Uncertainty evaluation of the 1 GHz GPR antenna for the estimation of concrete asphalt thickness

Measurement 46 (2013) 3032–3040

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Uncertainty evaluation of the 1 GHz GPR antenna for the estimation of concrete asphalt thickness Mercedes Solla a,⇑, Higinio González-Jorge b, Henrique Lorenzo b, Pedro Arias b a b

Dept. Materials Engineering, Applied Mechanics & Construction, University of Vigo, School of Industrial Engineering, Torrecedeira 86, CP 36208 Vigo, Spain Dept. Natural Resources & Environmental Engineering, University of Vigo, School of Mining Engineering, Rua Maxwell, Lagoas-Marcosende, CP 36310 Vigo, Spain

a r t i c l e

i n f o

Article history: Received 14 January 2013 Received in revised form 4 May 2013 Accepted 19 June 2013 Available online 27 June 2013 Keywords: Ground penetrating radar Pavement Metrology Civil engineering

a b s t r a c t The uncertainty evaluation in the measurement of pavement thickness using ground penetrating radar is depicted in this work for operating frequencies of 1 GHz. Three areas of pavement with a number of different layers and thicknesses, the influence of two different human operators and the calibration/non-calibration of the radar signal were taken into account to evaluate the uncertainty contribution. Results depict smaller corrections for the calibrated values (ranging from 1 cm to 1.5 cm), while in the case of tabulated values corrections range between 3 cm and 3 cm. On the contrary, uncertainties show similar trends in both cases (calibrated and non-calibrated) being the upper limit around 2.5 cm. There are important differences between the results obtained for each one of the interpreters and it seems that it is an important factor to take into account in the evaluation of the uncertainty. In this work, this contribution was included as a rectangular type B probability distribution. The results do not depict differences between the type of material of the pavement and the thickness of the layer. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction Road inspection is very important to determine the exact time when rehabilitation must be performed in a road infrastructure. Surveying companies use a number of complementary sensors to the measuring of different road characteristics. Some examples are mobile LiDAR for geometric measurements [1–3], cameras for the detection of pavement cracks [4], laser profilers for the evaluation of the pavement surface roughness [5], and ground penetrating radar for the measurement of the pavement thickness [6–8]. For subsurface evaluation, such as pavement thickness estimation, non-destructive techniques (NDT) and near-surface remote sensing are recommended to reduce deterioration and provide an accurate overall analysis in a relatively low time in order to reduce costs in mainte⇑ Corresponding author. Tel.: +34 699419032. E-mail address: [email protected] (M. Solla). 0263-2241/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.measurement.2013.06.022

nance. Pavement thickness has traditionally been determined by drilling and extracting cores, which are considerably slower and expensive methods that greatly damage the road structure. In this way, ground penetrating radar (GPR) is one of the most frequently recommended NDT on road subsurface inspection that is rapid, cost effective, and allows field surveys to be conducted without disturbing the pavement structure and the normal traffic flow. GPR is a close-range remote sensing prospecting method based on the propagation of very short time domain electromagnetic pulses (1–20 ns) in the frequency bands of 10 MHz to 2.5 GHz. Using the GPR method, a transmitting antenna emits short pulses of electromagnetic energy into the pavement, and these pulses are partly reflected when they encounters media with different dielectric properties and partly transmitted into deeper layers. The reflected signal is recorded with an arrival time and amplitude that vary according to the location and nature of the

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dielectric discontinuities of the material (e.g. air/pavement and pavement/base interfaces). The thickness of the pavement correlates with the delay time of the electromagnetic pulse when the velocity of the pulse in the propagation medium is known. The antenna type selection depends on the requirements of the work. The low-frequency antennas give greater penetration capabilities but poorer resolution, while the high-frequency antennas depict better resolution but shallower penetration. When the aim of the work is to conduct an inspection of road pavements, high frequency antennas of 1 GHz or 2 GHz are typically used [9,10]. The possibility of detecting targets is greater when there is higher contrast between the electromagnetic properties of the medium under study and the surrounding medium, which can affect the resolution and accuracy of

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the method. Depth results using GPR are typically accurate to within ±10% [9,11]. Although most manufacturers of GPR systems display information about the accuracy of the systems (e.g. the MALÅ Geoscience and the GSSI companies have reported an accuracy of 3–5% and 4%, respectively), they do not provide any data about the uncertainty [12]. This figure, although it would be essential to determine if the equipment may or not respond to a given measurement tolerance, it is sometimes difficult to obtain because the systems are used in varied applications, in the same way that the methodologies used for the acquisition and data processing. This work aims to develop a methodology for the calculation of uncertainties in GPR systems used for pavement

Fig. 1. Types of asphalt pavements used for the construction of the experimental zone and their main components.

