 90  A < 100

300 500 1000 2000 3000

1000 2000 4000 7000 10,000

5

A.A. Abouhussien, A.A.A. Hassan / Construction and Building Materials 229 (2019) 116858 Table 4 Compressive strength development of all mixtures. 1-day compressive strength (MPa)

NC SCC FA MK SF SG

2-day compressive strength (MPa)

1

2

3

Average

1

2

3

Average

1

2

3

Average

18.6 24.6 15.8 23.6 25.0 14.7

21.6 24.5 15.8 22.8 22.2 13.7

19.8 23.4 15.8 25.1 25.3 14.7

20.0 24.2 15.8 23.8 24.2 14.3

24.9 27.2 23.8 35.2 30.2 25.7

25.1 29.0 23.2 34.7 31.1 26.0

25.1 28.7 23.2 35.6 30.9 26.2

25.1 28.3 23.4 35.1 30.7 26.0

29.3 31.5 31.0 42.7 40.5 32.5

33.2 33.8 30.0 41.7 37.9 32.7

32.6 34.7 30.3 42.4 39.4 33.3

31.7 33.3 30.4 42.2 39.3 32.9

45 40

1.4

35

1.2

30

1

25

0.8

20

0.6

15

0.4

10

0.2

5

35 30 25

1-day 2-day

20

3-day 15

CSS x 106 (pV.s)

Average compressive strength (MPa)

3-day compressive strength (MPa)

10

Average compressive strength (MPa)

Mixture

5

0 0

0 0

NC

SCC

FA

MK

SF

20

SG

40

60

80

Time (h)

Fig. 2. Average compressive strength development of all mixtures (the vertical error bars represent the values of standard deviation in MPa).

Sample 1

Sample 2

Sample 3

Average compressive strength

140

35

120

30

100

25

80

20

60

15

40

10

20

5

0

Average compressive strength (MPa)

Number of cumulative hits

Fig. 4. CSS and average compressive strength versus time of the SG samples.

0 0

10

20

30

40

50

60

70

80

cumulative hits and CSS attained from the three samples from each mixture are shown in Fig. 5a and b, respectively. It should also be mentioned that, the analysis of the individual AE signal waveform parameters (such as signal amplitude, absolute energy and peak frequency) did not seem to capture the changes in the concrete microstructure owing to the strength gain. Therefore, the analysis in this section was performed using the AE number of hits and CSS only. As previously noted, all samples were air cured in the laboratory under constant humidity and temperature throughout all tests. As a result, further experiments on additional samples are recommended in order to study the influence of varying curing conditions on the resulting AE data.

5.4. Correlation between AE parameters and temperature variations

Time (h) Sample 1

Sample 2

Sample 3

Average compressive strength

Fig. 3. AE number of cumulative hits and average compressive strength versus time of the SG samples.

those of the number of cumulative AE hits. This increasing trend of CSS further confirmed the correlation between the recorded AE activities and the compressive strength gain of this tested specimen. It can also be observed from Figs. 3–4 that, the CSS curves exhibited some locations with more visible sudden changes than those of the number of hits curves. This observation may be related to the variations of the values of signal strength generated throughout the monitoring period. Tables 5 and 6 summarize the previously mentioned AE parameters for all other tested samples (NC, SCC, FA, MK and SF mixtures) at the ages of 1 and 3 days, respectively. In addition, the average values of the number of

Fig. 6 shows the internal temperature variations throughout the test (for the same three samples (SG mixture) described in Figs. 3 and 4) and compares the AE number of cumulative hits. The figure shows an increase in the heat of hydration until reaching a maximum values between 10 and 15 h from the beginning of the monitoring process. After this time, the temperature started to drop down then exhibited non-significant changes until the end of the test. The point of the maximum values of the heat of hydration was shortly followed by the occurrence of a sudden increase in the AE number of hits and CSS indicated by a noticeable slope change in Figs. 3 and 4 at nearly 15–25 h. However, this increased AE activity can only be correlated to the locations of maximum values of heat of hydration which occurred during the first 24 h from the beginning of the test. After 24 h, the heat of hydration did not show any clear correlation with the increasing trend in the AE number of hits and CSS. It should be noted that, the strength gain continues after reaching the temperature peak. The increase in the

