Thickness estimation of road pavement layers using Ground Penetrating Radar

Transcription

1 Dr. Eng. Audrey Van der Wielen Environment Concrete Roads Geotechnics & Surface Characteristics Division Belgian Road Research Centre (BRRC) Thickness estimation of road pavement layers using Ground Penetrating Radar 1

2 TABLE OF CONTENTS Introduction 3 1. Ground Penetrating Radar principle 5 2. Development of a thickness evaluation methodology 7 3. Method validation and assessment Radar speed range for the road pavement materials Speed estimation exploiting the known thickness of the layers Speed estimation with the manufacturer program Speed estimation with surface reflection equations Global comparison of the results 12 Conclusions, perspectives and acknowledgements 14 References 15 2

3 INTRODUCTION Prior to any road maintenance work, it is recommendable to perform a structural evaluation in order to estimate the residual lifetime of the structure and to select the type of rehabilitation (structural rehabilitation, inlay or overlay). In Belgium, the methodology to evaluate the road structure includes measurements performed with the FWD (falling weight deflectometer) (Figure 1 (a)) or with the curviameter (Figure 1(b)). (a) (b) Figure 1: (a) Falling Weight Deflectometer and (b) curviameter. For both methods, a controlled load is applied to the road and the local deflection is measured. Falling weight deflectometer measurements are performed at defined locations. The controlled load consists in a falling weight impacting the pavement structure. This measurement can be performed on any surface but requires a traffic interruption (Maser, 2008). With the curviameter, the load inducing the local deflection is the truck itself and the measurements are performed within the traffic (with a maximum speed of 18 km/h) (Van Geem, 2010). This method cannot be used on rigid (concrete) pavements, for which the FWD is suitable. From the deflection measurements, the mechanical properties of the different layers of the road pavement structure are estimated with a backcalculation algorithm. In a backcalculation routine, the theoretical deflection of a road presenting the same structure and average material properties is compared to the measured deflection. The road modulus is then automatically adjusted and, after a few iterations, the model converges to the actual moduli of the different layers. This estimation can only be accurate if the layer thicknesses are known with a sufficiently good precision: misestimating the thickness of one or two centimeters can be critical for the calculation. The layer thicknesses can be locally measured through coring, but it is always important in a testing campaign to limit the number of cores as much as possible. Indeed, each core is a destructive test causing local damages to the structure. It requires a temporary traffic 3

4 interruption and a subsequent repair. Moreover, a core only brings local information about the structure: the variations between the core locations can totally go unnoticed. For these reasons, the deflection measurements are more and more combined to nondestructive tests performed by ground penetrating radar (GPR) (Loizos and Papavasiliou, 2006). The GPR is a nondestructive method using electromagnetic waves to scan the underground. On the radar measurement, the interfaces between the different layers can be observed, which can allow estimating the thickness of each layer if the speed into the material is known. This speed can be estimated through a calibration with well-located cores, but it can also be deduced from the radar measurements themselves, via a comparison with calibration measurements performed over a perfect reflector (Saarenketo and Scullion, 2000). Estimating the layer thickness from radar measurements is important because it leads to a totally nondestructive road evaluation for each point at which the road deflection has been measured. The objective of this research is to determine the accuracy of totally nondestructive thickness measurements and to compare the results obtained with different methods and antennas on the same sections. In particular, we developed a measurement methodology and an algorithm for the estimation of the first layer thickness using surface reflection equations. In the first chapter of this paper, the global principles of GPR are presented. In the second one, the measurement methodology and the algorithm used for the estimation of the first layers thicknesses are detailed. This method is then evaluated in the third paragraph, in which it was applied to estimate the thicknesses of the surface layers of a trial section built in our laboratory. Those results are analyzed in the fourth and last section, in which they are compared to the results estimated with other methods. 4