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thickness measurements, so that it can accurately determine whether the system can be used or not for the measurement of a pavement according to a tolerance previously predefined. Particularly, we analyzed the effectiveness of the 1 GHz antenna to determine pavement thickness. The acquisition data was carried out in a dynamic mode, as well as a static mode in order to provide the highest stability of the GPR signal. Moreover, for a deeper analysis of the uncertainty, different variables affecting the understanding and interpretation of the GPR signal were considered: the type of asphalt, different layer thicknesses, the use of calibrated velocity of propagation instead of non-calibrated (published or tabulated) values, the resolution of the antenna and the interpretational criteria of the GPR signal by two different interpreters. We considered three sceneries for testing, an experimental zone under controlled conditions and two different sections on in-service roads having some control points, in which the thicknesses were known from coring. The knowledge of the thicknesses in these control points allowed us to calibrate the velocity of propagation of the radar-wave for the different types of asphalts used. The manuscript is structured in the following form: Part 2 shows the materials and methods, including the area of study, the GPR instrumentation, the acquisition and processing techniques, and the uncertainty evaluation. Part 3 depicts the results and discussion for the different tests done and the validation of the method. Part 4 exhibits the conclusions. 2. Materials and methods 2.1. Area of study Different typologies of pavement layers were used in this work. First, an experimental zone was built in the neighborhood asphalt plant owned by the company Extraco S.A., in order to analyze the accuracy and precision of the GPR for obtaining the thickness of asphalt pavements under controlled conditions. For an exhaustive analysis, four types of asphalts, with different material composition, were evaluated. Fig. 1 shows the asphalts considered and the proportion of their main components.

The entire experimental zone was divided in four grids 3 m  3 m size. All the grids were composed by two different asphaltic layers with thickness 10 cm each one. The intermediate course (AC 22 bin G asphalt) was the same in all the grids, and the surface course was different for each grid by using the four types of asphalts analyzed (AC 22 surf G, AC 16 surf S, AC 16 surf D and BBTM 11B in Fig. 1). The design of the complete experimental zone is shown in Fig. 2. Field GPR tests were also conducted on in-service roads. These case studies correspond to measuring opportunities derived from the rehabilitation tasks developed by local construction companies. Two different sections of in-service roads were analyzed, and the pavement layers under study also correspond to different pavement typologies in agreement with the experimental zone. The reason for this decision is the interest in studying the possible effect of such material on the uncertainty measurement taking into account the forms most commonly used in pavements. In addition, each section contains a number of control points with known thicknesses in order to calibrate the radar-wave velocity. Core drilling was used in these case studies to provide an excellent accurate estimation of the pavement thickness. Table 1 describes the number of control points in each section and their corresponding pavement structure, as well as the thickness measured for each layer in the different control points. The Section 1 corresponded with a pavement structure composed of two layers of materials, an intermediate course (AC 16 bin S asphalt) and a base course (bituminous macadam). The total thickness for pavement structure (both intermediate and macadam layers) was 17 cm. The Section 2 was composed of the following two layers, the surface course (AC 22 surf S) and the intermediate course (AC 22 bin G). In this section, the total thickness was about 15–16 cm. 2.2. GPR survey 2.2.1. Data acquisition methodology We conducted the GPR surveys with a RAMAC/GPR CUII system from MALÅ Geoscience. The 1 GHz shielded antenna in ground-couple configuration was determined

Fig. 2. Design of the entire experimental zone showing the four grids 3 m  3 m size and the different asphalts used in the surface course. All the grids have an intermediate course composed of AC 22 bin G asphalt. Both surface course and intermediate course are 10 cm thick.

