Samer Lahouar

In-Place Hot-Mix Asphalt Density Estimation Using Ground-Penetrating Radar

This research proposes the innovative use of ground-penetrating radar (GPR) for effectively, continuously, and rapidly estimating in-place hot-mix asphalt (HMA) density. On the basis of electromagnetic mixing theories, three candidate models were developed to determine HMAs dielectric constant, considering dielectric and volumetric properties of its three major components of HMA: air, binder, and aggregate. Laboratory tests were conducted on midsize HMA slabs (60 cm × 60 cm × 7.5 cm) to evaluate the models. After evaluating and comparing the three models, it was determined that the prediction model based on the Rayleigh mixing theory was the most accurate. The selected model was calibrated with a field core and then validated using field GPR measurements of a composite pavement with an HMA surface. The selected model provided accurate HMA density within a reasonable range.

MEASUREMENT OF VERTICAL COMPRESSIVE STRESS PULSE IN FLEXIBLE PAVEMENTS: REPRESENTATION FOR DYNAMIC LOADING TESTS

Testing at Virginia Smart Road allowed determination of the vertical compressive stress pulse induced by a moving truck and by falling weight deflectometer (FWD) loading at different locations beneath the pavement surface. Testing was performed on 12 different flexible pavement sections. Stress and temperature were measured using pressure cells and thermocouples, respectively, that had been installed during construction of the road. Target testing speeds were 8 km/h, 24 km/h, 40 km/h, and 72 km/h. The considered depths below the pavement surface were 40 mm, 190 mm, 267 mm, 419 mm, and 597 mm. A haversine or normalized bellshape equation was found to be a good representation of the measured normalized vertical compressive stress pulse for a moving vehicle. Haversine duration times varied from 0.02 s for a vehicle speed of 70 km/h at a depth of 40 mm to 1 s for a vehicle speed of 10 km/h at a depth of 597 mm. For the FWD loading, a haversine with a duration of 0.03 s was found to approximate the induced stress pulse at any depth below the pavement surface. Currently, laboratory dynamic testing on hot-mix asphalt (HMA) specimens is performed using a haversine wave at loading duration of 0.1 s. Because HMA is a viscoelastic material, the loading time affects its properties and, therefore, it is recommended that the loading time of HMA dynamic tests be reduced to 0.03 s to better match loading times obtained from moving trucks at average speed and from FWD testing.

Successful Application of Ground-Penetrating Radar for Quality Assurance-Quality Control of New Pavements

The successful application of ground-penetrating radar (GPR) as a quality assurance–quality control tool to measure the layer thicknesses of newly built pavement systems is described. A study was conducted on a newly built test section of Route 288 located near Richmond, Virginia. The test section is a three-lane, 370-m-long flexible pavement system composed of a granular base layer and three different hot-mix asphalt (HMA) lifts. GPR surveys were conducted on each lift of the HMA layers after they were constructed. To estimate the layer thicknesses, GPR data were analyzed by using simplified equations in the time domain. The accuracies of the GPR system results were checked by comparing the thicknesses predicted with the GPR to the thicknesses measured directly from a large number of cores taken from the different HMA lifts. This comparison revealed a mean thickness error of 2.9% for HMA layers ranging in thickness from 100 mm (4 in.) to 250 mm (10 in.). This error is similar to the one obtained from the direct measurement of core thickness.

APPROACH TO DETERMINING IN SITU DIELECTRIC CONSTANT OF PAVEMENTS: DEVELOPMENT AND IMPLEMENTATION AT INTERSTATE 81 IN VIRGINIA

A major problem in using ground penetrating radar (GPR) for estimating pavement layer thickness is assuming the dielectric properties of that layer. Pavement dielectric properties may vary significantly due to aggregate type, moisture presence, and other conditions. Therefore, uncertainties in the dielectric constant, which may vary from 3 to 15, will result in misleading thickness determination. Obtaining cores for calibration may reduce the error, but the variation in the dielectric constant along the roadway often leads to errors in the thickness determination. A method was developed to determine the dielectric constant, and therefore the thickness, of the hot-mix asphalt (HMA) layer of a pavement using GPR. Because of the different compositions and ages of the layers forming HMA in older pavements, dielectric constant estimation based on the surface reflection may not be accurate and may lead to wrong thickness estimations. The developed method uses a modified common midpoint technique (usually used in seismic testing) to estimate the dielectric constant, based on the reflections from a common point at the bottom of the layer. Data were collected from a 27-km portion of Interstate 81 and processed with this technique. Comparison between the thickness estimated by this method and that measured on cores extracted from the highway revealed a mean error of 6.8%.

DATA COLLECTION AND MANAGEMENT OF THE INSTRUMENTED SMART ROAD FLEXIBLE PAVEMENT SECTIONS

The flexible pavement research facility at the Virginia Smart Road consists of 12 different designs. All sections are closely monitored through a complex array of sensors located beneath the roadway embedded during construction. The environmental sensors include thermocouples for temperature measurements, time domain reflectometry probes to measure moisture content in the base layers, and resistivity probes to measure frost penetration. The dynamic sensors include pressure cells and strain gauges to measure stresses and strains, respectively, induced at different layers from truck loading. Environmental data are collected daily every 15 min for temperature, every hour for moisture, and every 6 h for frost penetration. Truck testing is performed every week with different loading configurations. The loading variables include three load levels, three wheel inflation pressures, and four different speeds. Data are managed by saving environmental data from different instruments separately using date and section number. Truck loading data are saved by test type (based on loading configuration, inflation pressure, and speed), date of test, and section number. A database is being generated for all 12 sections to study the effect of all tested variables on the different flexible pavement designs. The performance of the used instruments and collected data are presented, and the techniques used to manage the overwhelming data are discussed. In addition, based on instrumentation responses, a preliminary discussion of the load distribution in a tested pavement system, the effect of speed on pavement stress and strain responses, and the effectiveness of drainage layer are discussed.