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Table 5 AE number of hits and CSS of all samples at 1-day. Mixture

CSS (pV.s)  105

Number of hits

NC SCC FA MK SF SG

Sensor 1

Sensor 2

Sensor 3

Sensor 1

Sensor 2

Sensor 3

44 61 55 79 75 41

60 65 50 75 77 39

66 69 53 73 83 49

4.98 4.05 5.21 2.56 3.11 4.03

4.01 4.31 4.07 4.85 4.55 3.38

3.83 4.77 3.01 4.63 5.04 4.49

Table 6 AE number of hits and CSS of all samples at 3-days. CSS (pV.s)  105

Number of hits

NC SCC FA MK SF SG

Sensor 1

Sensor 2

Sensor 3

Sensor 1

Sensor 2

Sensor 3

110 116 103 145 135 114

99 105 98 157 129 103

102 108 85 140 131 122

9.95 10.51 9.69 13.43 12.50 10.52

9.56 9.87 9.06 14.08 12.23 9.76

9.77 10.88 8.07 13.21 12.09 11.68

Average number of cumulative hits

120

140

30

120

25

100

20

80

15

60

10

40

5

20

100 80 60

1-day 3-day

40 20

0

0 0

0 NC

SCC

FA

MK

SF

10

20

SG

a)

Sample 1 AE Sample 1

12

30

40 Time (h)

50

Sample 2 AE Sample 2

60

70

80

Sample 3 AE Sample 3

Fig. 6. Temperature variations of the SG samples compared with the AE number of cumulative hits on the secondary axis.

10

Average CSS (pV.s) x 105

35

AE number of cumulative hits

Mixture

8 6

1-day

the studied AE parameters (number of hits and CSS) exhibited better correlation to the compressive strength gain than the changes in heat of hydration.

3-day 4

5.5. Effect of concrete type on AE parameters

2

Five SCC mixtures were tested and compared to a NC mixture, as previously mentioned. Changing the concrete mixture type yielded a significant impact on the values of the cumulative AE number of hits and CSS, as can be seen from Figs. 7 and 8. This impact is attributed to the different rate and magnitude of strength development owing to the change in mixture type (NC versus SCC) and SCM’s types incorporated in SCC mixtures. As seen in Figs. 7 and 8, SCC exhibited higher values of AE number of hits and CSS than NC throughout the test. These results can be attributed to the higher rate of strength gain of SCC compared to NC (Table 4 and Fig. 2). It should be noted that, both mixtures followed similar increasing trend in the number of hits and CSS. Yet, the SCC mixture showed higher values of these AE parameters than those of the NC mixture at the end of the monitoring period

0 NC

SCC

FA

MK

SF

SG

b) Fig. 5. Average AE results at 1 and 3 days: a) number of cumulative hits and b) CSS.

AE activities past this point can be attributed to the water consumption, cavitation in the capillary pores, formation of cement hydrates, hardening and eventually the initiation of microcracking due to the drying shrinkage of concrete. These results matched the outcomes from similar previous research studies available in the literature [24,33,40]. This observation suggests that

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160

50

Amplitude (dB)

Number of cumulative hits

140 120 100

30

NC SCC

20 10

80

0

60

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76

AE hits

40

Fig. 9. Comparison between NC and SCC signal amplitudes.

20 0 0

10

20

30

40

50

60

70

80

Time (h) NC

SCC

FA

MK

SF

SG

Fig. 7. Variations in the number of cumulative hits versus time of all tested mixtures from Sensor 1.

1.6 1.4 1.2

CSS x 106 (pV.s)

40

1 0.8 0.6 0.4 0.2 0 0

10

20

30

40

50

60

70

80

Time (h) NC

SCC

FA

MK

SF

SG

Fig. 8. Variations in the CSS versus time of all tested mixtures from Sensor 1.

