Falling weight deflectometer dispersion curve method for pavement modulus calculation.

A new algorithm for predicting layer moduli using measurements from both falling weight deflectometer (FWD) and surface wave tests is presented. This algorithm employs numerical solutions of a multilayered half-space based on Hankel transforms as a forward model and an artificial neural network (ANN) for the inversion process. Phase velocities for frequencies ranging from 10 Hz to 10,000 Hz are calculated using the forward model for varying pavement structures with a range of layer moduli and thicknesses. These phase velocities, along with the layer moduli and thicknesses, are used to train an ANN to backcalculate layer moduli from dispersion curves (i.e., phase velocity versus frequency curves) constructed from the FWD and stress wave test data. To account for the effect of bedrock on the moduli prediction, another network is trained with layer thicknesses and phase velocities for predicting the depth to bedrock. Combining this network with the network for the moduli prediction results in a sequential dispersion analysis technique in which the depth to bedrock predicted from the first network becomes an input to the second network for predicting layer moduli. FWD and stress wave test measurements from an intact pavement and an asphalt overlay over cracked asphalt layer are processed using the sequential dispersion analysis technique and MODULUS 5.0 backcalculation program. Comparison of the results indicates that the dispersion analysis technique yields less variable subgrade moduli and is more sensitive to changes in the asphalt surface layer, because the high-frequency data from the stress wave test is incorporated.

Using Falling Weight Deflectometer (FWD) and Ground Penetrating Radar (GPR) to monitor the effects of seasonal moisture variation on the structural capacity of pavements

Seasonal and diurnal variations can significantly impact falling weight deflectometer (FWD) measurements and, thus, deflection-based parameters such as back-calculated layer moduli. Therefore, it is necessary to evaluate and quantify these effects. The back-calculated layer moduli data from the Long-Term Pavement Performance Seasonal Monitoring Program study were analyzed within each climatic region. The effects of seasonal and diurnal changes were quantified and related to ambient temperatures were related to the development of general measurement guidelines. The HMA layer moduli showed minimal variation during the spring and fall seasons in every climatic region and were consistently higher based on the deflections measured before noon. However, temperature correction, generally applied to the HMA layer moduli, can eliminate the temperature impacts of the FWD measurements. Because the unbound layer moduli are back-calculated from the single measured deflection basins on the surface, temperature and moisture conditions at the time of measurements can affect the material properties. The base and subgrade moduli showed minimal variation within the temperature ranges of the spring and fall seasons. Therefore, such temperature within each climatic region may result in layer moduli values closest to their representative in-field conditions. In addition, the unbound layer showed no effect of time within a day, suggesting that FWD testing can be conducted at any time during the day. Based on the results, the preferred temperature ranges for FWD measurements are 55–70 °F and 65–75 °F in the freeze and non-freeze regions, respectively. FWD measurements can be conducted at any time during the day. The preferred seasons for testing are spring and fall in all climatic regions.

Falling weight deflectometer (FWD) data are generally processed through backcalculation software for computing layer modulus of airport pavements. Though a number of backcalculation software are currently available, it is not known that software produces the most consistent and accurate results. Inconsistent and inaccurate backcalculation software can take a significant toll on the overlay and rehabilitation design of airport pavements as modulus being the inputs for such design calculations. Most backcalculation software requires an initial value of modulus, known as seed modulus, to initiate simulations. Unfortunately, in many of the software, the outputs vary with the initial choice of seed modulus (consistency). In addition, depending on the software algorithm, the final output differs from the laboratory measured value of modulus (accuracy). In this study, three backcalculation software programs are evaluated for consistency and accuracy. They are BAKFAA, MODULUS and EVERCALC. The major goal is to determine the most consistent and accurate software for determining layer modulus of airport pavements in New Mexico. Consistency of software is determined by comparing outputs from three sets of input data from FWD tests at identical locations with three magnitudes of loads: 40·03, 53·38 and 71·17 kN. Laboratory tests are conducted for modulus and tensile strength of asphalt and soil samples collected from the same locations of FWD tests. Software accuracy is determined based on the distribution and magnitude of the variance of laboratory and FWD modulus. Laboratory tensile stress is also compared with the backcalculated tensile stress using KENLAYER, a multilayer elastic analysis software. Study results show that EVERCALC software produces more consistent and accurate modulus values than the BAKFAA and MODULUS software. Therefore, EVERCALC is recommended for analyzing layer modulus of airport pavements.

