AUTOMATIC PROCESSING AND MODELING OF ...

AUTOMATIC PROCESSING AND MODELING OF GPR DATA FOR PAVEMENT THICKNESS AND PROPERTIES Gary R. Olhoeft GRORADAR P.O. Box 1520, Golden, Colorado 80402 USA [email protected], http://www.g-p-r.com Stanley S. Smith III Geo-Recovery Systems Inc. 400 Corporate Circle, Golden, Colorado 80401 USA

ABSTRACT A GSSI SIR-8 with 1 GHz air-launched horn antennas has been modified to acquire data from a moving vehicle. Algorithms have been developed to acquire the data, and to automatically calibrate, position, process, and full waveform model it without operator intervention. Vehicle suspension system bounce is automatically compensated (for varying antenna height). Multiple scans are modeled by full waveform inversion that is remarkably robust and relatively insensitive to noise. Statistical parameters and histograms are generated for the thickness and dielectric permittivity of concrete or asphalt pavements. The statistical uncertainty with which the thickness is determined is given with each thickness measurement, along with the dielectric permittivity of the pavement material and of the subgrade material at each location. Permittivities are then converted into equivalent density and water content. Typical statistical uncertainties in thickness are better than 0.4 cm in 20. cm thick pavement. On a Pentium laptop computer, the data may be processed and modeled to have cross-sectional images and computed pavement thickness displayed in real time at highway speeds. Key words: pavement, concrete, asphalt, thickness, modeling, moisture

INTRODUCTION The increase in the use of ground penetrating radar to evaluate concrete and asphalt pavement and construction may be measured in the trebling of publications in each of the past three decades. At the end of 1999, there were more than 260 papers, patents and standards. Such applications of GPR include studies of highway, runway and bridge pavements; sluices, spillways, and dams; tunnel linings and building construction (especially determination of "as builts" but also post-collapse rescue operations). Specific problems being addressed include material curing and aging, moisture determination, subgrade compaction (especially on bridge approaches),

void detection and mapping, subpavement hydrology, fracture and fault detection, delamination, swelling soils, frost heaves, karst, location and condition of rebar, bridge deck evaluation (especially concrete hidden under asphalt), and post-collapse structure evaluation and victim location. In the past few years, standards were developed for the use of radar to determine pavement thickness (ASTM 1999b) and to evaluate asphalt covered concrete bridge decks (ASTM, 1999c). Halabe (1996), Carino (1997), and Morey et al. (1998) and references therein contain recent reviews, with more current papers by Bungey et al. (1994, 1995, 1997), Buyukozturk and Rhim (1997), Carter et al. (1995), Chen et al. (1997), Chung et al. (1994), Halabe et al. (1997), Huston et al. (1999), Langenberg et al. (1997), Lytton (1995), Maser (1996a, b), Narasimharajan and Kanthan (1997), Padaratz and Forde (1995), Park and Uomoto (1997), Pitt (1992), Rhim et al. (1995), Spagnolini and Rampa (1999), Tomsett (1996), and Treybig and Godiwalla (1993).

CONCRETE AND ASPHALT PROPERTIES Concrete and asphalt are locally made materials with wide variations in constituents and properties (Neville, 1997; Derucher et al., 1998). Approximately 65 to 85 percent of concrete and 92 to 96 percent of asphalt structures are made of minerals composed of sand, gravel, crushed stone or slag called aggregates. The remainder is a binding medium such as bituminous or portland cement, macadam, mastic, mortar, or plastic. Air, water and salt fill any available pore spaces. The properties of these materials vary strongly as a function of composition, cure state and age, water content, salt chemistry, and temperature. Electrical properties vary from highly conductive (tens of ohm-m in freshly mixed concrete with high water contents or places with high wintertime road salt use) to highly resistive (thousands of ohm-m in older concretes, well cured, air dried, with low moisture content)(Neville, 1997, p. 346ff). Some are intentionally mixed with carbon to make them highly conductive (a fraction of one ohm-m; Farrar, 1978)

Figure 1: An example of an automatic fit from the full waveform modeling. On the right are the relative dielectric permittivity Cole-Cole parameters (Olhoeft, 1998) versus depth. The 0.51 m represents the height of the antenna above the ground. The 5.72 is the relative dielectric permittivity of the pavement for a thickness of 0.69-0.51 = 0.18 m, below which there is a wet clayey subgrade with a relative permittivity of 23.46. RF noise is the dominant cause of the imperfect fit.

and cannot be evaluated with radar methods. Some also contain aggregates with magnetic minerals (especially magnetite) or high iron contents (slag and ferroconcretes) and have high magnetic losses. The introduction of metal rebar also impacts electrical, electrochemical, and magnetic properties (Bungey et al., 1994). The sizes and distribution of aggregate and rebar also impact electromagnetic polarization and scattering losses. There has been a recent surge in interest in measuring electromagnetic properties of these materials (Al-Qadi et al., 1995; Bungey et al., 1997; Buyukozturk, 1996; Diefenderfer et al., 1998; Hadad and Al-Qadi, 1996, 1998; Janoo et al., 1999; Li et al., 1994; Rhim and Buyukozturk, 1998; Robert, 1998; Shang and Umana, 1999; Shang et al., 1999; Shaw, 1998; Stone, 1997; Subedi and Chatterjee, 1993; Tsui and Mathews, 1997; van Beek and Hilhorst, 1999; van Beek et al., 1998).