(Figs. 7 and 8). These figures also reflected the effect of strength gain on the increased values of number of cumulative hits and CSS in all tested mixtures throughout the tests. In other words, all mixtures exhibited an overall increase in the values of the number of hits and CSS (Figs. 7 and 8) which matched the increase in the average compressive strength throughout the monitoring period (Fig. 2). However, the CSS curves (Fig. 8) followed slightly different trends of variations than those of the number of cumulative hits (Fig. 7) in some mixtures. These slightly different trends can be related to the dependence of the CSS on the characteristics of each signal (in terms of signal strength) compared with the number of cumulative hits which just represent the number of the released waves. The variation of concrete type did not have any significant effect on the AE signal characteristics. For instance, the amplitudes of the signals obtained from NC and SCC mixtures are compared in Fig. 9. This figure shows that no significant variations were found between NC and SCC in terms of AE signal amplitude. These similar signal amplitudes can be related to the similar AE sources resulting from the process of strength gain and wave propagation characteristics in these mixtures. It should be mentioned that, the use of larger C/F ratio in the NC mixture compared to SCC counterpart (2.0 versus 0.7) was expected to contribute to higher wave attenuation and consequently lower amplitudes. This was expected due to the

anticipated change in the homogeneity of concrete containing high C/F ratio which contributes to wave dispersion and scattering [41– 42]. However, this effect was not pronounced by comparing the signal amplitudes obtained from these two concrete types. These results can be attributed to the constant size of coarse aggregate (10 mm) used in this investigation. Similar to the effect of concrete types on signal amplitude, changing concrete types did not affect other AE signal characteristics resulting from the strength development including absolute energy and peak frequency. Fig. 10 shows the average results of these previously defined AE signal parameters (signal amplitude, absolute energy and peak frequency) attained from the three sensors for all tested prisms at 1 and 3 days. Meanwhile, the results for these parameters attained from three samples at 1 and 3 days of all mixtures are shown in Tables 7–8. As seen from Fig. 10, the average values of absolute energy and peak frequency for NC were 1.35  102 aJ and 60 kHz compared to 1.34  102 aJ and 55 kHz for SCC at 3 days, indicating non-significant differences. Similarly, the results of these AE signal parameters did not show any noticeable changes among the SCC mixtures with or without SCM’s both at 1 and 3 days (Fig. 10). It can be observed from Fig. 10b that, the values of the average absolute energies of all specimens at 1 day were higher than those at 3 days. This trend suggested that the signal durations at 1 day were longer than those at 3 days leading to reduced values of average absolute energies at 3 days, since the average signal amplitudes were similar at 1 and 3 days (Fig. 10a). This observation was further confirmed by the visual inspection of the durations of the AE signals attained from all samples at one and three days. This difference in the AE signal signatures in terms of signal duration could be attributable to the different wave propagation characteristics resulted from the changes in concrete microstructure from one to three days [24,40]. More specifically, the process of strength development include the generation of different AE events due to the release of energy resulting from water consumption, development of hydrates, cavitation and porosity and micro-cracking. These different AE events are expected to show variable waveforms (including variable durations) at the different intervals during the monitoring period. This finding matched the outcomes of the study conducted by Chotard et al. [33] which classified the previously mentioned processes based on the signal characteristics of the collected AE events. However, this finding cannot be generalized considering the limited number of the tested specimens in this investigation. 5.6. Effect of SCM’s on AE parameters As previously noted, varying concrete type (NC to SCC) was found to have a non-significant influence on the AE signal characteristics (signal amplitude, absolute energy and peak frequency) throughout the test. This was also the case for using variable SCM’s in the SCC mixtures including FA, MK, SF and SG. The incorporation of these SCM’s did not show a significant influence of the signal

8

A.A. Abouhussien, A.A.A. Hassan / Construction and Building Materials 229 (2019) 116858

50 45 40

Amplitude (dB)

35 30 25

1-day

20

3-day

15 10 5 0 NC

SCC

FA

MK

SF

SG

a)

Absolute energy x 102 (aJ)

2.5

2.0

1.5 1-day 1.0

3-day

0.5

0.0 NC

SCC

FA

MK

SF

SG

b) 70

Peak frequency (kHz)

60 50 40 30

1-day

5.7. Evaluation of strength development using b-value analysis

3-day

The analysis of AE parameters including number of hits and CSS in the previous sections confirmed the sensitivity of AE parameters to the changes of the mixtures microstructure due to the hardening process and strength gain. The b-value analysis was completed on the AE signals to better describe these changes and distinguish the effects of concrete type and using SCM’s on the strength development. It should be mentioned that, the b-value analysis is a complementary approach to the analysis of the AE number of hits and CSS discussed in the previous sections. This analysis was conducted, as explained in Section 5.2, to calculate the b-value which represents the distribution of frequency and magnitude of the

