Falling Weight Deflectometer Data Research Articles

Development of a prediction model of dynamic deflections for asphalt pavements under FWD impulse loading

Pavement deflections are critical indicators in pavement design and acceptance programs. Falling weight deflectometer (FWD) technique has been widely adopted to measure the dynamic deflections in pavement engineering. Although the dynamic effect of FWD impulse loading on deflections is significant, FWD deflections are still commonly interpreted and examined using the static analysis programs in acceptance projects because of their ease of implementation and high computational efficiency. This may lead to an inability to effectively control the construction quality of pavements. Accordingly, this study aims to propose a simple and accurate approach for converting static deflection to dynamic deflection. Layered elastic theory (LET) and spectral element method (SEM) were adopted to generate the theoretical static and dynamic deflection databases, respectively. Based on the databases, a simple approach is proposed to predict the dynamic deflection only using four static deflections. The proposed approach shows lower computational workload and higher accuracy than other traditional formula forms which include equivalent layer moduli and thicknesses. In the field engineering cases, the calculated static deflections can be 2 ∼ 3 times higher than the measured ones, while the predicted dynamic deflections from the proposed approach match the theoretical dynamic deflections well and are comparable to the measured deflections for both asphalt pavements with asphalt binder or inorganic binder bases and granular bases. This indicates that dynamic deflections can more effectively control the construction quality of asphalt pavements in the FWD deflection-based design program. Meanwhile, the drawbacks of traditional prediction methods are investigated and evaluated. The proposed approach exhibits simpler form and higher accuracy, while avoiding the drawbacks of traditional methods. Combining with the common static LET-based programs, the proposed approach provides a more reasonable and practical way for the construction quality acceptance of asphalt pavements using FWD data.

RNN-based pavement moduli prediction for flexible pavement design enhancement

In order to facilitate the effective implementation of the MEPDG, researchers concentrate on quantifying local material properties, with a particular emphasis on pavement layer moduli. The layer modulus is a critical parameter necessary for calculating pavement responses (stress, strain, and deflections) resulting from traffic loading. Accurately determining the layer modulus is crucial for enhancing pavement design as it directly impacts the required pavement layer thicknesses and associated costs. Backcalculation is a commonly used method for analyzing Falling Weight Deflectometer (FWD) data to determine pavement layer moduli, with Artificial Neural Networks (ANNs) being the traditional choice. However, ANNs have limitations in terms of convergence accuracy and generalization capability. The aim of this study is to improve the backcalculation of layer moduli to enhance pavement design. By utilizing FWD data, Recurrent Neural Network (RNN) was employed to address the limitations of conventional ANN. Both ANN and RNN networks were developed and trained using identical properties. The findings demonstrate that RNN achieved faster convergence and higher convergence accuracy compared to ANN. The RNN network generated reasonable and precise layer moduli values, exhibiting a determination coefficient (R) of 0.95 in comparison to the measured values, while the ANN network had an R-value of 0.79. The results indicate that the RNN network can learn the continuity pattern between deflection basin points, thereby enhancing the accuracy of FWD backcalculation.

An Approach for Adjusting the Laboratory-Determined Dynamic Modulus Master Curve of Asphalt Layers Based on Falling Weight Deflectometer Measurements

Structural assessment is critical for designing asphalt pavement overlays, estimating the remaining life of pavements, and selecting an appropriate rehabilitation strategy for existing pavements. The falling weight deflectometer (FWD) serves as the primary nondestructive test used for evaluating the in situ properties and structural capacity of asphalt pavements. The current procedure involves analyzing the FWD response and estimating layer moduli by assuming an elastic response. However, the response of asphalt layers is viscoelastic (i.e., temperature- and frequency-dependent). This study proposes an approach that combines FWD data with laboratory measurements of the dynamic moduli of field cores to determine the in situ viscoelastic properties of asphalt layers. This approach is implemented by analyzing FWD data from four pavement sections in Qatar. Furthermore, the paper includes a comparative analysis of the response of pavement sections in which the asphalt layers are modeled using dynamic modulus master curves obtained from laboratory tests and those obtained using the approach presented in this study. It was found that using the laboratory-based master curves overestimated pavement performance (i.e., underestimated pavement distresses). It is recommended to use the dynamic moduli from the method presented in this paper for a more accurate estimation of pavement response and performance.