A condition assessment procedure for pavement layers that uses multiload-level falling weight deflectometer (FWD) deflections is presented. A dynamic finite element program that incorporates a stress-dependent soil model was used to generate the synthetic deflection database. On the basis of the data in this database, the relationships between surface deflections and critical pavement responses, such as the stresses and strains in each layer, have been established. The FWD deflection data, distress survey results, temperature, and laboratory testing results used to develop this procedure were collected from the Long-Term Pavement Performance project database. Research efforts also focused on the effect of the FWD load level on the condition assessment procedure. The results indicate that the proposed procedure can estimate the asphalt concrete (AC), base, and subgrade layer conditions. The AC layer modulus and the tensile strain at the bottom of the AC layer were found to be better indicators of the condition of the AC layer than the deflection basin parameter. It was also found that the structurally adjusted base damage index and base curvature index were good indicators for prediction of the stiffness characteristics of the aggregate base and subgrade, respectively. An FWD test with a load of 71.2 kN or less does not improve the accuracy of this procedure. The results from the study of the nonlinear behavior of a pavement structure indicate that the deflection ratio obtained from multiload-level deflections can predict the type and quality of the base and subgrade materials.

Dynamic Soil Models for Backcalculation of Material Properties from Falling Weight Deflectometer Deflection Data

Falling Weight Deflectometer (FWD) testing is used for nondestructive structural evaluation of existing pavements. The deflection data from FWD can be used for estimation of layer moduli by an iterative technique. In the calculation process, commonly known as backcalculation, comparison is done between calculated deflections from a suitable theory and those measured by the FWD. The estimated moduli values correspond to the best fit of the measured deflection basin. In the present study, a nonlinear optimization technique has been used to estimate the layer moduli in the backcalculation process. The backcalculation scheme developed is applicable to 3-layer to 6-layer pavements. The method was verified by simulated deflection data from elastic-layer theory and FWD-deflection measurements on 13 in-service pavements in Arizona. They were found to approximate closely the manual approach of matching deflection basins. The algorithm has been coded in a computer program called the Arizona Deflection Analysis Method (ADAM).

This paper presents a simple method to determine dynamic modulus master curve of asphalt layers by con­ducting Falling Weight Deflectometer (FWD) for use in mechanistic-empirical rehabilitation. Ten new and rehabilitated in-service asphalt pavements with different physical characteristics were selected in Khuzestan and Kerman provinces in south of Iran. FWD testing was conducted on these pavements and core samples were taken. Witczak prediction model was used to predict dynamic modulus master curves from mix volumetric properties as well as the bitumen viscosity characteristics. Adjustments were made using FWD results and the in-situ dynamic modulus master curves were ob­tained. In order to evaluate the efficiency of the proposed method, the results were compared with those obtained by us­ing the developed procedure of the state-of-the-practice, Mechanistic-Empirical Pavement Design Guide (MEPDG). Re­sults showed the proposed method has several advantages over MEPDG including: (1) simplicity in directly constructing in-situ dynamic modulus master curve; (2) developing in-situ master curve in the same trend with the main predicted one; (3) covering the large differences between in-situ and predicted master curve in high frequencies; and (4) the value obtained for the in-situ dynamic modulus is the same as the value measured by the FWD for a corresponding frequency.