20 10 0 NC

SCC

FA

MK

SF

amplitude, absolute energy and peak frequency as can be observed from Fig. 10 at 1 and/or 3 days. On the contrary, these SCM’s yielded a significant effect on the rate of strength gain of SCC mixtures (Table 4 and Fig. 2), thereby resulting in varying AE number of hits and CSS as can be seen from Tables 5 and 6 as well as Figs. 7 and 8. These tables and figures once more manifest the effect of strength gain on the AE number of hits and CSS. In particular, the variations of the rate of strength gain (in terms of average compressive strength) among the tested mixtures were in a good correlation with the values of AE number of hits and CSS. All tested samples followed similar trend of variation as that of SG sample, which showed that the values of AE number of hits and CSS generally increased from 1 day to 3 days due to the increase in compressive strength (shown in Figs. 2, 7 and 8). This increase in AE activities is related to the microstructural changes in the concrete mixture resulting from the process of strength development. This trend was observed from all tested mixtures including NC and SCC with or without SCM’s. This finding confirms the effectiveness of using the AE passive monitoring technique for monitoring early age strength gain of SCC mixtures regardless of the SCM’s type. The graphs in Figs. 7 and 8 also manifest the significant effect of using different SCM’s on the magnitudes of AE number of hits and CSS at the same age throughout the testing period. For instance, it can be noticed from Figs. 7 and 8 that the MK and SF mixtures showed higher strength development and higher values of the number of hits and CSS than the control SCC mixture followed by SG and FA mixtures, respectively, throughout the test. The figures also show that, MK and SF mixtures were similar in terms of AE number of hits and CSS and both had higher AE activities than the control mixture (SCC with no SCM’s). This observation may be attributable to the very similar compressive strength attained from MK and SF mixtures at 1 day (Table 4 and Fig. 2). These results also highlight the superior effect of using both MK and SF on enhancing the early age strength gain, as previously noticed from Table 4 and Fig. 2. This improvement can be related to the higher pozzolanic reactivity of MK and SF compared to other SCM’s which contributes to producing more cementitious products and eventually high compressive strength [43–44].

SG

c) Fig. 10. Average AE signal characteristics of all mixtures: a) Amplitude, b) Absolute energy and c) Peak frequency.

Table 7 Average AE signal parameters of all samples at 1-day. Mixture

NC SCC FA MK SF SG

Absolute energy  102 (aJ)

Amplitude (dB)

Peak frequency (kHz)

Sensor 1

Sensor 2

Sensor 3

AV

SD

Sensor 1

Sensor 2

Sensor 3

AV

SD

Sensor 1

Sensor 2

Sensor 3

AV

SD

45 45 43 44 45 46

43 44 44 46 45 45

41 44 44 42 44 46

43 44 44 44 45 46

2.0 0.6 0.6 2.0 0.6 0.6

2.61 1.36 2.55 2.90 4.00 1.05

1.53 3.57 2.20 0.67 2.23 1.47

2.17 0.82 1.71 2.95 0.69 3.31

2.10 1.92 2.15 2.17 2.31 1.94

0.5 1.5 0.4 1.3 1.7 1.2

59 56 50 59 68 52

59 52 49 67 49 51

46 59 53 42 52 55

55 56 51 56 56 53

7.5 3.5 2.1 12.8 10.2 2.1

Note: AV = average value of Sensor 1, 2 and 3 and SD = standard deviation.