These have been not only for use in ground penetrating radar data interpretation but also direct enhanced curing of asphalt (Bishara and McReynolds, 1995). Typical relative dielectric permittivities lie in the range of 5 to 6 when dry and upwards toward 14 when wet for both asphalts (c.f. Shang and Umana, 1999) and concretes (c.f. Janoo et al., 1999). The major variation in both is dominantly controlled by moisture content, with concretes in general showing a larger change with increasing moisture than asphalts.

DATA ACQUISITION For this example, ground penetrating radar data are acquired using a GSSI SIR-8 with a 16-bit PCMCIA A/D digitizer card in a laptop computer, with position location provided by markers derived from the rotation of the

Figure 2: An example of a stretch of asphalt road, auomatically calibrated, processed, and modeled to statistically determine pavement thickness. The two horizontal reflectors at about 4 and 7 ns two-way travel time are the air-pavement and pavement-soil interfaces, respectively. The vertical black line in the data shows the position of the waveforms to the right, the noisier of which is the data and the smoother is the model determining permittivity and thickness (like the solid and dashed lines in Figure 1). Across the top, at that same location are shown the mean thickness and standard deviation, determined from averaging 10 scans at that location.

vehicle's drive shaft. A pair of air-launched 1 GHz GSSI 4208 antennas are suspended off the rear vehicle bumper. The SIR-8 is setup for approximately a 20 nanosecond two-way range window, calibrated by various standoff distances and the known speed of light in air. The range gain (time variable gains) settings and a time calibration are each recorded by digitizing several traces for later processing. The amplitude of the system is calibrated by placing an aluminum plate on the surface of the ground, starting the radar system recording, and driving away after several scans are recorded. This provides a record of a reflection from a near perfect reflector (metallic aluminum) against which to calibrate (the "cal scan" in Figure 1). The reflection from the air/pavement surface is smaller as only a portion is reflected, and the rest is transmitted into the material, eventually to reflect from the pavement/soil interface and yield thickness.

PROCESSING AND MODELING The recorded radar data are then calibrated for time scale (range) using the recorded time calibration file, and calibrated for time varying gain using the recorded range gain file and the recorded metal plate reflection. Traverse distance is calibrated by rubber sheeting position against markers recorded with the data from the vehicle drive shaft odometer. The data are contrast stretched to allow a better operator visual display (Figure 2). Full waveform modeling is automatically performed to determine the height of the antenna above the ground (to compensate for vehicle bounce on its suspension and consequent amplitude changes from geometric spreading), the dielectric permittivity of the pavement, the thickness of the pavement, and the dielectric permittivity of the

subgrade material. This modeling assumes no significant surface material different from the bulk volume of the pavement (like a pavement covered by a layer of water or ice), magnetic properties everywhere are those of free space, normal incidence reflections, and no significant surface or volumetric scattering (in other words, on the scale of a wavelength, the surface is smooth and the volume is homogeneous). This modeling proceeds iteratively to produce the best fit to the data with both amplitude and time-shift measures of goodness of fit. The operator can select how often the modeling occurs, and how many scans are modeled and included in a statistical average of thickness. The display output looks like Figure 1 for the full waveform modeling in detail, and like Figure 2 for the operator display output in cross section. As the modeling is performed, the output parameters and statistics of permittivity and thickness are recorded in a separate data file for later use. The permittivity is commonly used to determine moisture content using empirical formulas as in Shang et al. (1999) or Janoo et al (1999). However, a more accurate determination of moisture content uses a broader range of frequency data and determines the permittivity from the frequency dependence as in Olhoeft (2000), which also removes the ambiguity between density and water content in dielectric permittivity. This requires two antennas, the higher frequency (at or above 1 GHz) to acquire accurate pavement thickness and the lower frequency (at or below 500 MHz) to acquire accurate moisture content and density. The lower frequency antenna may also be used to evaluate the subgrade materials.

DISCUSSION All of the described processing and modeling are performed automatically by the computer in real time without operator interaction. The automatic iterative model fitting and goodness of fit criteria are adequate to deal with cellular phone tower noise commonly encountered along highways in the United States (Olhoeft, 1999a, 2000) and are more robust than those described in Lytton (1995) and van Beek et al. (1998). The statistical accuracy of the GPR pavement thickness determined as described by this method is better than that commonly encountered in ASTM D3549 by coring (ASTM, 1999a). This method (Olhoeft, 1999b) also exceeds ASTM D4748 by short-pulse radar (ASTM, 1999b, section 7) as it measures the dielectric permittivity at every location for each pavement thickness determination and also provides a quantitative statistical measure of uncertainty for each thickness determination.

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