Decisions regarding the selection and implementation of appropriate pavement rehabilitation methods is usually based on pavement functional and structural condition data. Visual distress surveys and Falling Weight Deflectometer (FWD) testing are often carried out by agencies as parts of their pavement preservation programmes. Although backcalculation of individual layer moduli from FWD data is a common approach to assess a pavement’s structural condition, the accuracy of this approach is largely dependent on exact estimates of individual layer thicknesses. Coring operations to determine pavement layer thicknesses require significant time and resource commitments, and hence cannot always be accommodated within an agencies’ operational constraints. Accordingly, alternative analysis methods to assess the pavement’s structural condition from FWD data are often desired. An ongoing research study at Boise State University is focusing on combined usage of pavement structural and functional evaluation data for making pavement rehabilitation decisions. Considering the lack of pavement layer thickness information for all locations, this study is using Deflection Basin Parameters (DBPs) calculated from FWD test data to make inferences regarding the structural condition of individual pavement layers. This manuscript presents findings from this study, and establishes DBPs as reasonable alternatives to be used in network-level pavement condition evaluation practices. The adequacy of DBPs to assess the structural condition of individual pavement layers was first assessed through Finite-Element Modelling. A series of analyses were performed by assigning typical modulus values to individual pavement layers, and the corresponding DBPs were calculated. The calculated DBP values mostly fell within typical ranges specified in the literature for different layer conditions. Once the DBPs were established as adequate alternatives for making network-level pavement assessment decisions, four selected pavement sections in the state of Idaho were analyzed based on this method, and the results were compared against those obtained from visual distress assessment routines.

A pavement temperature profile is indispensable for analyzing and interpreting falling weight deflectometer (FWD) testing data. This paper presents a simple analytical approach to rapidly predicting the transient temperature profile within the asphalt layer of a flexible pavement on the basis of measured surface temperatures during FWD testing. The proposed analytical approach consists of the classical solution of a one-dimensional heat equation in a homogeneous half-space subjected to specified surface temperatures and a Gaussian-quadrature numerical scheme to approximate the classical solution. Model validation was conducted using a large temperature database. Comparisons between predicted and measured pavement subsurface temperatures suggest that the proposed simplified analytical method generated a reasonable asphalt temperature profile during the short period of FWD testing. The main advantage of the proposed method is that it can be easily applied to assist field engineers in rapidly estimating the temperature profile within the asphalt layer of a multilayered flexible pavement based on measured pavement surface temperatures during FWD testing.

Seasonal and diurnal variations can significantly impact Falling Weight Deflectometer (FWD) measurements and, thus, any deflection-based parameter such as slab elastic modulus (Epcc ), modulus of subgrade reaction (k), and load transfer efficiency (LTE). Also, general guidelines for the FWD measurements are needed to help agencies better interpret the derived parameters. Long-Term Pavement Performance (LTPP) Seasonal Monitoring Program (SMP) study data for Jointed Plain Concrete Pavements (JPCP) were analyzed. The results showed that seasonal and diurnal changes impact the derived parameters for the rigid pavements in different climatic regions. PCC slab’s upward curling and full contact with the underlying layer at lower temperatures resulted in higher Epcc and k-values and vice versa. Trends seen for k-values closely followed those for moduli values. Therefore, any guidelines concerning FWD testing should consider PCC layer temperature, which affects its shape at testing time. Also, LTE was found higher at elevated temperatures due to locked joints. Relating testing temperatures with observed trends of parameters helped determine general guidelines for FWD testing. The preferred temperature ranges for FWD testing are 55 to 70°F and 60 to 75ᵒF in freeze and non-freeze regions respectively; testing before noon is recommended in spring and fall seasons in all climates.

