Falling Weight Deflectometer Deflection Data Research Articles

Integration of non-destructive testing methods to assess asphalt pavement thickness

The pavement engineering community has consistently drawn its attention to a broadened utilization of advanced Non-Destructive Testing (NDT) systems for pavement evaluation. Amongst these systems, the Falling Weight Deflectometer (FWD) is designed to measure surface deflections and assess the pavement structural condition, while Ground Penetrating Radar (GPR) is utilized to estimate pavement layers’ thicknesses. An integration of the capabilities of these two separate systems has been considered a powerful resource in pavement evaluation. With this in mind, the present study aims to investigate whether this integration could result in the utilization of the derived FWD deflection data to directly assess pavement layers thicknesses. For the research, an integrated regression analysis is conducted considering both GPR and FWD data with the aim to develop a relationship between GPR-estimated AC thicknesses and FWD deflection indexes. The developed relationship is calibrated considering both GPR and core thicknesses and further validated yielding an equivalent and satisfactory performance, with thickness prediction errors around 10%. This finding produces evidence in support of the statement that deflection data is capable of roughly producing pavement layer thicknesses. Such an approach could suggest a practical and cost-effective pavement evaluation tool, when the procurement and transportation logistics of implementing multiple and expensive equipment may be a critical issue.

Assessment of modulus using falling weight deflectometer and cores for stabilized layer

There are conventional methods available for designing of flexible pavement based on the past experience whereas for the design of Cement Treated Base (CTB) and Cement Treated Sub Base (CTSB) layers no such conventional methods are available. Use of cement stabilization in granular material is an effective technique to increase the stiffness of base and sub base layers and also in the thickness reduction in comparison to conventional layers. Furthermore, cement base layers can improve the fatigue behaviour of asphalt surface layer and subgrade rutting over the short and long term. In our country, there is no performance data available for CTB and CTSB layers in in-situ conditions. This paper deals with the investigation of the resilient modulus (MR) of stabilized pavement layers through field data collection on in-service flexible pavement sections which may be useful for design purpose. The main objective of this research study is to make a comparison of MR values for cement stabilized layers using non-destructive method viz. Falling Weight Deflectometer (FWD) and destructives test method through cores. The field data has been collected from State Highway (SH) and Major District Road (MDR) around Lucknow, Uttar Pradesh. No visual distress was observed on both the road sections at the time of field surveys. Two study sections on SH and one on MDR of about 500 m length has been identified for the study.The in-situ cores were obtained from CTB and CTSB layers which were tested for determination of MR values through Unconfined Compressive Strength (UCS) as per IRC:37-2018. FWD deflection data was used for determination of MR values using backcalculation software for CTB and CTSB layers. Furthermore, the thickness and the properties of each layer were determined by conducting test pits on the existing pavement.

Mechanistic-Empirical design of perpetual pavement

Perpetual pavements use multiple, durable asphalt layers to produce a safe, smooth, long-lasting road. The pavement design begins with a strong, yet flexible bottom layer that resists tensile strain caused by traffic, and thus stops cracks from forming at the bottom of the pavement. Four perpetual pavement sections were designed and constructed in 2005 and currently are scheduled for rehabilitation for top-down surface cracking. These cracks have been confirmed by full-depth cores. This study revisited the design of these perpetual pavement sections using now available PerRoad and AASHTOWare Pavement ME Design software to investigate whether the design assumptions were correct. Fatigue lives of these sections were also evaluated based on bottom-up cracking. Layer moduli were backcalculated from the falling weight deflectometer (FWD) deflection data using the EVERCALC software. Tensile strains at the bottom of the asphalt layer were then computed. Results showed that predicted strain values derived from the mechanistic response models were lower than the tensile strains computed using moduli from the FWD deflection data. The required asphalt thickness from PerRoad was relatively higher than the asphalt thickness obtained from the AASHTOWare Pavement ME Design software over a 50-year design period. The study identified the most successful perpetual pavement design as the one with a rich bituminous mix in the base layer.