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A.A. Abouhussien, A.A.A. Hassan / Construction and Building Materials 229 (2019) 116858 Table 8 Average AE signal parameters of all samples at 3-days. Mixture

NC SCC FA MK SF SG

Absolute energy  102 (aJ)

Amplitude (dB)

Peak frequency (kHz)

Sensor 1

Sensor 2

Sensor 3

AV

SD

Sensor 1

Sensor 2

Sensor 3

AV

SD

Sensor 1

Sensor 2

Sensor 3

AV

SD

47 45 45 44 47 46

46 43 44 44 46 45

43 44 44 47 45 46

45 44 44 45 46 46

2.1 1.0 0.6 1.7 1.0 0.6

2.80 0.86 0.82 0.96 0.98 1.33

0.65 2.51 3.25 1.38 0.54 0.95

0.61 0.67 2.00 0.71 0.45 2.63

1.35 1.35 2.02 1.02 0.66 1.64

1.3 1.0 1.2 0.3 0.3 0.9

68 49 53 58 66 54

60 58 50 69 54 55

53 57 52 41 45 58

60 55 52 56 52 56

7.5 4.9 1.5 14.1 10.5 2.1

Note: AV = average value of Sensor 1, 2 and 3 and SD = standard deviation.

1.8 1.6 1.4

b-value

1.2 1 0.8 0.6 0.4 0.2 0 0

10

20

30

40

50

60

70

80

Time (h) Fig. 11. Variations of b-value of the SG mixture from Sensor 3.

recorded AE events. The typical variations of the b-values of the SG mixture attained from Sensor 3 are presented in Fig. 11, as an example of the other tested mixtures. It can be noticed from the figure that, the b-values witnessed an overall reduction trend throughout the test. It has been reported in the literature that the decrease in the b-values represents an increased AE activity, owing to the higher number of hits having high amplitudes [11,17,37–39]. Similarly, the decline in the b-values in this study can be related to the higher AE events resulting from the hardening process and strength development of all tested mixtures. In addition, it is clear from Fig. 11 that the b-value curve contains some areas of higher fluctuations than other locations. For example, the SG mixture shown in Fig. 11 exhibited noticeable changes in the b-value curve at approximately 15 to 50 h. These periods of high variations in the b-value are mostly related to the increased AE signals from increased changes in the microstructure of the mixture. These changes of the microstructure can be related to water consumption, cavitation during solidification, formation of hydrates/porosity and micro-cracks resulting from drying shrinkage which led to the release of AE signals [20–22,33,40]. Eventually, the b-value reaches its approximately minimum magnitude at nearly the end of the test, which was noticed in all tested specimens.

The magnitudes of the b-value of all tested specimens corresponding to the signals recorded at the ages of 1 and 3 days are presented in Table 9. The magnitudes of the b-value shown in Table 9 are based on the minimum value calculated during the periods of 1 and 3 days for all mixtures. It can be observed from the table that, the b-values were generally lower at 3 days than those at 1-day, regardless of mixture type. These results indicate the sensitivity of the b-values to the compressive strength gain of all mixtures (Table 4 and Fig. 2). Furthermore, the changes in concrete type and SCM type showed similar impacts on the bvalues to those observed from the number of AE hits and CSS, as explained in the preceding section. The higher the b-value obtained from each mixture, the lower the AE events recorded from this mixture, and vice versa. It is worth noting that, the bvalue analysis was found to be more sensitive than analyzing the AE number of hits and CSS, since it enabled a clearer identification of the locations of high AE activities during the tests. In other words, the zones of significant variations in the b-value curve (Fig. 11) were more noticeable than locations of slope changes in the AE number of hits and CSS curves (Figs. 3 and 4). The outcomes of the b-value analysis of the AE data obtained from the experimental tests in this study revealed its effectiveness in evaluating the rate of strength development of different concrete and SCM types. Nevertheless, this finding is only based on the results from the current investigation on small-scale specimens under laboratory controlled environment. Additional verifications on actual concrete structures are then due prior to suggesting the field application of AE analysis for monitoring strength gain in-situ. Therefore, this work would be treated as a proof-ofconcept study that requires further research.

