The application of synthetic aperture radar imaging technique to measure moisture content of concrete structures

Journal Pre-proofs The Application of Synthetic Aperture Radar Imaging Technique to Measure Moisture Content of Concrete Structures Jones Owusu Twumasi, Paul DeStefano, John T. Christian PII: DOI: Reference:

S0263-2241(19)31199-6 https://doi.org/10.1016/j.measurement.2019.107335 MEASUR 107335

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Measurement

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24 June 2019 8 October 2019 27 November 2019

Please cite this article as: J.O. Twumasi, P. DeStefano, J.T. Christian, The Application of Synthetic Aperture Radar Imaging Technique to Measure Moisture Content of Concrete Structures, Measurement (2019), doi: https://doi.org/ 10.1016/j.measurement.2019.107335

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The Application of Synthetic Aperture Radar Imaging Technique to Measure Moisture Content of Concrete Structures Jones Owusu Twumasi1, Paul DeStefano2*, and John T. Christian3 1

2

Ph.D. Candidate, Department of Civil and Environmental Engineering, UMass Lowell Assoc. Teaching Professor, Department of Civil and Environmental Engineering, UMass Lowell 3 Professor, Department of Civil and Environmental Engineering, UMass Lowell

1 University Avenue, Lowell, MA, 01854 *

Corresponding author: [email protected]

Abstract The presence of free moisture inside concrete is a significant cause of deterioration (e.g. alkalisilica-reaction-ASR, steel rebar corrosion, freeze-thaw) in reinforced concrete structures. A 10 GHz synthetic aperture radar system was used to scan five concrete specimens with water-tocement (w/c) ratios ranging from 0.35 to 0.55 and at moisture contents varying from dry (0.25%) to saturated (4.8%). The experimental data were used to develop a regression model, which illustrates an important relationship between the SAR image and moisture content at a specified w/c ratio. The model indicated that integrated SAR amplitude increases non-linearly with the moisture content of concrete at a given w/c ratio. The study concludes that the inverse of the developed model is adequate for estimating in-place moisture content of concrete using SAR imaging techniques and that the w/c ratio has a significant effect on the developed relationship. Keywords: Concrete, Moisture content, Water-to-cement ratio, Oven-drying, Synthetic aperture radar 1. Introduction The presence of free moisture inside cured concrete is a significant cause of various types and levels of deterioration (e.g. alkali-silica-reaction-ASR, carbonation, steel reinforcement corrosion, freezing, and thawing) in reinforced concrete (RC) structures. Bu et al. [1] reported that the critical degree of saturation required for freeze-thaw damage of concrete is between 80% and 90%. In addition, Goran [2] found that moisture saturation levels greater than 80% (~5% moisture content by mass) will damage non-air-entrained concrete by freeze-thaw activity. Also, moisture accelerates the diffusion of chloride ions through concrete pores which cause chlorideinduced corrosion in RC structures [3, 4]. Carbonation in RC structures begins when the optimal relative humidity is between 55% and 65% (1.8% ∼ 2% moisture content by mass) [5]. Tomosawa et al. [6] reported that moisture content beyond 4% (by mass) induces significant ASR damage in concrete. Apart from the physicochemical durability problems free moisture causes, concrete mechanical properties (e.g., compressive strength, tensile strength, and modulus of elasticity) are greatly influenced by the water-to-cement (w/c) ratio of the concrete mix [7], [8].