Falling weight deflectometer dispersion curve method for pavement modulus calculation.

The falling weight deflectometer (FWD) test is a common non-destructive testing method for evaluating the structural capacity of pavements. At present, data processing of the FWD test mainly focuses on the deflection data, while paying less attention to the deflection-time history. Because a FWD is equipped with impulse loads and geophones, which allow for the generation and capture of surface wave signal propagation, it is hypothesized that Rayleigh wave dispersion theory can be applied to calculate the modulus profile along the pavement depth by analysing the dispersive properties of the deflection signal measured during FWD tests. To test this hypothesis, we develop a new methodology for the FWD test and data analysis, referred to as the FWD dispersion curve method. We first introduce the concept of the new method, followed by an illustration of the procedure and the experimental set-up. Case studies on three concrete pavement segments are then presented to evaluate the effectiveness of the FWD dispersion curve method. Modifications to the existing FWD device are further recommended for the impact loading sources and signal collection process so that the modulus of a much shallower layer, such as the concrete slab and upper asphalt layers, can be obtained. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

Field Aging Characterization of Asphalt Pavement Based on the Artificial Neural Networks and Gray Relational Analysis

Accurate characterization of field aging of asphalt pavement is critical to precisely assessing its in-service performance. However, most of the traditional test/predictive methods either cannot fully capture the field aging characteristics or involve costly testing/computational efforts to ensure satisfactory prediction accuracy. To alleviate these problems, this study developed a new field aging predictive model based on artificial neural networks (ANNs) and gray relational analysis (GRA), which takes the field-aged viscosity of asphalt binder as the target predictive property. A series of influencing factors that may affect the field-aged viscosity were systematically investigated, among which the eight most significant ones were screened out for the ANN modeling through the GRA. A total of 479 data extracted from long-term pavement performance (LTPP) database were used for the training, validation, and testing of the ANN model. The calculation results showed that the predictive model developed using the ANN approach provided a high prediction accuracy with R2 value greater than 0.90. Furthermore, the falling-weight deflectometer (FWD) data collected from the database were utilized to evaluate the predictive performance of the well-trained ANN model. Consistent results were obtained between the viscosity values predicted from the ANN model and those back-calculated from the FWD data, indicating that the newly developed field aging model has the capability to accurately characterize the field aging evolution of asphalt pavement.

Use of falling weight deflectometer data to determine the effective structural number of full-depth asphalt pavements for structural condition assessment

The effective structural number (SNeff) determined from the falling weight deflectometer (FWD) has been widely used for pavement structural evaluations. In Indiana, the AASHTO 1993 method is currently used to determine SNeff, but it was found to be inaccurate for full-depth asphalt pavements. Thus, this study developed an enhanced model to more accurately estimate SNeff of full-depth asphalt pavements. The AASHTO 1993 method and the Rohde model tend to overestimate the SNeff, and the Rohde model was calibrated using the FWD deflection thresholds. Since the calibrated Rohde model requires an interpolation procedure sometimes resulting in negative deflections, a more practical method was developed using an FWD database generated by finite element analyses. New model provides SNeff values nearly identical to the calibrated Rohde model without the problem of negative deflections. Therefore, this enhanced SNeff prediction model is recommended for evaluating the structural conditions of full-depth asphalt pavements.

Mechanistic Assessment of Flexible Pavements in Wisconsin Using Traffic Speed Deflectometer

The traffic speed deflectometer (TSD) has been gaining interest for pavement management practices, as it can continuously collect deflection data at traffic speed without requiring traffic control. TSD data have been typically used to provide an estimate of the structural condition of pavements with a limited range of significant parameters considered in the analysis. To date, limited studies have been conducted in or for cold regions to consider the potential use of TSD data for pavement structural assessment. This study aimed to bridge this gap by developing a twofold approach to: (1) mechanistically characterize the structural integrity and remaining life of pavements; and (2) compare TSD and falling weight deflectometer (FWD) data collected in the field, considering the features of pavement facilities in Wisconsin. A field testing plan was developed to characterize the site-specific features of eight representative pavement sections, with a total length of about 30 mi and distinct pavement structures, truck traffic patterns, and climatic conditions. The postprocessed results underscored the significance of the TSD data and their potential for the structural evaluation of underlying layers in a multilayered pavement system when integrated with the site-specific pavement profile and truck traffic information derived from field testing. A comparison of the normalized deflections and structural parameters characterized by TSD and FWD data showed similar trends, while the average FWD-based deflections were 12%–37% higher than the TSD-based deflections.