6. Conclusions Passive AE monitoring technique was employed to evaluate the strength gain of SCC mixtures at early age. Five SCC mixtures containing four variable SCM’s and one NC mixture were tested in this investigation. Prism samples made with those varied concrete mixtures were constantly monitored using attached AE sensors throughout the first 3 days. The analysis of the AE data acquired

Table 9 Results of b-value at 1 and 3 days for all samples. Mixture

NC SCC FA MK SF SG

b-value at 1-day

b-value at 3-days

Sensor 1

Sensor 2

Sensor 3

Sensor 1

Sensor 2

Sensor 3

0.62 0.55 0.61 0.32 0.31 0.76

0.59 0.51 0.69 0.32 0.33 0.8

0.58 0.47 0.64 0.35 0.3 0.59

0.47 0.41 0.48 0.27 0.28 0.47

0.51 0.45 0.51 0.26 0.29 0.51

0.48 0.43 0.55 0.28 0.33 0.36

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A.A. Abouhussien, A.A.A. Hassan / Construction and Building Materials 229 (2019) 116858

using these sensors were completed in comparison with the results of temperature changes and compressive strength development. This analysis led to the following conclusions:
The use of passive AE monitoring method presented in this paper was found to be sensitive to the strength development of all tested NC and SCC mixtures. The internal changes in the microstructure resulting from the compressive strength gain up to 3 days were found to be associated with an overall increase in the number of AE hits and CSS, regardless of the concrete and/or SCM types. These AE parameters were also found to be correlated to changes of the internal heat of hydration of all mixtures, but only during the first 24 h.
By comparing NC to SCC counterpart; it was found that the SCC mixture exhibited higher number of AE hits and CSS than those attained from NC counterpart. However, no significant variations were observed in the AE signal parameters including signal amplitude, absolute energy and peak frequency between NC and SCC counterpart mixture. Similarly, these AE signal characteristics showed non-significant changes at various ages in all mixtures.
The use of various SCM’s in SCC mixtures yielded significant changes on their strength development at early ages as well as AE parameters including AE number of hits and CSS. MK and SF mixtures exhibited higher strength gain and higher AE activities (number of hits and CSS) than the control SCC mixture (without SCM’s) followed by SG and FA mixtures, respectively. In contrast, the incorporation of these SCM’s in SCC mixtures did not seem to have a significant impact on the AE signal characteristics (signal amplitude, absolute energy and peak frequency), regardless of concrete age.
The variations in the b-value throughout the tests followed a declining trend in general, which were in a good correlation with the development of compressive strength of the tested samples. Furthermore, the high fluctuations in the b-values were associated with increased AE activities, which matched the occurrence of significant strength gain. The b-value analysis was also effective in capturing the varied rates of strength development of all mixtures including NC and SCC mixtures with and/or without SCM’s.
The results of the b-value analysis on the AE data obtained from continuous passive AE monitoring proved its potential for evaluating the changes in the early age properties of the SCC mixtures tested in this study. Additional tests on SCC mixtures with variable proportions are required in order to confirm the outcomes from this paper and to generalize this method for possible practical application for continuous NDT of concrete structures.

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References [1] X. Jin, Z. Li, Effects of mineral admixture on properties of young concrete, J. Mater. Civ. Eng. 15 (5) (2003) 435–442. [2] T. Chotard, N. Gimet-Breart, A. Smith, D. Fargeot, J.P. Bonnet, C. Gault, Application of ultrasonic testing to describe the hydration of calcium aluminate cement at the early age, Cem. Concr. Res. 30 (2001) 405–412. [3] K.V. Subramaniam, J.P. Mohsen, C.K. Shaw, S.P. Shah, Ultrasonic technique for monitoring concrete strength gain at early age, ACI Mater. J. 99 (5) (2002) 458– 462.