1

2. Literature review Several non-destructive testing techniques have been developed over the years to detect and measure the in-place moisture content of concrete structures. This is due to the critical role moisture plays in the durability of concrete. These moisture measurement techniques include gravimetric ([9] and [10]), gamma-densitometry ([11], [12], and [13]), and microwave/radar methods ([14], [15] and [16]). Sbarta et al. [17] suggested that the most suitable moisture detection method must be non-destructive and sensitive at all moisture levels. In addition, they must be unaffected by factors such as temperature and soluble salt content. Above all, they must be able to measure the amount of moisture within a concrete matrix. Azenha et al. [18] argued that the ideal moisture measurement technique should be able to quantify the free, physically bound, and chemically bound water inside the concrete. Recently, various moisture measurement techniques are utilized to investigate the moisture content of field concrete structures. In the case of operational structures (e.g. bridges), nondestructive evaluation (NDE) techniques are preferred to destructive/intrusive methods because structural integrity and performance remain intact. Microwave/radar is a popular NDE technique used for both laboratory evaluation of concrete specimens ([15], [17], [19] and [20]) and field condition assessment of RC structures ([8] and [21]). This is because electromagnetic (EM) waves can penetrate dielectrics such as concrete and are sensitive to variations of moisture inside the concrete. Furthermore, changes in light intensity and temperature cannot alter EM wave propagation. Hasted and Shah [22] detected moisture content by measuring the dielectric constant of concrete using guided waves at three frequencies (3, 9 and 24 GHz). They found that the presence of moisture amplifies the dielectric constant of concrete non-linearly. Al-Mattarneh et al. [23] characterized moisture content inside concrete specimens with different w/c ratios using a 12 GHz open-ended rectangular waveguide. Their results indicated that the dielectric constant of concrete increases exponentially with moisture content at a specified w/c ratio. Al-Qadi et al. [24] reported that dielectric constant decreases steadily with frequency within 28 days of curing concrete. However, the loss factor remains almost constant with frequency. After the 28 days curing period, the dielectric constant decreased with the w/c ratio. Al-Qadi et al. [23] used a parallel plate capacitor to measure the dielectric constant and the loss factor measurements in the frequency range of 100 kHz ∼ 40 MHz. Lai et al. [25] detected moisture inside normal and lightweight concrete using groundpenetrating radar (GPR) with a center frequency of 1.5 GHz. In both normal and lightweight concrete, the dielectric constant increased non-lineally with the increase in moisture content. Sbarta et al. [26] also modeled the moisture content of concrete using GPR measurements and reported that the loss factor (attenuation) increases non-linearly with the increase in concrete moisture content. Klysz et al. [27] measured the moisture content of concrete specimens with w/c ratios of 0.66 and 0.48 using a 1.5 GHz GPR and found that the GPR signal amplitude decreased linearly with the moisture content due to the attenuation of EM waves. Furthermore, Alzeyadi and Yu [28, 29] applied a 10 GHz SAR imaging system to a concrete specimen with a w/c ratio of 0.45, and the integrated SAR amplitude was reported to increase non-linearly with moisture content. 2

Several studies relating to concrete moisture content measurement using microwave/radar sensors have been reported [15-17]. In general, the literature reveals that microwave/radar sensors can detect and measure moisture within the concrete matrix and indicate that the presence of moisture inside concrete absorbs EM wave energy (attenuation of EM waves). Also, the dielectric constant of concrete increases non-linearly with the increase in moisture content. Moreover, the dielectric constant of concrete decreases with the w/c ratio at a constant moisture content. Although these studies provide significant insights into microwave/radar NDE capabilities, empirical models that relate microwave/radar parameters to both moisture content and w/c ratio are scarce in the literature.

3. SAR imaging principle Synthetic aperture radar imaging (SAR) NDE is an active microwave technique where EM waves are emitted to a target and reflected EM waves are collected from the target data analysis. Commonly used SAR imaging modes are the spotlight, stripmap, and inverse SAR. In the spotlight SAR mode, a longer aperture is formed by steering the transmitted radar beam, so it follows the target as the platform moves. Whiles in the inverse SAR mode, a synthetic aperture is created by utilizing the movement of the target rather than the radar. The stripmap SAR mode, on the other hand, employs a linear collection geometry and a fixed antenna orientation. Stripmap SAR imaging mode was used in this research. Imaging results are either represented as cross-range-cross-range (rx - rx) or range-cross-range (r - rx). The image formation process can be formulated by considering a planar scattering problem in a domain (Ωs) containing N scattering points (Fig. 1). Given an incident wave with unit amplitude described by Eq. (1) [30]: 1 𝜓inc (𝑟̅ ) = 𝑟 . exp(𝑖𝑘̅𝑖 . 𝑟̅ )

(1)

where 𝑘̅𝑖 = 𝑘𝑖𝑥 𝑥̂ − 𝑘𝑖𝑥 𝑦̂ = the incident wave vector; 𝑟̅ = the relative position from the radar to any observation point; 𝑟 = length of relative position vector; i = imaginary number; 𝑥̂ and 𝑦̂ are both vectors in the Cartesian coordinate system. The incident and scattered wave configuration are shown in Fig. 1.

Fig. 1. Scattering of N-point scatterers [21] 3

For a scatterer j at 𝑟̅𝑗 and observed at 𝑟̅ , the scattered field is given by: 𝜓scat (𝑟̅ , 𝑟̅𝑗 ) =

̂ 𝑖) 𝑠𝑗 (𝑟̅ ,𝑘 |𝑟̅ −𝑟̅𝑗 |

. exp(𝑖𝑘|𝑟̅ − 𝑟̅𝑗 |). 𝜓inc (𝑟̅ )

(2)

Where; 𝑠𝑗 = 𝑠𝑗 (𝑟̅ , 𝑘̂𝑖 ) = scattered amplitude at scatterer j due to an incident wave at 𝑘̂𝑖 observed at 𝑟̅ . Assuming no interaction among scatterers, the total scattered field from N scatters observed at 𝑟̅ is obtained by summing the scattered fields from all scatterers and is defined as follows:

𝜓scat (𝑟̅ ) = ∑𝑁 𝑗=1

̂ 𝑖) 𝑠𝑗 (𝑟̅ ,𝑘 |𝑟̅ −𝑟̅𝑗 |

. exp(𝑖𝑘|𝑟̅ − 𝑟̅𝑗 |) . 𝜓inc (𝑟̅ )

(3)

Where; 𝑘̅𝑠 = 𝑘𝑠𝑥 𝑥̂ + 𝑘𝑠𝑦 𝑦̂ = the scattered wave vector and 𝑘̅𝑠 = −𝑘̅𝑖 when a single radar antenna is used. The case of a single scatterer without losing generality can be deduced from this formulation. Since 𝑘 = 𝜔⁄𝑐 and 𝜃 = 𝜃𝑖 = tan−1(𝑘𝑖𝑦 ⁄𝑘𝑖𝑥 ), the Eq. (3) can be rewritten as: 𝑠

𝑟

𝜓scat (𝜔, 𝜃) = 𝜓scat (𝑘, 𝑟̅𝑠 ) = 𝑟𝜃2 . exp [𝑖 𝑐 𝜔(1 + cos 2 𝜃 − sin2 𝜃)]

(4)

Eq. (4) is a sliced projection of the two-dimensional (2D) Fourier transform of the scatterer domain (Ωs). While reconstruction algorithms integrate all projections and perform 2D inverse Fourier transform (IFT -plane projection) to obtain the final image, the backprojection algorithm on the other hand first performs a line projection (1D IFT) to generate all subimages. The final image is rendered by integrating all subimages. A modulation operation (frequency domain) or a convolution operation (time-domain) is performed so that the center of the backprojected image coincides with the center of the scatterer. In the backprojection algorithm, this shifting-back step is achieved by applying a ramp filter in the frequency domain, where the frequency ωn is shifted back by a carrier frequency ωc [31]. 𝜔max

𝑃(𝜈, 𝜃) = ∫ 𝑑𝜔. 𝜓scat (𝜔 − 𝜔𝑐 , 𝜃)|𝜔 − 𝜔𝑐 |. exp(−𝑖𝜔𝜈) 𝜔min

𝜔max

𝑠𝜃 𝑟 = 2 . ∫ 𝑑𝜔. |𝜔 − 𝜔𝑐 |. exp [𝑖 (𝜔 − 𝜔𝑐 ) × (1 + cos2 𝜃 − sin2 𝜃) − 𝑖𝜔𝜈] (5) 𝑟 𝑐 𝜔min

Where; 𝜈 = spatial variable of the 1D IFT projection. Translating the local 1D IFT coordinate [𝜈, 𝑃(𝜈, 𝜃𝑠 )] to the global polar coordinate (𝑟, 𝜙), 𝜈 leads to:

4

𝜈 = 𝑟cos(𝜙 − 𝜃𝑠 )

(6)

SAR image quality is improved by transforming 𝑃(𝜈, 𝜃) to 𝑃[𝑟cos(𝜙 − 𝜃), 𝜃] through an upsampling process. The mathematical background behind the upsampling can be found elsewhere [30, 31]. In polar coordinates, the backprojection image is finally obtained by integrating the azimuth angle over the entire inspection range as shown in Eq. (7). 𝜃int⁄2

∫ 𝑑𝜃. 𝑃 (𝑟cos(𝜙 − 𝜃), 𝜃)

𝐼(𝑟, 𝜙) =

−𝜃int⁄2

𝑠𝜃 = 2. 𝑟

𝜃int⁄2

∫ 𝑑𝜃 −𝜃int⁄2

𝜔max

𝑟 ∫ 𝑑𝜔. |𝜔 − 𝜔𝑐 |. exp [𝑖 (𝜔 − 𝜔𝑐 ) 𝑐

𝜔min

× (1 + cos 2 𝜃 − sin2 𝜃) − 𝑖𝜔𝑟cos(𝜙 − 𝜃𝑠 )]

(7)

Where; the polar coordinate variables (𝑟, 𝜙) are related to the Cartesian coordinate variables by: 𝑥 = 𝑟cos𝜙

(8)

𝑦 = 𝑟sin𝜙

(9)

Thus, the image plane is reconstructed as 𝐼(𝑥, 𝑦) = 𝐼(𝑟cos𝜙, 𝑟sin𝜙). Therefore, the amplitude of the final backprojected SAR image in the range-cross-range plane is given by: ∞