5 1. GROUND PENETRATING RADAR PRINCIPLE The Ground Penetrating Radar (GPR) is a non-destructive evaluation technique using radio waves to map structures buried into the ground. Initially developed in the 60 s and 70 s for archaeological or geophysical research, it has been, since the eighties, increasingly employed for the characterisation of civil engineering structures (Annan, 2009). The GPR antenna emits short electromagnetic pulses in the direction of the ground. A portion of the energy is reflected at each interface between materials of different electromagnetic properties and travels back to the antenna. At the surface, the amplitude of the signal over time is measured. The set of all the measurement collected at the same point is called a trace, in which both the direct wave, and the waves reflected on the different interfaces can be observed (Figure 2). Figure 2: Principle of GPR measurement. When GPR measurements are performed, the antenna is generally moved along a defined line of the surface and traces are recorded with a regular spatial interval. All the scans taken together and represented as a function of their position constitute a profile (Figure 3). In a profile, the interfaces are directly observable while the local reflectors appear as hyperbolas, because they are also detected when the antenna is not exactly over them. Figure 3: Profile acquisition principle. 5

6 The frequency of GPR waves range from 100 MHz to 3 GHz. Antennas of higher frequencies present a better resolution but a lower penetration depth. For geological applications, in which penetration depth has to be quite important, frequencies of antennas are generally less than 500 MHz. For civil engineering applications, useful central frequencies can rise up to 3 GHz. Two main types of GPR antennas are distinguished: the contact antennas, placed in close proximity to the ground, and the horn antennas, suspended several tens centimetres over the surface. With ground antennas, the power of the wave transmitted into the ground is maximized. Moreover, most contact antennas are also shielded, which limits the interferences and the noise into the signal. Horn antennas are often preferred for road inspection because they can be mounted on a car (Figure 4) and perform fast measurements into the traffic (up to km/h). Another advantage of horn antennas is the fact that the reflection on the surface is clearly separated from the direct wave into the signal. This reflection can then be exploited to evaluate the properties of the surface material. Figure 4: Horn antenna mounted behind a vehicle To convert the reflection time in the GPR profile into a layer thickness, the speed of the wave into the material has to be known. If the distance between the emitter and the receiver antennas can be neglected, the depth of the target can be evaluated by the equation (1):. 2 (1) For most building materials, which can be considered as low loss and non-magnetic, the speed into the material can be estimated with a good precision as a function of the relative dielectric permittivity of the material : (2) In (2), is the speed of light and equal to 30 cm/ns (Annan, 2009). 6

7 2. DEVELOPMENT OF A THICKNESS EVALUATION METHODOLOGY From the observation of a GPR profile, the thickness variations of each layer can be observed. But the thicknesses cannot be quantitatively estimated unless the radar wave speed in each layer is known. The speed can be estimated locally by comparing the radar traces to thickness measurements obtained through coring at the same location. The location of these calibration cores can be optimized by using the radar measurements for identifying the limits of homogeneous zones into the structure. Ideally, one core should be drilled into each homogeneous zone, at a point presenting little variation. But drilling cores into a structure is not harmless: it is cost and time consuming, requires a traffic interruption and permanently damages the structure. For these reasons, the cores number should be minimized as much as possible, by using alternative techniques for the estimation of the wave speed. When the measurements are performed with horn antennas, the permittivity of the surface material, and thus its speed, can be deduced from the surface reflection. The method consists in measuring the reflection coefficient of the surface, which corresponds to the proportion of the incident wave reflected by this interface. In practice, the reflection coefficient is estimated by comparing the reflection to a reflection obtained on a perfect reflector with the same conditions. This reference reflection is obtained through a calibration measurement performed over a metallic plate with sufficient dimensions. During the calibration, the antenna is moved vertically, so that the reflection is measured for all the soil-surface distances (Figure 5 (a)). On the basis of this measurement (Figure 5 (b)), a calibration file is created by calculating the mean trace of all the signals measured from the same position (Figure 5 (c)). For each arrival time, the peak-to-peak amplitude of the perfect reflection is calculated. 0 Calibration measurement 0 Calibration file time (ns) 6 8 time (ns) reflection time (ns) (a) (b) (c) Figure 5: (a) Calibration measurement; (b) Profile measured during the calibration and (c) Resulting calibration file. 7