[4] Z. Sun, T. Voigt, S.P. Shah, Temperature effects on strength evaluation of cement-based materials with ultrasonic wave reflection technique, ACI Mater. J. 102 (4) (2005) 272–278. [5] Y. Lu, H. Ma, Z. Li, Ultrasonic monitoring of the early-age hydration of mineral admixtures incorporated concrete using cement-based piezoelectric composite sensors, J. Intell. Mater. Syst. Struct. 26 (3) (2015) 280–291. [6] H. Gu, G. Song, H. Dhonde, Y.L. Mo, S. Yan, Concrete early-age strength monitoring using embedded piezoelectric transducers, Smart Mater. Struct. 15 (2006) 1837–1845. [7] J. Zhu, Y.-T. Tsai, S.-H. Kee, Monitoring early age property of cement and concrete using piezoceramic bender elements, Smart Mater. Struct. 20 (11) (2011) 7. [8] W. Chen, Y. Li, P. Shen, Z. Shui, Microstructural development of hydrating Portland cement paste at early ages investigated with non-destructive methods and numerical simulation, J. Nondestruct. Eval. 32 (3) (2013) 228– 237. [9] T.-K. Oh, J. Kim, C. Lee, S. Park, Nondestructive concrete strength estimation based on electro-mechanical impedance with artificial neural network, J. Adv. Concr. Technol. 15 (2017) 94–102. [10] H. Sun, J. Zhu, Monitoring early age properties of cementitious material using ultrasonic guided waves in embedded rebar, J. Nondestruct. Eval. 36 (5) (2017) 1–12. [11] M.K. ElBatanouny, J. Mangual, P.H. Ziehl, F. Matta, Early corrosion detection in prestressed concrete girders using acoustic emission, J. Mater. Civ. Eng. 26 (2014) 504–511. [12] A.A. Pollock, Classical wave theory in practical AE testing. In Proceedings, Int. Acoustic Emission Symp, In: Japanese Society for Nondestructive Testing, 1986, pp. 708–721. [13] P.H. Ziehl, N. Galati, A. Nanni, J.G. Tumialan, In-situ evaluation of two concrete slab systems. II: evaluation criteria and outcomes, J. Perform. Constr. Fac. 22 (2008) 217–227. [14] A. Nair, C.S. Cai, Acoustic emission monitoring of bridges: review and case studies, Eng. Struct. 32 (6) (2010) 1704–1714. [15] M.A.A. Aldahdooh, N.M. Bunnori, Crack classification in reinforced concrete beams with varying thicknesses by mean of acoustic emission signal features, Constr. Build. Mater. 45 (2013) 282–288. [16] S. Shahidan, R. Pulin, N.M. Bunnori, K.M. Holford, Damage classification in reinforced concrete beam by acoustic emission signal analysis, Constr. Build. Mater. 45 (2013) 78–86. [17] D. Li, Z. Chen, Q. Feng, Y. Wang, Damage analysis of CFRP-confined circular concrete-filled steel tubular columns by acoustic emission techniques, Smart Mater. Struct. 24 (2015) 1–11. [18] J. Mangual, M. ElBatanouny, P. Ziehl, F. Matta, Acoustic-emission-based characterization of corrosion damage in cracked concrete with prestressing strand, ACI Mater. J. 110 (1) (2013) 89–98. [19] J. Mangual, M. ElBatanouny, P. Ziehl, F. Matta, Corrosion damage quantification of prestressing strands using acoustic emission, J. Mater. Civ. Eng. 25 (9) (2013) 1326–1334. [20] M. Abdelrahman, M.K. ElBatanouny, P. Ziehl, J. Fasl, C.J. Larosche, J. Fraczek, Classification of alkali-silica reaction damage using acoustic emission: A proofof-concept study, Constr. Build. Mater. 95 (2015) 406–413. [21] K. Van Den Abeele, W. Desadeleer, G. De Schutter, M. Wevers, Active and passive monitoring of the early hydration process in concrete using linear and nonlinear acoustics, Cem. Concr. Res. 39 (5) (2009) 426–432. [22] P. Lura, J. Couch, O.M. Jensen, J. Weiss, Early-age acoustic emission measurements in hydrating cement paste: evidence for cavitation during solidification due to self-desiccation, Cem. Concr. Res. 39 (2009) 861–867. [23] L. Qin, H.-W. Ren, B.-Q. Dong, F. Xing, Acoustic emission behavior of early age concrete monitored by embedded sensors, Mater 7 (2014) 6908–6918. [24] S.N. Iliopoulos, Y. El Khattabi, D.G. Aggelis, Towards the establishment of a continuous nondestructive monitoring technique for fresh concrete, J. Nondestruct. Eval. 35 (37) (2016) 1–11. [25] A.A. Abouhussien, A.A.A. Hassan, Optimizing the durability and service life of self-consolidating concrete containing metakaolin using statistical analysis, Constr. Build. Mater. 76 (2015) 297–306. [26] ASTM, Standard Test Method for Slump Flow of Self-Consolidating Concrete. ASTM C1611, ASTM International, West Conshohocken, PA,, 2009. [27] ASTM, Standard Specification for Portland Cement. ASTM C150, ASTM International, West Conshohocken, PA., 2012. [28] ASTM, Standard Specification for Chemical Admixtures for Concrete. ASTM C494, ASTM International, West Conshohocken, PA, 2013. [29] ASTM, Standard Test Method for Compressive Strength of Cylindrical Concrete Specimens. ASTM C39, ASTM International, West Conshohocken, PA, 2012. [30] Physical Acoustics, R6I-AST sensor. Princeton Junction, NJ, USA, 2005. [31] Mistras Group, PCI-2 Based AE System 1 user’s Manual, Physical Acoustics Corporation, Princeton Junction, NJ, USA, 2007. [32] ASTM, Standard Terminology for Nondestructive Examinations. ASTM E1316, ASTM International, West Conshohocken, PA, 2014. [33] T.J. Chotard, A. Smith, D. Rotureau, D. Fargeot, C. Gault, Acoustic emission characterization of calcium aluminate cement hydration at an early stage, J. Eur. Ceram. Soc. 23 (3) (2003) 387–398. [34] T. Fowler, J. Blessing, P. Conlisk, New directions in testing, Acoustic Emission Working Group, Memphis, TN, 1989, pp. 16–27.