𝐼(𝑟, 𝑟𝑥 ) = ∫−∞ ℎ (𝑡 − 𝑟 ′ 𝑥 )2 ] 𝑑𝑟 ′ 𝑥 𝑑𝑟 ′

2𝑟 ′ 𝑐

𝑟′

𝑅

𝑟𝑥 −𝑟 ′ 𝑥

) exp (−4𝑖𝜋 𝜆 ) . 𝐴(𝑟 , ) ∫0 𝑥 𝑆(𝑟 ′ , 𝑟 ′ 𝑥 ). 𝑎 (

𝑅0

) exp[−𝑖𝐹(𝑟𝑥 − (10)

Where; r = range, rx = cross-range, h = a matched filter, t = time, c = the velocity of light, i = imaginary number (√−1), λ = wavelength, A = a function accounting for antenna pattern, processing gain and the range spreading loss, Rx = maximum cross-range, S = scattering amplitude, a = the two-way amplitude azimuth antenna pattern, and R0 = range location of the radar and F = a focusing function. Fig.3 illustrates the processing step of the backprojection algorithm.

5

Backprojection/ image reconstruction Data collection Superposition of sub-images Radar (Tx) Transformation of coordinates (upsampling)

Incident EM waves

Concrete specimen 1-D image projection (IFT)

Reflected EM waves after interaction with concrete

Radar (Rx)

Back-shifting process

Fig. 2. SAR image data collection and backprojection/image reconstruction process

4. Experimental investigation 4.1. Specimens design and description Five concrete specimens with dimensions of 152.4 × 152.4 × 50.8 mm were manufactured in the laboratory using Type I/II ordinary Portland cement. Basic cement- aggregate mix ratio (by mass using Mettler digital platform scale: Model PE24 with accuracy of ±1 g ) used to prepare the specimens was 1 cement: 2 sand: 3 gravels and were grouped by specified w/c ratios (Φ) of 0.35, 0.40, 0.45, 0.50 and 0.55, by mass. The specimens were moist cured for 7 days. Furthermore, they were air-dried for 36 months in the laboratory to simulate well-cured and aged concrete specimens. Following the air-drying process, each specimen was moisture conditioned to represent varying degrees of free moisture contained within the concrete as described in the next section.

4.2.Moisture conditioning The concrete specimens were submerged in water for four days and a napkin was used to clean the surface water. SAR images of the concrete specimens at wet conditions were collected. Mass measurements were made using an electronic scale (Model: VB-302A by Virtual Measurement and Control, ± 0.05 g) after the SAR image measurement. An electronic oven (Model: Blue M by Electric Company) was used to heat the concrete specimens at a temperature of 105◦C and at different times to achieve various moisture contents. At the end of each oven heating period, the concrete specimen was allowed to cool to room temperature (∼ 25◦C) and the SAR measurement was taken. Following each SAR measurement, the mass of the concrete specimen was measured (mt). The heating process, SAR measurements, and mass measurements were continued until the mass of the concrete specimen became stable. Mass of the concrete specimen after stabilization 6

was taken as the oven-dried mass (mOD). The moisture content of the concrete specimen was calculated using Eq. (11). 𝑚𝑡 −𝑚𝑂𝐷

𝛹=(

𝑚𝑂𝐷

) × 100

(11)

where ψ = moisture content (%), mt = mass of specimen at a given oven drying time (g), mOD = oven-dried mass of specimen (g).

4.3.SAR imaging of the concrete specimen Laboratory SAR measurements of the concrete specimens were taken at the University of Massachusetts Lowell Electromagnetic Remote Sensing Laboratory using a monostatic (single antenna) SAR imaging system. The SAR system consisted of a 313 × 305 × 133 mm radar box (horn antenna, signal generator, and power supply (Fig. 3(a)), operating at 10 GHz frequency and 1.5 GHz bandwidth. It was attached to a 2-dimensional positioner which allows both vertical and horizontal scans (Fig. 3 (b)). Throughout the SAR imaging, a constant angle was maintained between the radar antenna and the concrete specimen. All the concrete specimens were positioned such that the emitted EM waves impinged on it at an angle of 90◦. A constant range (R = 18 cm), cross-range (Rx = 30cm), and a stepping increment of 0.625 cm was used in this study. Photographs of the radar hardware and the SAR system are shown in Fig. 3. Fig. 4 is a photograph and schematic diagram of the SAR imaging setup.