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Table 1 Field GPR tests on in-service roads: configuration and pavement structure of the sections. For each section, there are a number of control points with known thickness in order to calibrate the velocity of propagation of the radar-wave, and to determine the effectiveness of the GPR method for the estimation of the pavement thickness. Pavement structure (layers)

Control points and thicknesses (cm) Section 1

Surface Intermediate Base

Section 2

Pt. 1

Pt. 2

Pt. 3

Pt. 4

Pt. 1

Pt. 2

Pt. 3

Pt. 4

– 9 8

– 7.6 9.4

– 8.0 9.0

– 9.0 8.0

6.1 10.0 –

6.4 9.6 –

6.6 8.0 –

6.3 9.0 –

to be optimal in order to estimate the pavement thickness, as other authors have reported for a similar context [13,14]. This frequency provides on the order of 1.5 m in-depth penetration (under optimum conditions), and a spatial resolution of 3.0 cm, considering an average radar-wave velocity of 12.5 cm/ns, as reported in the literature for asphaltic medium [15]. Data acquisition was carried out in both static and dynamic modes for a better understanding of the GPR signal. The dynamic data acquisition was conducted using the common-offset mode (CO) with the antenna polarization perpendicular to the data collection direction. Using the CO mode, one or two antennas (shielded or unshielded antennas, respectively) are moved over the surface along a specific direction while keeping a constant distance between the transmitter and receiver. The acquisition parameters selected were total time windows of 17 ns and 0.05 s. or 3 cm trace-intervals for both static (in time) and dynamic (in distance) modes, respectively. Regarding this total time window defined, it is necessary to maintain appropriate resolution and avoid probable vertical or horizontal aliasing while the required depth is achieved. Then, it is necessary to consider the optimal number of samples (N) in which the total time window assumed is divided. The number of samples, when more accuracy is required, can be determined by Eq. (1) [16,17]. The value obtained is adjusted since the MALÅ GPR system uses a 16 bit A/D converter to transform the continuous GPR signal into digital values that represents the quantity’s amplitude. Therefore, the GPR data collection parameters included a sampling interval (Eq. (2)) of 0.17 ns and acquisition of 216 samples.

twt t

ð1Þ

1000 6f

ð2Þ

N¼2

T¼

where N is the number of frequency steps or samples, twt is the total time windows in ns, t is the maximum sampling interval in ns, and f is the central frequency of the antenna in MHz. 2.2.2. Data processing and velocity determination We processed the GPR data collected with the ReflexW v.5.6 software [18] to correct for the down-shifting of the radar section due to the air-ground interface, to amplify the received signal, and to remove both low- and high-

frequency noise in the vertical and horizontal directions. Our procedures to correct the GPR data collected in a dynamic mode were performed in the following sequence: time-zero correction, dewow filtering, gain application, spatial filtering, and band-pass filter. The sequence processing assumed to filter the data acquired in a static mode was: time-zero correction, dewow filtering and gain application. To evaluate the accuracy of the 1 GHz GPR antenna for the estimation of the pavement thickness, we assumed the following procedure: – First, the radar-wave velocity was determined from the knowledge of the real thicknesses obtained by coring, which were constant thicknesses in the case of the experimental zone and the thicknesses of the first control point for the sections on in-service roads (Table 1). – Once the radar-wave velocity was calibrated, we determined the pavement thicknesses in the remaining control points by considering the respectively velocity previously obtained. Then, we compared the thickness values thus estimated with the real values obtained from the core measurements. For the uncertainty analysis, we considered more variables as explained in the next Section 2.3. To calculate the radar-wave velocities, we use the manual difference pick application [18] by considering the wave travel-time distance (Dt) from reflections at known distance (the reflections from the air/ground interface (R1) to the one at the interface between two different pavement layers (R2) as illustrated in Fig. 3). The traveltime distance was determined as the distance (ns) between the maximums (positive or negative) of the reflected pulses. Knowing the thickness of the layers (Table 1) and the travel-time difference (Dt) to and from the target, the radar-wave velocity of propagation can be derived from Eq. (3), where the thickness is coincident with the distance travelled by the radar-wave (d).

d¼m

t 2

ð3Þ

where m is the velocity of propagation and t is the traveltime difference (Dt) to and from the target. Fig. 3 shows both processed dynamic (A) and static (B) data obtained in Section 2 with two different pavement layers, intermediate course and surface course (Pt.3 in Table 1). We identified the reflections produced by the air/ ground (R1) and the intermediate/base (R2) interfaces.

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Fig. 3. GPR profile collected with the 1 GHz ground-coupled antenna in dynamic mode (A) through Section 2 and the individual pulse from the static data (B) acquired over the point 3 in the Section 2. The profiles have reflections form interfaces of two mediums with different dielectric properties (e). R1 and R2 indicate the air/ground and intermediate/base interfaces, respectively. The orange line in radargram A represents the location for the static profile conducted.