A.A. Abouhussien, A.A.A. Hassan / Construction and Building Materials 229 (2019) 116858 [35] M. Abdelrahman, M.K. ElBatanouny, P.H. Ziehl, Acoustic emission based damage assessment method for prestressed concrete structures: modified index of damage, Eng. Struct. 60 (2014) 258–264. [36] W. Vélez, F. Matta, P. Ziehl, Acoustic emission monitoring of early corrosion in prestressed concrete piles, Struct. Control Heallth 22 (2015) 873–887. [37] I.S. Colombo, I.G. Main, M.C. Forde, Assessing damage of reinforced concrete beam using ‘‘b-value’’ analysis of acoustic emission signals, J. Mater. Civ. Eng. 15 (3) (2003) 280–286. [38] M. Ohtsu, Y. Tomoda, Phenomenological model of corrosion process in reinforced concrete identified by acoustic emission, ACI Mater. J. 105 (2) (2008) 194–199. [39] R.V. Sagar, B.K.R. Prasad, Laboratory investigations on cracking in reinforced concrete beams using on-line acoustic emission monitoring technique, J. Civ. Struct. Health Monit. 3 (2013) 169–186. [40] L. Assi, V. Soltangharaei, R. Anay, P. Ziehl, F. Matta, Unsupervised and supervised pattern recognition of acoustic emission signals during early hydration of Portland cement paste, Cem. Concr. Res. 103 (2018) 216–225. [41] T.P. Philippidis, D.G. Aggelis, Experimental study of wave dispersion and attenuation in concrete, Ultrasonics 43 (2006) 584–595.

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[42] S.N. Iliopoulos, D.G. Aggelis, D. Polyzos, Wave dispersion in fresh and hardened concrete through the prism of gradient elasticity, Int. J. Solids. Struct. 78–79 (2016) 149–159. [43] A.A.A. Hassan, K.M.A. Hossain, M. Lachemi, Effect of metakaolin and silica fume on rheology of self-consolidating concrete, ACI Mater. J. 109 (6) (2012) 657– 664. [44] A.A.A. Hassan, M. Lachemi, K.M.A. Hossain, Effect of metakaolin and silica fume on the durability of self-consolidating concrete, Cem. Concr. Compos. 34 (6) (2012) 801–807. [45] A.A. Abouhussien, A.A.A. Hassan, The use of acoustic emission intensity analysis for the assessment of cover crack growth in corroded concrete structures, J. Nondestruct. Eval. 35 (3) (2016) 52. [46] K.E. Ridgley, A.A. Abouhussien, A.A.A. Hassan, B. Colbourne, Characterisation of damage due to abrasion in SCC by acoustic emission analysis, Mag. Concr. Res. (2017). Ahead of Print: 1-10. [47] K.E. Ridgley, A.A. Abouhussien, A.A.A. Hassan, B. Colbourne, Evaluation of abrasion resistance of self-consolidating rubberized concrete by acoustic emission analysis, J. Mater. Civ. Eng. 30 (8) (2018) 04018196.