2D Positioner

Radar sensor

(a)

(b)

Fig. 3. Photographs of radar hardware (a) [32] and radar system (b)

7

Concrete specimen Radar

Wooden support

(a)

(b)

Fig. 4. Photograph (a) and schematic diagram (b) of the concrete specimen and SAR system

5. Result and Discussions 5.1.SAR images In total, twenty-five SAR images of the concrete specimens with different w/c ratios and moisture contents were collected and analyzed in this research. To visualize the amplitude distribution on a color scale of 0 to 2000, each SAR image was rendered in a range-cross-range (r – rx) domain. The amplitude of a SAR image is an amplified reflected scattering signal from a target, which is obtained at the end of the backprojection process (Eq. (10)). Amplification and the backprojection processes make the unit of measurement of the SAR amplitude meaningless, hence, it is ignored. However, the physical meaning of SAR amplitude is related to the dielectric properties of the target under investigation. Higher SAR amplitudes (warmer colors - red in color print, and black in grayscale print) is an indication of a stronger electromagnetic scattering response, which means that the target under investigation has greater values of dielectric constant. The black rectangular dashed outline in the SAR images represents the top view (r = 5.08 cm by rx = 15.24 cm) of the concrete specimen. Fig. 5 shows typical 2-D SAR images of concrete specimens at the highest and the lowest moisture contents for each w/c ratio (Φ). Figs. 6(a) and 6 (b) illustrate the general moisture effect on SAR amplitude at the highest and lowest moisture contents for Φ = 0.45.

5.2.Effect of moisture on SAR image amplitude 8

Fig. 5 clearly shows, by color variation, that SAR amplitudes are significantly higher at higher moisture contents compared to the ones at lower moisture contents for all w/c ratios. Fig. 6 also illustrates that an increase in moisture content (0.76% to 4.58%) corresponds to an increase in peak SAR amplitude (1500 to 1800). This is because moisture content amplifies the dielectric constant of dry concrete [22, 23, 25]. In the microwave frequency range, dry concrete and moisture/water have dielectric constants in the range of 5~ 15 [33] and 78~81 [34], respectively. Although the dielectric constant of water will decrease after interacting with other substances (of lower dielectric constants), the effective/average dielectric constant of the mixture (water and the substance) will be higher than the one of the substances according to dielectric mixture theory [35]. Hence, it is expected that the presence of moisture/water will amplify the dielectric constant (or amplitude in the case of SAR imaging) of dry concrete. With high effective dielectric constant, the dielectric contrast between air (the first medium through which the EM wave propagates) and concrete (the second medium through which the EM wave propagates) increases. This is because the dielectric constant of air (~1 [36]) is relatively small compared to the effective dielectric constant of moist concrete. As a result, most of the incident EM waves are reflected and received by the radar antenna. Alzeyadi and Yu [27, 28] reported a similar finding. A quantitative analysis of moisture effect on the amplitude of the SAR image is presented in Section 5.3. 5.3.Moisture content modeling 5.3.1. Integrated SAR amplitude The quantitative analysis of free moisture inside concrete was performed by mapping the moisture contents and integrated amplitudes of the SAR image (integrated SAR amplitude). The integrated SAR amplitude provides more insights into the amplification of the reflected EM wave from the concrete specimens as moisture content increases, which is illustrated in Fig. 7 and described by Eq. (12). 𝑟

𝑗 𝑥𝑖 ∑𝑟𝑖=𝑟 𝐼𝑖𝑛𝑡 = ∑𝑗=𝑟 𝐼(𝑟𝑗 , 𝑟𝑥𝑖 ) 𝑥1

(12)

1

Where; Iint = integrated SAR amplitude, r, and rx = range and cross-range, respectively, i and j = cross-range and range indexes, respectively.

9

Fig. 5. SAR images of concrete specimens with different w/c ratios (ϕ) at the highest (e.g. 𝜓 = 4.02% at ϕ =0.35) and lowest moisture contents (𝜓 = 0.69% at ϕ =0.35)

(a) SAR amp. along cross-range rx = 15 cm 10

(b) SAR amp. along range r = 18 cm

Fig. 6. The amplitude of SAR image along cross-range (a) and range axes based the SAR image of the concrete specimen with w/c ratio (Φ) = 0.45 at highest (4.58%) and lowest (0.76%) moisture content

(𝑟𝑥1 , 𝑟𝑗 )

(𝑟𝑥1 , 𝑟1 )

(𝑟𝑥𝑖 , 𝑟𝑗 )

(𝑟𝑥𝑖 , 𝑟1 )