The 1 GHz frequency did not provided enough range resolution [16] to separately identify both reflections generated at the interface between different layers (surface/ intermediate and intermediate/base interfaces). For thicknesses less than approximately 10 cm, the reflection at the interface between both surface course and intermediate course might be overlapped with the reflection at the air/ground interface and they are not like different events [19]. Therefore, the radar-wave velocity was calculated for each individual type of asphalt in the case of the experimental zone, both layers with thickness 10 cm each one. For the case of the sections on in-service roads, as the thicknesses of the surface courses (Table 1) were not enough to be detected by this frequency, the velocity was estimated for the combination of the different asphalts at each control point. Consequently, the pavement thicknesses were also determined for each individual asphalt or combination of different asphalts depending on the study case. The time and the amplitude are two variables of interest in a GPR signal. We determined the difference pick in time application to be optimal for the estimation of the wave travel-time distance (Dt) by taking into account the previous tests performed [20], with the same GPR system used in this work, in order to evaluate the stability of the recorded signal. The results obtained in those works demonstrated a higher stability in time than in amplitude, which is very suitable to ensure an accurate estimation of the wave travel-time distance (Dt) and the subsequent estimation of thicknesses in road pavements. Rial et al. [20] reported variations in amplitude of 1%, whereas the variations in time are practically insignificant. Nevertheless, by using the

manual difference pick application greatly depends on the interpretation criterion of the GPR signal. The value resulted for the wave travel-time distance (Dt) could be therefore significantly different [20]. In this sense, all the calculations made in this work were obtained by two different interpreters in order to minimize this probable variability. Moreover, we acquired the GPR data over the control points in a static mode to achieve a higher stability of the GPR signal, and each measurement was successively repeated ten times in order to provide the most appropriate average value. Table 2 shows the average radar-wave velocities determined by the two interpreters for each case study in this work. The velocity values obtained for the individual layers of asphalts ranged from 10.1 to 12.2 cm/ns (Table 2), whereas the values for the combinations of different types of asphalts, which ranged from 10.4 to 13.0 cm/ns, presented higher variation. It demonstrated the importance of the most appropriate calibration of the radar-wave velocity for each particular case when accurate pavement thicknesses are required. We compared the average velocity values obtained for both interpreters, which resulted in a maximum difference of 5%. We used the remaining control points to estimate the pavement thickness by following the inverse procedure explained to determine the velocity. Knowing the velocity of propagation for each asphalt or combination of different asphalts (Table 2) and the travel-time difference to and from the target (Dt), a measurement of thickness can be therefore derivate from Eq. (3). All the measurements were also made by two different interpreters and repeated ten times to carefully analyze the uncertainty of the method.

Table 2 Average radar-wave velocities obtained for either individual type of asphalt or combination of different asphalts. The velocity values were obtained from the procedure of the manual difference pick application by the known thickness obtained from core measurements, in which all the measurements were repeated ten times by two different interpreters. Pavement structure (layers)

Section 1 Section 2 Experimental zone

Velocity (cm/ns)

AC 16 bin S + Bituminous Macadam AC 22 surf S + AC 22 bin G AC 22 surf G AC 16 surf S AC 16 surf D BBTM 11B

Interpreter 1

Interpreter 2

10.74 12.71 10.14 10.38 12.22 10.72

10.40 13.00 10.30 10.47 11.92 10.20

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2.3. Uncertainty budget

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Measurement uncertainty is a non-negative parameter characterizing the dispersion of the values attributed to a measured quantity. The uncertainty has a probabilistic basis and reflects incomplete knowledge of the quantity. All measurements are subject to uncertainty and a measured value is only complete if is accompanied by a statement of the associated uncertainty [21–24]. The Guide to the Expression of Uncertainty in Measurement [25] is a document published by the Joint Committee for Guides in Metrology that establishes the general rules for evaluating and expressing uncertainty in measurement. One of the basic premises of the GUM approach is that it is possible to characterize the quality of a measurement by accounting for both systematic and random errors on a comparable footing, and a method is provided for doing that. This method refines the information previously provided in an ‘‘error analysis’’, and puts it on a probabilistic basis through the concept of measurement uncertainty. Knowledge about an input quantity Xi is inferred from repeated measured values (Type A evaluation of uncertainty) or scientific knowledge or other relevant information about the possible values of the quantity (Type B evaluation of uncertainty). In Type A evaluations of measurement uncertainty, the distribution best describing an input quantity X from repeated measured values of it is a Gaussian distribution. X then has expectation equal to the