Investigation of In Situ Strength of Various Construction and Widening Methods Utilized on Local Roads

The project goal was to develop and verify a low-cost, repeatable, nondestructive methodology to characterize the load-carrying capacity of materials used in road widening and construction when established values are not available, and to establish a range of structural coefficients and moduli for these materials. Ninety-nine test sites were selected from 68 projects in eight counties across Ohio, grouped into five clusters. These sites included 19 different widening treatments. Each site was visited, tests conducted, and specimens gathered using the following techniques: falling weight deflectometer (FWD), portable seismic property analyzer, light weight deflectometer, dynamic cone penetrometer, and coring. The data and specimens collected were used to measure layer thicknesses, moduli, effective structural numbers, and layer coefficients applicable to each treatment. At least seven analysis methods were used to obtain the numbers from the data collected. The results were plotted in box plot and cumulative frequency formats for each material and analysis method. For each material, there was a wide variability of values both within and between sections. There were many sources of variability, however a range of numbers for moduli and layer coefficients were identified for most treatments, which could be utilized by local engineering personnel to design future projects. The procedure based on Section 2.3.5 of the 1993 AASHTO pavement design guide using FWD data provided the best estimate of published layer coefficients. However, using these values in other areas cannot guarantee accuracy; truly accurate layer coefficients result from careful monitoring of test sections under controlled loads.

Case Study on the Impact of Flooding and Inundation on Pavement Performance

The experimental program was designed to discover the effectiveness of various treatments on mitigating pavement distress (mostly longitudinal cracking issues) caused by normal seasonal volumetric changes in expansive clays. Unfortunately, these objectives were compromised by subsequent flooding events. However, these flooding events offered rare opportunities to evaluate the impact of inundation on the performance of newly constructed pavement sections. Flood-induced pavement movement was monitored with cross-section surveys. Pavement condition data were obtained from regular pavement management activities conducted by the Louisiana Department of Transportation and Development’s Pavement Management System unit. The roadway pavements of test sections experienced significant movement because of the floods, but it is differential movement across the pavement that caused longitudinal cracks. The test section, with a geotextile-wrapped sand layer placed between the pavement structure and subgrade, had the best performance among all test sections with respect to longitudinal cracking; better performance was achieved when this sand layer was extended to the edge of the foreslope. This may be because the geotextile-wrapped sand layer created both a drainage system to minimize moisture fluctuations in the subgrade and a flexible mechanical system to absorb the swelling pressures from the underlying subgrade. Flood-induced structural damage was apparent by comparing falling weight deflectometer data of different test sections. Flood-induced heaves and depressions caused the ride quality of the roadway to deteriorate faster than expected. This may be because the prolonged inundation expedited the transition process of the pavement structure and materials from the newly constructed state to the natural equilibrium state.

Structural health assessment of pavement sections in the southern central United States using FWD parameters

Various Departments of Transportation in the South-Central States and elsewhere have made extensive use of the Non-Destructive Testing (NDT) surface deflection bowl data. The falling weight deflectometer (FWD) test is a popular NDT-based test used by transportation authorities to evaluate the performance of flexible pavement. Nevertheless, it is rare to develop a method for evaluating pavement sections using FWD data from all sensors. There is a constant demand for DOTs and highway agencies to have a streamlined approach that can be applied directly to their databases. This research focuses on extending and verifying the concept of previously published area ratio parameters to the pavement section of South-Central States (Arkansas, Louisiana, New Mexico, Oklahoma, and Texas) in order to properly analyze pavement performance. Simulation-based deflections are employed to strengthen deflection-based parameters and minimize the need for lengthy FWD field testing. Ninety-seven pavement sections in these states are being investigated for the implementation and validation of simplified processes that will be widely accessible to different transportation authorities in order to assess pavement problems at the network level.