Fig.7. Definition of integrated SAR amplitude

5.3.2. Regression modeling The integrated SAR amplitude and moisture content of concrete at a specified w/c ratio are exponentially related as reported by Alzeyadi and Yu [27, 28]. In addition, Al-Mattarneh et al. [23] reported that at a constant w/c ratio, the reflection coefficient increases exponentially when the moisture content of concrete increases by using open-ended rectangular waveguide. The exponential increase in reflected EM wave parameters such as integrated SAR amplitude and reflection coefficient is attributed to the exponential decay of EM wave propagation in dielectrics (e.g., moist concrete) [37]. Based on this knowledge, an exponential model was developed for moisture content using SAR images of concrete specimens and grouped by different w/c ratios. Integrated SAR amplitude was used as the response variable and moisture content as the independent variable. Least-square regression error technique was used to estimate the model parameters in MATLAB®. The Best-fit exponential regression model resulted in a non-linear relationship between integrated SAR amplitude and the moisture content data. As moisture content increases, the integrated SAR amplitude increases exponentially for all the concrete specimens tested as shown in Fig. 8 and described by Eq. (13). This result is consistent with reported findings in the literature [23, 27, 28]. The model performance was assessed using the coefficient of determination (R2). The high R2 values in Table 1 indicate that the model fits the experimental data very well (Fig. 8).

11

𝐼𝑖𝑛𝑡 (𝜓) = 𝐼o − 𝜌[1 − exp(𝛽𝜓)]

(13)

where 𝐼o = Integrated SAR amplitude at 0% moisture content, 𝜌 = Integrated SAR amplitude modulation factor, 𝛽 = moisture loss factor, An inverse model (Eq. (14)) is obtained by rearranging Eq. (13) which is used to estimate the moisture content of concrete specimens using an integrated SAR image in this study.

1

𝐼

−𝐼

𝜓(𝐼𝑖𝑛𝑡 ) = 𝛽 ln [1 + ( 𝑖𝑛𝑡𝜌 o)]

(14)

Table 1. Regression model coefficients and R2 values of integrated SAR amplitude models Specimen 𝐼o × 106 𝜌 × 104 𝛽 * CN35 4.8390 6.8197 0.5228 CN40 4.5888 9.2567 0.5492 CN45 4.4198 9.7104 0.5622 CN50 4.3204 5.8892 0.6903 CN55 4.2170 2.8034 0.8205 * CN 35 means w/c ratio of the specimen is 0.35

R2 0.9665 0.9771 0.9918 0.9889 0.9901

4

𝑆 × 10 4.8345 9.7433 18.7645 7.7471 7.5794

The following important observations are noted from the results in Table 1. First, Table 1 clearly shows that integrated SAR amplitude at 0% moisture content (Io) decreases with the w/c ratio, which suggests that the dielectric constant of dry concrete (0% moisture content) decreases with the increase in the w/c ratio. At 0% moisture content, concrete specimens with higher w/c ratios are more porous (more air voids) than the ones with lower w/c ratios. Therefore, the effective dielectric constant of dry concrete is expected to decrease with the w/c ratio. Other findings reported in the literature [23, 24, 27] have confirmed this result. Second, the moisture loss factor (β) increases with the w/c ratio, which means that within the same environmental condition, concrete specimens with higher w/c ratios lose moisture faster than the ones with lower w/c ratios [38]. Finally, the integrated SAR amplitude modulation factor (ρ) increases with w/c ratio to a maximum value and then decreases. In this study, w/c ratio = 0.45 corresponded to the maximum ρ, while w/c ratio = 0.50 was deduced from Al-Mattarneh et al.'s study [23].

12

Fig.8. Relationship of moisture content (𝜓) to the integrated SAR amplitude of concrete specimens at different w/c ratios (Φ)

6. Conclusion This paper presents an application of the SAR imaging NDE technique to the detection and measurement of moisture content in cured and aged concrete. Nonlinear regression model (Eq. (13)) of in-place moisture content versus the defined Integrated SAR amplitude was developed from observed data of five different specified water-to-cement ratio concrete specimens at varying degrees of moisture saturation. Based on the findings of this experimental investigation, significant conclusions of this research are summarized as follows: -

-

The presence of moisture within well-cured and aged concrete has a significant influence on the SAR image amplitudes at various known levels of water-to-cement (w/c) ratio. At various levels of the w/c ratio, the integrated SAR amplitude increases exponentially with an increase in moisture content for a given w/c ratio. W/C ratio of in-place concrete has a significant effect on the diagnosis of moisture content of cured concrete specimens using SAR techniques. The nonlinear regression model presented in Eq. (14) and the model parameters (Table 1) are considered adequate for estimating the moisture content of concrete structures with known levels of w/c ratio (0.35 to 0.55) using SAR imaging techniques. Considering the variability of regression model parameters exhibited in Table 1 (based on the standard error, S), the reliability of measuring in-place moisture content using SAR techniques should be further investigated.