8 For all the traces measured on the structure to be analyzed, the amplitude of the surface reflection is estimated and compared to the one measured on the metal plate for the same arrival time ( ). The reflection coefficient is then obtained by multiplying this quotient by -1, to take into account the phase change generated by the reflection on the metal (Figure 6). Figure 6: Comparison of a measured trace to the calibration file trace presenting the same surface reflection time. On the basis of the reflection coefficient, the dielectric relative permittivity is calculated using the equation (Al-Qadi and Lahouar, 2005): 1 1. (3) In this relationship, the incidence angle of the waves with respect to the surface is assimilated to 90. This is only valid when the distance between the receiving and the emitting antennas can be neglected compared to the soil-antenna distance. The speed of the material can then be calculated using equation (2). This method can be applied to deduce the speed from every measured trace for which the soilantenna distance was considered in the calibration. For a given homogeneous section, the speed can be estimated with a local mean and the precision of its determination can be evaluated by the standard deviation. The estimated speed is only valid for the surface material. It will not be representative of the whole layer if a gradient of the properties is present (for example, if the material is drying). Moreover, if the surface layer thickness is too small compared to the wavelength, the bottom interface of this layer can be mixed in the trace with the surface reflection, and induce errors in the estimation of the speed. This method can also be extended for deeper layers, but the equations are then not exact anymore. Indeed, the results will be influenced by the wave attenuation caused by the material conductivity which cannot be calculated from the measured traces. 8

9 3. METHOD VALIDATION AND ASSESSMENT To validate the method, an indoor test trench containing four different road structures with controlled dimensions was built (Figure 7). The structures were chosen to be representative of Belgian road structures. Figure 7: Description and dimensions of the test trench. Two GSSI horn antennas were used to measure this trench, with frequencies of 1 GHz and 2 GHz. The profile measured with the 2 GHz antenna is shown in Figure 8. In this profile, the direct wave, the surface reflection and the majority of the buried interfaces are visible. In particular, the interface corresponding to the bottom of the first layer is marked with a dashed line. Figure 8: Profile of the test trench measured by the 2 GHz antenna. For each measurement, the estimation of the first layer speed obtained with the equations described in the previous section, implemented in Matlab (see Subchapter 3.4), was compared with the results obtained with the program designed by the antenna manufacturer (see Subchapter 3.3). Those results were also compared to the nominal values currently accepted for the material (see 3.1), and to the speed determined on the basis of the known thickness (see 3.2). With the 2 GHz antenna, the influence of the acquisition parameters was studied: the measurement were performed with and without the noise reduction filter provided by the antenna 9

10 manufacturer and different acquisition time ranges were used. The influence of a postprocessing stacking on the measurement results was also studied. 3.1 RADAR SPEED RANGE FOR THE ROAD PAVEMENT MATERIALS In Table 1, the range of values for the permittivity and the wave the speed into road surface materials are detailed. Table 1: Radar wave speed into road materials (ASTM, 2006, Daniels, 2004) Dielectric permittivity Speed (cm/ns) Dry asphalt Wet asphalt Dry concrete Wet concrete Even if the material and its moisture are known, the range of possible wave speeds is relatively large. 3.2 SPEED ESTIMATION EXPLOITING THE KNOWN THICKNESS OF THE LAYERS Because the layers thicknesses into the test trench are known, the speeds can be deduced from the time at which each reflection is measured in the profile (Figure 8). In asphalt, the first reflection appears about 0.64 ns after the surface reflection. If this time is necessary for a path of 8 cm into the layer (4 cm round trip), it means that the speed into the layer is equal to 12.5 cm/ns. According to the contractor of the trench, the actual layer thickness can present variations up to 0.5 cm from the nominal value. This means that the speed is situated in the range cm/ns. In concrete, the reflection appears 3.57 ns after the surface reflection. The speed should then be equal to 11.2 cm/ns. If we consider the construction dimensional tolerance, it should belong to the range cm/ns. It should be noted that the two interfaces limiting the 6 cm asphalt layer below the concrete are not totally distinguishable. This could be a source of error in the concrete speed estimation. Comparing those speeds to the general values of Table 1, it was observed that the asphalt speed is quite low for a dry material. On the other hand, the speed of 11.2 cm/ns corresponds well to a dry concrete. 10