Development of a Structural Capacity Prediction Model Based on Traffic Speed Deflectometer Measurements

The importance of incorporating pavement structural conditions in the selection of maintenance and rehabilitation strategies along with functional indices has been recognized by state agencies. To measure in-service pavement structural capacity, surface deflection under a defined load has been typically used. The traffic speed deflectometer (TSD) has emerged as a continuous deflection-measuring device as it operates at traffic speed and reduces lane closure and user delays. The present study developed a nonlinear regression model to predict pavement structural number (SN) based on surface deflections measured by the TSD along with the total pavement layer thickness and traffic volume. The proposed model was successfully developed and validated with SN calculated based on TSD and falling weight deflectometer deflection data obtained from two testing programs in Louisiana and Idaho. The model was further validated with respect to its prediction of in-service structural capacity loss and deficiency. Based on statistical measures and the model’s ability in identifying structurally deficient sections, results showed satisfactory accuracy of the model and supports its use for network-level decision-making processes in the pavement management system.

A new method to determine maintenance and repair activities at network-level pavement management using falling weight deflectometer

Pavement condition assessment at network level requires structural evaluation that can be achieved using Falling Weight Deflectometer (FWD). Upon analysing FWD data, appropriate maintenance and repair methods (preser­vation, rehabilitation or reconstruction) could be assigned to various pavement sections. In this study, Structural Condi­tion Index (SCI), defined as the ratio of Effective Structural Number (SNeff) to Required Structural Number (SNreq), was used to determine if a pavement requires preservation or rehabilitation works (i.e. preservation SCI > 1, rehabilitation SCI < 1). In addition to FWD deflection data, SCI calculation requires pavement layer thicknesses that is obtained using GPR with elaborated and time consuming works. In order to reduce field data collection and analysis time at network-level pavement management, SCI values were calculated without having knowledge of pavement layer thicknesses. Two regression models were developed based on several thousand FWD deflection data to calculate SNeff of pavements and resilient modulus (MR) of their subgrades. Subgrades MR values together with traffic data were then used to calculate SNreq. Statistical analysis of deflection data indicated that Area under Pavement Profile (AUPP) and the deflection at distance of 60 cm from load center (D60) parameters showed to have strong correlation with SNeff and MR respectively. The determination coefficients of the two developed models were greater than those of previous models reported in the literature. The significant result of this study was to calculate SNeff and MR using the same deflection data. Finally, imple­mentation of the developed method was described in determining appropriate Maintenance and Repair (M&R) method at network level pavement management system.

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

Falling Weight Deflectometer (FWD) test is one of the most widely used methods for in-situ nondestructive evaluation of pavement/soil properties and examination of the structural condition of in-service pavements. A dynamic half-space model is employed in the present work for backcalculation of engineering soil properties from the FWD data. The advantages, limitations and the reliability of the backcalculation elastic moduli evaluated from dynamic soil model are discussed in this paper. Selected numerical results are presented to portray the influence of governing parameters, for example, the presence of shallow stiff layer and the mass density of soil material on dynamic backcalculation of the soil elastic modulus. The investigation presented in this study provides a better understanding of dynamic backcalculation processes which is essential for the development of dynamic backcalculation program and its applications.

Performance Evaluation of Highway Embankment Constructed from Tire-Derived Aggregate Using Falling Weight Deflectometer Tests

To evaluate the performance of tire-derived aggregate (TDA) for use as highway embankment fill material, a test embankment was constructed in Edmonton, Alberta, Canada. The test embankment was 80 m in length and contained four different test sections, each 20 m in length. The four test sections consisted of embankment material made from passenger and light truck tires (PLTT), off-the-road (OTR) truck tires, TDA-soil mixture, and soil without TDA. The performance of the test embankment was evaluated using falling weight deflectometer (FWD) tests following the placement of each pavement layers (subgrade, base course, and asphalt). FWD deflection data measured at different times for a year, and backcalculated moduli and deflection basin parameters, such as maximum deflection (Do), base layer index (BLI), middle layer index (MLI), and lower layer index (LLI) computed from the deflection data suggest that a TDA fill with a design similar to that used in this study would perform satisfactorily as highway fill.