Acknowledgments 13

The authors are grateful to the U.S. Department of Transportation, Office of the Assistant Secretary for Research and Technology (OST), through Commercial Remote Sensing and Spatial Information (CRS&SI) under Grant OASRTRS-14-H-UML (PI: Dr. Tzuyang Yu, UMass Lowell). The authors also thank undergraduate students Reny Yohana LendeMere, Thet Myat Noe Sein, Zixiang Huang and Nicolas D’Amico for helping with specimen manufacturing. Disclaimer The views, opinions, findings, and conclusions reflected in this paper are the responsibility of the authors only and do not represent the official policy or position of the USDOT/OST-R, or any State or other entity.

References [1] Bu, Y., R. Spragg, and W. J. Weiss. "Comparison of the pore volume in concrete as determined using ASTM C642 and vacuum saturation." Advances in Civil Engineering Materials 3, no. 1 (2014): 308-315. [2] Fagerlund, Göran. "The critical degree of saturation method of assessing the freeze/thaw resistance of concrete." Materials and Structures 10, no. 4 (1977): 217-229. [3] Homan, Lydia, Ayman Nureddin Ababneh, and Yunping Xi. "The effect of moisture transport on chloride penetration in concrete." Construction and Building Materials 125 (2016): 1189-1195. [4] Bentur, Arnon, Neal Berke, and Sidney Diamond. Steel corrosion in concrete: fundamentals and civil engineering practice. CRC Press, 1997. [5] Russell, Derek, PA Muhammed Basheer, GI Barry Rankin, and Adrian E. Long. "Effect of relative humidity and air permeability on prediction of the rate of carbonation of concrete." Proceedings of the Institution of Civil Engineers-Structures and Buildings 146, no. 3 (2001): 319-326. [6] Tomosawa, F., K. Tamura, and M. Abe. "Influence of water content of concrete on alkaliaggregate reaction." In Proc., 8th Int. Conf. on Alkali Aggregate Reaction in Concrete, pp. 881885. 1989. [7] Shoukry, Samir N., Gergis W. William, Brian Downie, and Mourad Y. Riad. "Effect of moisture and temperature on the mechanical properties of concrete." Construction and Building Materials 25, no. 2 (2011): 688-696. [8] Chen, Dar Hao, and Andrew Wimsatt. "Inspection and condition assessment using ground penetrating radar." Journal of geotechnical and geoenvironmental engineering 136, no. 1 (2009): 207-214. [9] Jafarifar, Naeimeh, Kypros Pilakoutas, and Terry Bennett. "Moisture transport and drying shrinkage properties of steel–fibre-reinforced-concrete." Construction and building materials 73 (2014): 41-50. [10] Thiyagarajan, Karthick, Sarath Kodagoda, and Nalika Ulapane. "Data-driven machine learning approach for predicting volumetric moisture content of concrete using resistance sensor measurements." In 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA), pp. 1288-1293. IEEE, 2016. [11] Multon, Stéphane, E. Merliot, Michel Joly, and François Toutlemonde. "Water distribution in beams damaged by Alkali-Silica Reaction: global weighing and local gammadensitometry." Materials and Structures 37, no. 5 (2004): 282. 14