11 3.3 SPEED ESTIMATION WITH THE MANUFACTURER PROGRAM In Radan, the program created by the manufacturer of our antenna, an infrastructure module allows calculating the speed and the thickness of each layer. The different layers must be picked out by the user into the profile (Figure 9 (a)). The speed in the first layer is automatically estimated by the program, using equations similar to (2) and (3). The speeds in the following layers are determined using approximate equations neglecting the attenuation of the wave into the material. For this reason, errors on the deeper layers thicknesses are larger. The estimated thicknesses are represented in Figure 9 (c), and the deduced interfaces depths are compared in Figure 9 (b) to their actual depth into the structure, drawn with dashed lines. Figure 9: (a) Profile of the test trench measured with the 2 GHz antenna and picking of the interfaces; (b) comparison of the interface depth calculated by Radan to their real depth (dashed) and (c) speeds estimated by Radan for the different layers. The speed estimation for the first layer oscillates between 14 and 15 cm/ns for the asphalt and between 13 and 14 cm/ns for the concrete. The deeper layers present lower speeds but more oscillations. The thicknesses deduced from these nondestructive tests are about 4.5 cm for the asphalt and 24.5 cm for the concrete (Figure 9 (b)). Those values are higher than the real thicknesses, so it is likely that the speed is slightly overestimated by the method, as well. This could be due to the method but could also result from speed variations inside the layer. 11

12 Those speeds are higher than the speeds deduced from the layer thickness (see 3.2), but the asphalt speed corresponds better to the typical range for the dry material (see 3.1). 3.4 SPEED ESTIMATION WITH SURFACE REFLECTION EQUATIONS Using the equations described in Chapter 2, the surface speed can be estimated without any commercial program. The results are described in Figure 10. Figure 10: Speed estimation for the profile measured with the 2 GHz antenna, calculated with the equations in Chapter 2. The speed estimated with the equations for top asphalt (red line in Figure 10) oscillates around the value of 14 cm/ns. This is slightly lower than the results obtained by Radan in Figure GLOBAL COMPARISON OF THE RESULTS In Figure 11, the results obtained with the nondestructive methods of Subchapters 3.3 and 3.4 on asphalt and on concrete are compared. Matlab is the program in which the equations in Chapter 2 were implemented, and Radan is the commercial software designed by the antenna manufacturer. The signals for which a noise reduction filter was applied during the acquisition are marked NR. On some signals, the influence of a preliminary signal stacking before the speed analysis was also studied. To perform a stacking, the mean of a certain number of adjacent traces is calculated. This operation is supposed to reduce the local variation of the signal. In this study, the use of a 8 traces stack was chosen. 12