Backcalculation analysis of pavement layer moduli is typically conducted based on falling weight deflectometer (FWD) deflection measurements; however, the stationary nature of the FWD requires lane closure and traffic control. In recent years, traffic speed deflection devices such as the traffic speed deflectometer (TSD), which can continuously measure pavement surface deflections at traffic speed, have been introduced. In this study, a mechanistic-based approach was developed to convert TSD deflection measurements into the equivalent FWD deflections. The proposed approach uses 3D-Move software to calculate the theoretical deflection bowls corresponding to FWD and TSD loading configurations. Since 3D-Move requires the definition of the constitutive behaviors of the pavement layers, cores were extracted from 13 sections in Louisiana and were tested in the laboratory to estimate the dynamic complex modulus of asphalt concrete. The 3D-Move generated deflection bowls were validated with field TSD and FWD data with acceptable accuracy. A parametric study was then conducted using the validated 3D-Move model; the parametric study consisted of simulating pavement designs with varying thicknesses and material properties and their corresponding FWD and TSD surface deflections were calculated. The results obtained from the parametric study were then incorporated into a Windows-based software application, which uses artificial neural network as the regression algorithm to convert TSD deflections to their corresponding FWD deflections. This conversion would allow backcalculation of layer moduli using TSD-measured deflections, as equivalent FWD deflections can be used with readily available tools to backcalculate the layer moduli.

This paper presents the results of a detailed comparison conducted by the authors between two falling weight deflectometers, the Dynatest Model 8000 and the KUAB two-mass falling weight deflectometer (2M-FWD). The Dynatest Model FWD has been operating in the United States for several years. It is based upon the principal of dropping a mass on a single buffer system connected to a rigid plate resting on the pavement surface. The 2M-FWD is a newly improved version of the KUAB falling weight deflectometer which recently has been introduced into the United States. It is based upon the principal of dropping a mass on a double buffer system separated by a second mass which is connected to a segmented plate resting on the pavement surface. The addition of the second mass and buffer results in a consistent half sine curve shaped load pulse with a longer duration of loading. The two devices also differ in the type of sensor used to measure the surface deflection during loading. The Dynatest device uses geophones which measure deflection up to 2 mm, while the 2M-FWD uses LVDTs which are capable of measuring surface deflections up to 5 mm. Other hardware differences between the two devices also are discussed in the paper. A testing program was initiated to obtain comparative deflection data from a variety of pavement types including flexible and rigid pavements over weak and strong foundations. In addition to the comparison of actual deflection data, layer moduli are computed and compared for each device. Cores from representative test locations were obtained to verify in-field pavement thicknesses.

In Surat, India, a total of 15 full-scale test sections were constructed to examine the performance of geosynthetic-reinforced flexible pavement. The impact of different factors, including the types of geosynthetics, their characteristics, and their placement locations, on the in-situ modulus of the pavement layer was assessed through Falling Weight Deflectometer (FWD) testing. The Modulus Improvement Factors (MIF) established from the three-year field study were found between 1.30 and 1.60 for the geocell-reinforcement, and Layer Coefficient Ratios (LCR) were found between 1.27 and 1.40 for the geogrid-reinforcement. FWD testing revealed an increasing trend in average base layer modulus values in February 2021 compared to initial readings from November 2019, indicating improved stiffness due to traffic compaction and reinforcement effects. However, a slight reduction in modulus was observed in July 2022, attributed to seasonal moisture infiltration from pre-monsoon rainfall. Additionally, deflection basin parameters were analyzed to assess the structural benefits of geosynthetic reinforcement.

The article presents the research results of the temperature influence on the flexural bowl on the road surface determined by falling weight deflectometer testing. The work relevance is conditioned by the high temperature effect of asphalt concrete on test results and the need to take it into account by bringing the results obtained at the actual temperature to a comparable form with the design data, when performing diagnostics of operating highways.Experiments identify the temperature factors that have an impact on the flexural bowl parameters. Temperature dependences are suggested for the flexural bowl on different road surfaces. A falling weight deflectometer is used to simulate the load produced by a moving truck, equipped with deflection meters located at the load center and various distances from it. Together with flexure bowl measurements, the temperatures of air, pavement and asphalt concrete at various depth is determined and dependences of these parameters are plotted.The paper identifies factors that have the greatest influence on the flexural bowl parameters. The results make it possible to bring flexural bowls into a form comparable with design values and subsequently use them to determine the overall bearing capacity of the pavement as well as to assess the condition of individual structural layers, thereby increasing the reliability of repair measures.