[12] Amba, J. C., J. P. Balayssac, and C. H. Détriché. "Characterisation of differential shrinkage of bonded mortar overlays subjected to drying." Materials and structures 43, no. 1-2 (2010): 297. [13] Villain, Géraldine, and Mickaël Thiery. "Gammadensimetry: A method to determine drying and carbonation profiles in concrete." NDT & E International 39, no. 4 (2006): 328-337. [14] Sbartaï, Zoubir Mehdi, Stephane Laurens, Jean-Paul Balayssac, Gerard Ballivy, and Ginette Arliguie. "Effect of concrete moisture on radar signal amplitude." ACI materials journal 103, no. 6 (2006): 419. [15] Senin, S. F., and Roszilah Hamid. "Ground penetrating radar wave attenuation models for estimation of moisture and chloride content in concrete slab." Construction and Building Materials 106 (2016): 659-669. [16] Laurens, S., J-P. Balayssac, J. Rhazi, and Ginette Arliguie. "Influence of concrete relative humidity on the amplitude of Ground-Penetrating Radar (GPR) signal." Materials and Structures 35, no. 4 (2002): 198-203. [17] Sbartaï, Z. M., S. Laurens, J. Rhazi, J. P. Balayssac, and G. Arliguie. "Using radar direct wave for concrete condition assessment: Correlation with electrical resistivity." Journal of applied geophysics 62, no. 4 (2007): 361-374. [18] Cornell, J. B., and A. T. Coote. "The application of an infrared absorption technique to the measurement of moisture content of building materials." Journal of Applied Chemistry and Biotechnology 22, no. 4 (1972): 455-463. [19] Quincot, Gonzalo, Miguel Azenha, Joaquim Barros, and Rui Faria. "State of the art– Methods to measure moisture in concrete." Projetos De Investigação Científica E Desenvolvimento Tecnológico, Portugal (2011). [20] Yu, Tzu-Yang. Damage Detection of GFRP-Concrete Systems Using Electromagnetic Waves: Theory and Experiment. LAP LAMBERT Academic Publishing, 2010. [21] Yu, Tzu-Yang. "Distant damage-assessment method for multilayer composite systems using electromagnetic waves." Journal of Engineering Mechanics 137, no. 8 (2011): 547-560. [22] Hasted, J. B., and M. A. Shah. "Microwave absorption by water in building materials." British Journal of Applied Physics 15, no. 7 (1964): 825. [23] Al-Mattarneh, Hashem MA, Deepak K. Ghodgaonkar, and Wan Mahmood BWA Majid. "Microwave sensing of moisture content in concrete using open-ended rectangular waveguide." Subsurface Sensing Technologies and Applications 2, no. 4 (2001): 377-390. [24] Al-Qadi, Imad L., O. A. Hazim, W. Su, and S. M. Riad. "Dielectric properties of Portland cement concrete at low radio frequencies." Journal of Materials in Civil Engineering 7, no. 3 (1995): 192-198. [25] Lai, W. L., Shi Cong Kou, W. F. Tsang, and Chi Sun Poon. "Characterization of concrete properties from dielectric properties using ground penetrating radar." Cement and Concrete Research 39, no. 8 (2009): 687-695. [26] Sbartaï, Zoubir-Mehdi, Stéphane Laurens, and Denys Breysse. "Concrete moisture assessment using radar NDT technique–comparison between time and frequency domain analysis." Proceedings of non-destructive testing in civil engineering (NDTCE’09) (2009): 1-8. [27] Klysz, G., and J-P. Balayssac. "Determination of volumetric water content of concrete using ground-penetrating radar." Cement and concrete research 37, no. 8 (2007): 1164-1171 [28] Alzeyadi, Ahmed, and Tzuyang Yu. "Moisture determination of concrete panel using SAR imaging and the KRI transform." Construction and Building Materials 184 (2018): 351-360. [29] Alzeyadi, Ahmed, and Tzuyang Yu. "Characterization of Moisture Content in a Concrete Panel Using Synthetic Aperture Radar Images." Journal of Aerospace Engineering 32, no. 1 (2018): 04018112. [30] Kong, J. A. (2002). Electromagnetic Wave Theory EMW Publishing. 15

[31] Desai, Mita D., and W. Kenneth Jenkins. "Convolution backprojection image reconstruction for spotlight mode synthetic aperture radar." IEEE Transactions on Image Processing 1, no. 4 (1992): 505-517. [32] Yu, Tzuyang, Jones Owusu Twumasi, Viet Le, Qixiang Tang, and Nicolas D’Amico. "Surface and subsurface remote sensing of concrete structures using synthetic aperture radar imaging." Journal of Structural Engineering 143, no. 10 (2017): 04017143. [33] Buyukozturk, Oral. "Electromagnetic properties of concrete and their significance in nondestructive testing." Transportation research record 1574, no. 1 (1997): 10-17. [34] Archer, Donald G., and Peiming Wang. "The dielectric constant of water and Debye‐Hückel limiting law slopes." Journal of physical and chemical reference data 19, no. 2 (1990): 371-411. [35] Tinga, Wayne R., W. A. G. Voss, and D. F. Blossey. "Generalized approach to multiphase dielectric mixture theory." Journal of Applied Physics 44, no. 9 (1973): 3897-3902. [36] Hector, L. G., and H. L. Schultz. "The dielectric constant of air at radiofrequencies." Physics 7, no. 4 (1936): 133-136. [37] Kim, Sung, Jack Surek, and James Baker-Jarvis. "Electromagnetic metrology on concrete and corrosion." Journal of research of the National Institute of Standards and Technology 116, no. 3 (2011): 655. [38] Kim, Yun-Yong, Kwang-Myung Lee, Jin-Wook Bang, and Seung-Jun Kwon. "Effect of W/C ratio on durability and porosity in cement mortar with constant cement amount." Advances in Materials Science and Engineering 2014 (2014).

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Highlights

    

Synthetic aperture radar (SAR) image concrete measurement techniques are proposed. SAR image data of concrete with varying moisture conditions were collected in a laboratory. An exponential function was developed based on experimental SAR data. SAR is an effective technique for evaluating moisture content of concrete. SAR is an effective technique for evaluating water-to-cement ratio of concrete.

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