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

Moisture fluctuations in pavement foundation due to environmental conditions (e.g., heavy precipitation, freeze–thaw cycles, groundwater table variation, etc.) can significantly affect pavements’ short- and long-term performance. Moisture variation in the pavement foundation is often monitored as a way to anticipate the structural capacity of pavements. Thus, the ability to monitor moisture variation through a proven non-destructive technology (NDT) such as Ground Penetrating Radar (GPR) would be beneficial for asset management by transportation agencies. This paper summarizes the Minnesota Department of Transportation’s (MnDOT) efforts at validating the use of GPR to monitor moisture in the pavement foundation through comparison with Falling-Weight Deflectometer (FWD) parameters that represent the structural condition of the pavement. The results in this paper are based on GPR, FWD and in-place moisture sensor data collected over 17 months on MnROAD instrumented test sections. The pavement test sections considered in this study included unbound aggregate bases (UAB) with virgin and recycled materials (i.e., RCA and RAP) covering a broad range of geotechnical behavior. Previous efforts established a strong correlation between GPR-based moisture measurements and sensor-based moisture measurements. This correlation is further validated by investigating the relationship between GPR-based moisture measurements and FWD-based indices for structural capacity. The observed correlation is reasonable and in good agreement with the expected correlation between volumetric moisture content (VMC) of the base layer and the structural capacity of a pavement. This agreement validates the use of GPR to monitor moisture in pavement foundation. GPR is not a replacement for FWD, but it can be used in asset management efforts in addition to FWD to assess moisture fluctuation in pavement foundation during and after extreme environmental events (e.g., flooding, freeze-thawing) to determine when and where FWD testing may be necessary.

Surface layer modulus prediction of asphalt pavement based on LTPP database and machine learning for Mechanical-Empirical rehabilitation design applications

Evaluating the modulus of the existing asphalt concrete (AC) layer is a critical procedure in Mechanical-Empirical (ME) rehabilitation analysis. Generally, the modulus could be back-calculated by the Falling Weight Deflectometer (FWD) test. However, the raw FWD data of each pavement section is not always readily prepared for local highway agencies. To address this issue, the main objective of this study is to establish a reliable model by machine learning (ML) methods to predict AC layer modulus for the existing flexible pavement with data readily available from the local pavement management system, which could be an auxiliary tool for network-level sections with no FWD tests. The long-term pavement performance (LTPP) database was used to collect the original data for model training and testing, including pavement structures, service age, climate records, and pavement distresses. After preliminary data processing, matrix correlation analysis, and feature selection, the prepared dataset (total data points = 6477) with 14 predictors was fed into three regression models, including Ordinary Least-Squares regression (OLS), Random Forest regression (RF), and Gradient Boosting regression Method (GBM). The related key hyperparameters were optimized by grid search and 5-folds cross-validation. By comparison, the GBM model was finally selected due to its considerably higher prediction accuracy (R2 = 0.7921) than RF model (R2 = 0.7525) and OLS model (R2 = 0.4371) in the test set. According to the variable importance given by GBM model, surface temperature and AC layer thickness are more dominant variables in modulus estimation. In addition, a case study with predicted AC layer moduli in ME rehabilitation design was provided to verify the model application. In summary, the trained GBM model can be utilized to predict AC layer modulus for pavement evaluation and then ME rehabilitation when FWD data is not available.

ANN Model to Predict Joint Stiffness of White-topped Pavements Using Falling Weight Deflectometer (FWD) Data

ANN Model to Predict Joint Stiffness of White-topped Pavements Using Falling Weight Deflectometer (FWD) Data