13 Figure 11: Comparison of the average speeds measured on asphalt and on concrete with the different methods. From Figure 11, the following observations can be made. - Depending on the method, the mean estimated speed presents variations. Those differences can only be attributed to the method and not to a material evolution because all the measurements were performed on the same day. Moreover, except the noise reduction filter, all other treatments are applied on the same initial measurement. The order of magnitude of the variations between the mean values for different measurements is 1 cm/ns. The error committed for a 4 cm layer would then be around 3 mm (around 8 mm for a 10 cm layer). - The noise reduction filter allows to reduce the results standard deviation. On the opposite, it modifies the estimated speeds, which are systematically higher with the filter than without. This overestimation is stronger when the speed estimation is performed with the software Radan (Δ 24% than when it is calculated with the equations (Δ02%. - The use of stacking allows in almost all the cases to reduce the standard deviation of the measurements, without affecting the mean speed. - In almost all the cases, the use of the equations instead of the software leads to a lower estimated speed and a smaller standard deviation. - In the case of asphalt, the speed estimated with 1 GHz antenna is higher than with 2 GHz antenna, while the opposite is observed in the case of concrete. Comparing those values to the values estimated in Subchapter 3.2 from the known thicknesses, they are all larger and will lead to a thickness overestimation. But doubts still exist on the exact thicknesses of the layers. For example, the error committed in the asphalt section is only a few millimeters, so it would be good to perform a core to assess the thickness of the surface layers by 1 mm precision. 13

14 CONCLUSIONS, PERSPECTIVES AND ACKNOWLEDGEMENTS In this study, we wanted to evaluate on a test trench the possibility to estimate nondestructively the thickness of the road pavements materials. The results obtained with surface reflection equations were compared to the results estimated using a commercial program, for different materials and different measurements and analysis parameters. The calculated material speeds were also compared to the material speeds evaluated on the basis of the known layer thicknesses. The nondestructive results obtained by the simple equations are consistent with the estimations obtained by the help of the commercial program. The range of variation of the estimated mean speed is about 1 cm/ns. This uncertainty is still large regarding the precision required for a backcalculation after a deflection measurement but reduces considerably the variation range for a given material. It was shown that a stacking was useful for reducing the standard deviation of the results without modifying the mean estimated speed. On the other hand, the noise reduction filter also reduces the standard deviation, but it induces a slight speed overestimation. The speeds determined by nondestructive means are all higher than the speed deduced from the nominal thickness of the layer. This could result from uncertainties in the method, material inhomogeneity or uncertainties in the layer thickness. To exclude this last hypothesis, a core will be drilled in the test section. For the future, the method will be extended to lower layers. The thicknesses determined by Radan for these layers will be compared to calibration cores and equations similar to the ones described in Chapter 2 will be developed for the second and third layers, even if the expected precision is lower than for the first layer. I would like to thank the NBN (Belgian bureau for normalization), that finances this prenormalization project, my colleagues in the project, Colette Grégoire and Carl Van Geem, and the radar technician, Yves Pollet, who performed almost all the measurements for this study. 14

15 REFERENCES Al-Qadi I.L. and S. Lahouar (2005). Measuring layer thicknesses with GPR Theory to practice. In: Construction and building materials Vol.19., Issue 10, pp Annan, A.P. (2009). Electromagnetic principles of Ground Penetrating Radar. In: Ground penetrating radar theory and applications, H.M. Jol, Editor. Elsevier. pp ASTM D (2006). Standard test method for determining the thickness of bound pavement layers using short-pulse radar. Daniels, D.J. (2004). Ground penetrating radar, 2nd Edition, Volume 1. London. Institution of electrical engineers. Loizos, A. and V. Papavasiliou (2006). Evaluation of Foamed Asphalt Cold In-Place Pavement Recycling Using Nondestructive Techniques. In: J. Transp. Eng., 132(12), pp Maser, K. (2008). Automated Pavement Thickness Evaluation for FWD Backcalculation using GPR. Infrasense, Inc., presentation at the 2008 USA FWDUG. Saarenketo, T. and T. Scullion (2000). Road evaluation with ground penetrating radar. In: Journal of Applied Geophysics, Vol. 43, Issues 2 4, pp Van Geem, C. (2010). Overview of interpretation techniques based on measurement of deflections and curvature radius obtained with the Curviameter. 6 th European FWD Users Group Meeting, Sterrebeek, June