average measured value and standard deviation equal to the standard deviation of the average. For a Type B evaluation of uncertainty, often the only available information is that X lies in a specified interval [a, b]. In such case, knowledge of the quantity can be characterized by a rectangular probability distribution with limits a and b. Once the input quantities have been characterized by appropriate probability distributions, and the measurement model has been developed, the probability distribution for the measure and Y is fully specified in terms of this information. The model function for the uncertainty evaluation in pavement measurements using GPR contains two main terms. The first one, type A evaluation, is related with the standard deviation of the measurements. The standard deviation indicates the repeatability, the variation in the measurements taken by a single person or instrument on the same item and under the same conditions. The second one, type B evaluation, indicates the reproducibility and resolution of the GPR antenna. Reproducibility is the degree of agreement between measurements or observations conducted on replicate specimens in different locations by different people. This fact appears very important in GPR metrology because this instrument is very influenced by the interpretation of the operator. Resolution is related with the level of information obtained for a certain device. In GPR technology resolution is related with the wavelength of the instrument and the radar-wave velocity of propagation. Antennas with higher frequency depict

Fig. 4. Correction and uncertainty results for the experimental zone. Surface course.

Fig. 5. Correction and uncertainty results for Section 1. Intermediate course joined to base course.

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Fig. 6. Correction and uncertainty results for Section 2. (A) intermediate layer and (B) surface course and intermediate course (both joined).

higher spatial resolution. The expanded uncertainty U can be expressed in the following form:

3. Results and discussion

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi U ¼ 2 u2A þ u2B

Fig. 4 shows the correction and uncertainty results for the surface course of the pavement in the experimental zone (Fig. 2). Four different types of pavement are studied (AC 22 surf G, AC 16 surf S, AC 16 surf D and BBTM 11B). Results are divided for the two different operators (op1 and op2) and two different methodologies, using the calibration with coring or estimated radar-wave velocities as previously explained in Section 2.2.2 (cal results) and non-calibration or tabulated radar-wave velocities (tab results). To obtain these tabulated results, a single radarwave velocity of 12.5 cm/ns was used to characterize the asphalt media, which was assumed from the published literature [15]. We observed as calibrated values are closer to real values (<0.5 cm) in all cases, while tabulated values range between 0 and 1.8 cm. Differences between the two interpreters are not very significant being in the most of the cases around 0.1–0.2 cm. Uncertainty values depict in general a similar trend for the different methodologies (calibrated and non-calibrated) and for the different operators. Uncertainty values approximately ranged from 1.8 to 2.4 cm. Fig. 5 exhibits the correction and uncertainty results for the intermediate and base layer (both joined) in Section 1. The same methodology is applied (two interpreters using calibrated and tabulated data). The non-calibrated (tabulated) values are far from the calibrated values. They depict a correction approximately ranging from 2 to 2.5 cm. On the other hand, the calibrated values range between approximately 1 to 0.5 cm. Values from expanded uncertainty range from 1.8 to 2.8 cm. In this case, tabulated

ð4Þ

u2A ¼

10 1 X ðxi  xm Þ2 10 i¼1

ð5Þ

u2B ¼

1 1 2 2 ðb  aÞ þ R 12 12

ð6Þ

where uA (type A uncertainty) is the standard deviation of ten measurements performed in a certain point of the pavement thickness, xi is a single measurement of the pavement thickness, xm the average of ten measurements of pavement thickness, uB (type B uncertainty) the standard deviation of two rectangular distribution whose limits are the thickness values of the pavement layer obtained from inspector one a and two b and the resolution R of the 1 GHz antenna for the different pavement layers, respectively. This resolution is the theoretical value obtained as one quarter of the wavelength. Accuracy of a measurement system is the degree of closeness of measurements of a quantity to that quantity’s true value xTV. This value is parameterized using the correction term C.