Moving Wheel versus Impact Deflections and Their Use in Pavement Evaluation

Traffic speed deflection devices (TSDDs) have been developed since around 2000 to allow for safe and efficient structural evaluation of highway networks. One barrier to TSDD implementation is the inherent differences in deflections produced by moving truck loads and by falling weight deflectometer (FWD), the current deflection testing standard. To better understand the differences in data produced by the two devices, FHWA sponsored research into one particular TSDD, the rolling wheel deflectometer (RWD). The study utilized the finite layer program ViscoWave to model both FWD and RWD loads to demonstrate the effect of their inherent differences on pavement deflections and other simulated parameters. In addition, ViscoWave was used to generate theoretical FWD and RWD deflections for a diverse set of pavement structures and subgrade conditions. The resulting deflections were used to develop correlations between the two devices, which were validated with side-by-side FWD and RWD field tests performed on 23 sites. The research determined that the differences between FWD and RWD deflections vary depending on pavement factors and loading characteristics. The two devices produced similar deflections on thicker, stiffer, lower-deflection pavements, while the FWD produced relatively higher deflections on thinner, weaker, higher-deflection pavements. Therefore, use of common FWD data analysis programs will produce different results, such as layer moduli, for TSDD devices. Advanced analysis routines capable of modeling the TSDD’s moving load and loading configurations are needed.

Performance analysis of cement treated base layer by incorporating reclaimed asphalt pavement material and chemical stabilizer

Milling of an existing pavement after it's service life engenders massive production of reclaimed asphalt pavement material (RAPM), which may cause disposal issues. The current study highlights the sustainable valorization of RAPM in the cement-treated base (CTB) layer by incorporating chemical stabilizers by the full-depth reclamation (FDR) method. CTB mix was prepared in the laboratory by using 100% RAPM with varying cement and chemical admixture content. In order to identify the effect of different content of cement and chemical stabilizer on the laboratory measured different engineering properties of CTB mix were evaluated. Furthermore, to impart more insights on the laboratory-designed CTB mix under actual traffic conditions, the test track was also laid and being monitored by the durability properties of fields sample and falling weight deflectometer (FWD) analysis. Four cement contents of 3.5, 4.0, 4.5, and 5 percent and five chemical stabilizer contents of 0, 3.0, 3.5, 4.0, and 4.5 percent by weight of cement were added. From obtained data, it can be concluded that mix prepared with 4.5% cement and 4% chemical stabilizer provides the best result in terms of strength parameters and durability properties of studied samples. FWD data advocates that constructed pavement with a designed CTB layer imparts low fatigue behavior as compared with the performance criteria prescribed in Indian Roads Congress (IRC) guidelines. Our study accentuated that 100% recycling of RAPM in FDR technology can be a viable solution as the comprehensive cost analysis showed a 45% cost reduction for the proposed mix by incorporating studied materials.

Application of a hybrid neural network structure for FWD backcalculation based on LTPP database

ABSTRACT The road layer modulus backcalculation based on the road deflection basin obtained by the Falling Weight Deflectometer is a key issue in road engineering. Traditional Falling Weight Deflectometer backcalculation method based on Artificial Neural Network has the disadvantages of poor generalisation ability and low convergence accuracy in terms of the dynamic modulus. In this paper, a hybrid neural network structure, combined with Residual Neural Network, Recurrent Neural Network and Wide & Deep (Abbreviated as ResRNN–W&D) structure, was proposed for Falling Weight Deflectometer deflection basin backcalculation. A case study using the United States Long-Term Pavement Performance database verified that the ResRNN–W&D structure can train Falling Weight Deflectometer data on multiple roads together and achieve fast and high-precision convergence, thereby greatly improving the availability of the multi-source heterogeneous data. Moreover, two transfer learning methods for the ResRNN–W&D structure were proposed to improve the divergence issue. It was found that the ResRNN–W&D structure has stronger generalisation ability than traditional Artificial Neural Network.

Evaluation of the effects of transverse cracking on the falling weight deflectometer data of asphalt pavements

ABSTRACT This study aims to investigate the effects of transverse cracking on the deflections of asphalt pavements. 3D finite element models were developed to simulate various intact and cracked asphalt pavements. The deflections of intact and cracked pavements under falling weight deflectometer (FWD) loading were computed and compared. In most cases analyzed, the deflections of cracked pavements are greater than those of intact pavements. For pavements with thermal cracking, the deflections can be 12% and 25% greater than those of intact pavements for cement treated base (CTB) and granular base (GB), respectively. For pavements with reflective cracking, the deflections of cracked pavements can be 98% greater than those of intact pavements, and the deflection decreases sharply across the reflective crack and in some cases it can even increase with increasing distance from the loading center. Crack location and width mainly affect the deflections on the loaded side of crack. Both thermal and reflective cracking have greater influences on the deflections of pavement with a thicker AC layer and a weaker subgrade. The effects of reflective cracking on the deflections are significantly greater than thermal cracking. LTE30–60cm is recommended as a deflection index to distinguish reflective cracking from thermal cracking.