Measuring Rebar Cover Depth in Rigid Pavements with Ground-Penetrating Radar

Ground-penetrating radar (GPR) is a nondestructive investigation tool that is usually used in flexible pavement evaluation to estimate the thicknesses of the various layers composing the pavement. GPR is also used in flexible pavements to detect subsurface distresses, such as moisture accumulation and air voids. For rigid pavements and bridge decks, GPR is used to measure the thickness of the concrete slab and detect the location of reinforcing bars (rebar). Rebar detection is typically achieved, in this case, when an experienced operator finds the rebars classic parabolic signature in the GPR data. This paper presents image-processing techniques that can be used to detect the rebar parabolic signature automatically in GPR data collected from rigid pavements with a high-frequency ground-coupled antenna. After detection of the rebar, the reflected parabolic shape is fit to a theoretical reflection model to estimate the pavements dielectric constant and the rebar depth. The algorithms were validated on GPR data collected from a known continuously reinforced concrete pavement section. The technique showed an average error of 2.6% on the estimated rebar cover depth.

Runway Instrumentation and Response Measurements

This paper presents ongoing research to measure the in situ response to airplane traffic of flexible pavement on a runway at Cagliari-Elmas Airport in Italy. Understanding how pavement materials respond to traffic and environmental loading is fundamental to designing pavements and assessing their performance. The pavement material behavior is affected by many factors (i.e., load magnitude, material properties, and environmental conditions). The influence of these factors can be simultaneously taken into account by measuring in situ stresses and strains using embedded instruments. The pavement layers of the Cagliari-Elmas runway were equipped with 149 instruments: 36 linear variable differential transformers, 36 pressure cells, four time domain reflectometers, 28 T-thermocouples, and 45 hot-mix asphalt strain gauges. The instrumented area, 55 m2, allows measuring the responses during three main loading maneuvers: taking off, landing, and taxiing. The preliminary data acquired during and after the runways construction and before its opening to airplane traffic and its analysis show that the instrumentation process was a success. The instrument response testing includes falling weight deflectometer, truck, and airplane loading of various types, magnitudes, and speeds. The collected data were successfully compared with preliminary numerical simulations. Further data collection and research will be performed, particularly involving airplane traffic data. Data analysis will include the effect of the environmental data (i.e., moisture and temperature) and airplane configuration and speed. The collected data will be used to validate advanced pavement modeling and predict pavement runway performance. In addition, data resulting from this research have the potential to support and improve runway pavement design and to improve the evaluation process for new and existing runway pavement performance and damage prediction.

Accuracy of Ground-Penetrating Radar for Estimating Rigid and Flexible Pavement Layer Thicknesses

In this paper, the accuracy of ground-penetrating radar (GPR) for estimating pavement layer thicknesses is studied on the basis of the investigation of 17 pavement sites in Virginia. The considered sites have different types of pavement systems (flexible, continuously reinforced, jointed concretes, and composite) and different ages (0 to 5 years; 10 to 15 years; older than 20 years with a surface less than 10 years; and older than 20 years with a surface older than 10 years). Because of the diversity of the test sections considered, the accuracy of the GPR thicknesses was studied for pavement age for the same type of pavement and against pavement type for sites of the same age category. For flexible pavements, the GPR thickness error was found to increase as the pavements age increased (4.4% error for pavements 0 to 5 years old versus 5.8% error for pavements older than 20 years with surfaces older than 10 years). Moreover, for the same age category, flexible pavements were found to have a relatively hig...

Part 4: Portland Cement Concrete Pavement: Measuring Rebar Cover Depth in Rigid Pavements with Ground-Penetrating Radar

Ground-penetrating radar (GPR) is a nondestructive investigation tool that is usually used in flexible pavement evaluation to estimate the thicknesses of the various layers composing the pavement. GPR is also used in flexible pavements to detect subsurface distresses, such as moisture accumulation and air voids. For rigid pavements and bridge decks, GPR is used to measure the thickness of the concrete slab and detect the location of reinforcing bars (rebar). Rebar detection is typically achieved, in this case, when an experienced operator finds the rebars classic parabolic signature in the GPR data. This paper presents image-processing techniques that can be used to detect the rebar parabolic signature automatically in GPR data collected from rigid pavements with a high-frequency ground-coupled antenna. After detection of the rebar, the reflected parabolic shape is fit to a theoretical reflection model to estimate the pavements dielectric constant and the rebar depth. The algorithms were validated on GP...

GROUND-PENETRATING RADAR SIGNAL MODELING TO ASSESS CONCRETE STRUCTURES

In order to better understand reflected ground-penetrating radar (GPR) signals from sound concrete and delaminated concrete structures, 5 bare concrete slabs and 9 slabs with simulated delaminations were built. Five concrete mixtures were used; all were cast at 1.5 x 1.5 x 0.127 m. Delamination was simulated by placing polystyrene plastic pieces inside slabs during casting. A method was developed to determine the complex dielectric constant of the 5 concrete mixtures. The effect of delamination was studied by comparing signals obtained from the bare concrete slabs with those obtained from slabs with embedded polystyrene pieces. It was found that modeling the reflected signals using an average complex dielectric constant over the entire radar frequency range results in waveforms comparable to the measured ones. It was also found that reflections from air voids located at 50 mm from the surface overlap with the surface reflection. The distorted shape of the reflected signal, however, serves as an indication of the void pressure.