C ¼ xTV  xm

ð7Þ

There is a relation between correction and error E as follows:

C ¼ E

ð8Þ

M. Solla et al. / Measurement 46 (2013) 3032–3040

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Fig. 7. Correction and uncertainty results for the three sections versus thickness of the layers.

values are in general worse than calibrated values. The calibrated values are all around 2 cm, while the tabulated values approximately range between 1.9 and 2.8 cm. Fig. 6 shows the correction and uncertainty results for the intermediate layer (A) and the surface and intermediate courses (B) in Section 2. We applied the same methodology as in the previous cases. Correction results show again better results for the calibrated option. Correction in case A ranges between 0.3 and 1.3 cm, while in the non-calibrated case ranges from 1 to 0.5 cm. Correction in case B ranges between 1.5 and 1.5 cm for the calibrated case and between 2.3 and 0.3 cm for the tabulated case. Uncertainty values appear between 1.8 and 2.1 cm (A) and from 1.8 to 2.4 cm (B). However, there is an intense difference between the data provided for each one of the interpreters. Data from operator 2 show lower uncertainty levels than those obtained from operator 1. These results demonstrate the clear influence of the interpretational criterion and its importance in the evaluation of the uncertainty budget. Fig. 7 depicts the results of the correction and the expanded uncertainty versus the thickness of the different layers of pavement. Thicknesses ranged between 9 and 18 cm. There is not any trend between the increasing in layer thickness and the correction or the expanded uncertainty. Our experiments were performed in zones

with layers of different materials. There is not any appreciable trend between the type of asphalt and the correction or uncertainty values.

4. Conclusions A metrology evaluation of the pavement thickness measurement using ground penetrating radar is performed in terms of correction and uncertainty for three different cases of study. Expanded uncertainty is evaluated using type A and type B probability distributions. The first one depicts the influence of repeatability and the second one the influence of reproducibility and resolution of the GPR. Reproducibility comes from the difference in the results obtained with different human operators and resolution from the wavelength of the GPR and the type of pavement. Important differences appear between the two interpreters that must be taken into account in the uncertainty evaluation. Correction in the measurements is obtained as the difference between the data provided from the study and the ground truth provided from coring. The results show as the correction is very influenced by the calibration or non-calibration of the GPR data. Calibrated results are closer to the real data. On the other

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hand, calibration or non-calibration do not affect to the expanded uncertainty calculation. The correction and expanded uncertainty are evaluated for different types of pavement and there is not any dependence with the thickness or the type of layer. Acknowledgements Authors want to give thanks to the Spanish Ministry of Economy and Competitiveness, the Spanish Centre for Technological and Industrial Development and Xunta de Galicia for the financial support given; Grant IPP055EXP44 and projects (BIA2009-08012 and IDI-20101770). References [1] I. Puente, H. González-Jorge, B. Riveiro, P. Arias, Accuracy verification of the Lynx Mobile Mapper system, Optics and Laser Technology 45 (2013) 578–586. [2] B. Yang, L. Fang, Q. Li, J. Li, Automated extraction of road markings from mobile LiDAR point clouds, Photogrammetric Engineering and Remote Sensing 784 (2012) 331–338. [3] A. Boyko, T. Funkhouser, Extracting roads from dense point clouds in large scale urban environment., ISPRS Journal of Photogrammetry and Remote Sensing 666 (2011) 2–12. [4] Q. Zou, Y. Cao, Q. Li, Q. Mao, S. Wang, CrackTree: Automatic crack detection from pavement images, Pattern Recognition Letters 33 (2012) 227–238. [5] M. Simun, T. Rukavina, Criteria for longitudinal evenness of asphaltpavement driving surfaces, Gradjevinar 6112 (2009) 1143–1152. [6] G. Morcous, E. Erdogmus, Use of Ground Penetrating Radar for Construction Quality Assurance of Concrete Pavement. Nebraska Department of Roads, University of Nebraska, Lincoln, 2009. [7] A. Loizos, C. Plati, Accuracy of ground penetrating radar hornantenna technique for sensing pavement subsurface, IEEE Sensors Journal 75 (2007) 842–850. [8] T. Saarenketo, T. Scullion, Road evaluation with ground penetrating radar, Journal of Applied Geophysics 43 (2000) 119–138. [9] K. Maser, J. Puccinelli, J.K. Amestoy, Accuracy of Ground Penetrating Radar Asphalt Thickness Data and its Impact on Pavement Rehabilitation Design, Transportation Research Board Annual Meeting, Washington, USA, 2012. [10] B.W. Rister, R.C. Graves, Investigation of the Extended Use of Ground Penetrating Radar GPR for MEasuring in-situ Material Quality

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