An attempt for development of pavements temperature prediction models based on remote sensing data and artificial neural network

ABSTRACT Results of Falling Weight Deflectometer (FWD) test are dependent upon pavement temperature and its changes daily or seasonally. It is crucial to have the temperature of pavement when FWD data are collected, and also to compare the FWD data for different temperatures. Conventional models for pavement temperature rely on parameters such as air temperature, solar radiation, wind speed, and humidity. However, the accuracy of these data might not be high when the pavement is far from meteorological stations. Thus, this study is investigating the applicability of using remote sensing technology for the estimation of pavement temperature. Satellite input data are used to develop three models as linear, non-linear and Artificial Neural Network (ANN) for depths of 20 and 25 cm and in different layers of asphalt pavement. The predicted temperatures are very close to the measured temperature(correlation coefficients 0.79–0.99). Moreover, the results of this model are compared with the BELLS3 model for Chosen sites in Raton Pass, Aguilar, and Schurz-Nevada, based on the data in IOWA State University in the United States. Nonetheless, the results of this model can be extended for similar conditions with hot weather, subtropical area and low vegetation.

Dynamic Modulus Predictive Models for In-Service Asphalt Layers in Hot Climate Areas

Dynamic modulus of asphalt mixtures is one of the most important input parameters in design and rehabilitation of asphalt pavements. Dynamic modulus predictive models are alternative methods for laboratory determination of this parameter. These models were developed based on laboratory data. Due to sophisticated and laborious laboratory testing methods, there is a need to develop new models to determine dynamic moduli of in-service asphalt layers. In this study, nine different asphalt pavement sites were selected in two southern provinces of Iran. Falling weight deflectometer (FWD) testing was performed at each site and core samples were taken from the asphalt layers for volumetric analysis. Using the results, dynamic moduli of asphalt layers were determined using six predictive models, including Witczak, modified Witczak, Hirsch, Al-Khateeb, global, and simplified global models. Predicted dynamic moduli were compared with those determined from back-calculation analysis of FWD data. Existing predictive models were calibrated and new models were developed for the prediction of in situ dynamic modulus of asphalt layers in hot climate areas. Performance evaluation and validation of the new models showed high prediction accuracy and low prediction bias with very good correlations between the predicted and back-calculated values (R2=0.96).

Sensor-based pavement diagnostic using acoustic signature for moduli estimation

The diffusion of smart infrastructures for smart cities provides new opportunities for the improvement of both road infrastructure monitoring and maintenance management.Often pavement management is based on the periodic assessment of the elastic modulus of the bound layers (i.e., asphalt concrete layers) by means of traditional systems, such as Ground Penetrating Radar (GPR) and Falling Weight Deflectometer (FWD). Even if these methods are reliable, well-known, and widespread, they are quite complex, expensive, and are not able to provide updated information about the evolving structural health condition of the road pavement. Hence, more advanced, effective, and economical monitoring systems can be used to solve the problems mentioned above.Consequently, the main objective of the study presented in this paper is to present and apply an innovative solution that can be used to make smarter the road pavement monitoring. In more detail, an innovative Non-Destructive Test (NDT)-based sensing unit was used to gather the vibro-acoustic signatures of road pavements with different deterioration levels (e.g. with and without fatigue cracks) of an urban road. Meaningful features were extracted from the aforementioned acoustic signature and the correlation with the elastic modulus defined using GPR and FWD data was investigated.Results show that some of the features have a good correlation with the elastic moduli of the road section under investigation. Consequently, the innovative solution could be used to evaluate the variability of elastic modulus of the asphalt concrete layers, and to monitor with continuity the deterioration of road pavements under the traffic loads.