Systematic back-calculation protocol and prediction of resilient modulus for MEPDG

A research focusing on the characterisation of representative local material properties was conducted to facilitate the full implementation of the Mechanistic-Empirical Pavement Design Guide for roadway designs in Wyoming. As part of the test program, falling weight deflectometer deflection data were collected from 25 test sites in Wyoming for back-calculation of subgrade resilient modulus. Also, subgrade materials from these test sites were sampled for laboratory resilient modulus measurement in accordance with the AASHTO T 307. The back-calculation is a user-dependent procedure and produces a non-unique resilient modulus estimation. To alleviate this limitation, this paper focuses on the recent development of a systematic back-calculation protocol for subgrade resilient modulus using MODCOMP6 software. The protocol is intended for use on a flexible pavement with a crushed base. The proposed procedure discusses pre-analysis checks, seed modulus adjustment, pavement structure adjustment and program termination criteria. A correlation study was conducted to correct back-calculated resilient modulus to laboratory-equivalent values. The results conclude that a non-zero intercept linear regression model provides a better correlation than the widely used zero intercept linear regression model. Furthermore, better correlations are achieved when the back-calculated resilient modulus of a lower subgrade layer and resilient modulus measured at higher laboratory test sequences Nos. 11 to 15 are considered. The non-zero model based on Mr test sequence No. 14 and lower subgrade layer yields the best correlation. For the zero model, a C-factor of 0.645 based on Mr test sequence No. 15 and lower subgrade layer yields the best correlation.

Noise-tolerant inverse analysis models for nondestructive evaluation of transportation infrastructure systems using neural networks

The need to rapidly and cost-effectively evaluate the present condition of pavement infrastructure is a critical issue concerning the deterioration of ageing transportation infrastructure all around the world. Nondestructive testing (NDT) and evaluation methods are well-suited for characterising materials and determining structural integrity of pavement systems. The falling weight deflectometer (FWD) is a NDT equipment used to assess the structural condition of highway and airfield pavement systems and to determine the moduli of pavement layers. This involves static or dynamic inverse analysis (referred to as backcalculation) of FWD deflection profiles in the pavement surface under a simulated truck load. The main objective of this study was to employ biologically inspired computational systems to develop robust pavement layer moduli backcalculation algorithms that can tolerate noise or inaccuracies in the FWD deflection data collected in the field. Artificial neural systems, also known as artificial neural networks (ANNs), are valuable computational intelligence tools that are increasingly being used to solve resource-intensive complex engineering problems. Unlike the linear elastic layered theory commonly used in pavement layer backcalculation, non-linear unbound aggregate base and subgrade soil response models were used in an axisymmetric finite element structural analysis programme to generate synthetic database for training and testing the ANN models. In order to develop more robust networks that can tolerate the noisy or inaccurate pavement deflection patterns in the NDT data, several network architectures were trained with varying levels of noise in them. The trained ANN models were capable of rapidly predicting the pavement layer moduli and critical pavement responses (tensile strains at the bottom of the asphalt concrete layer, compressive strains on top of the subgrade layer and the deviator stresses on top of the subgrade layer), and also pavement surface deflections with very low average errors comparable with those obtained directly from the finite element analyses.

Network-level flexible pavement structural evaluation

The Kansas Department of Transportation has a comprehensive pavement management system known as network optimisation system (NOS). Annual condition surveys are conducted for NOS. Currently, the structural number (SN) of flexible pavements is computed using the American association of state highway and transportation officials equation based on the centre and fifth sensor deflections of a falling weight deflectometer (FWD). However, a rolling wheel deflectometer (RWD) can be used to collect deflection data at the network-level. This study was conducted to see whether the SN of flexible pavements can be obtained from this RWD deflection and NOS condition survey results. In this study, FWD deflection data, collected from 1998 to 2006, were analysed. Multiple regression analysis was done. The results showed that there is a negative relationship between SN and centre deflection. Equations can be used to calculate SN based on FWD (or RWD) centre deflections and network-level condition survey results. The SN is more sensitive to centre deflection than the total pavement thickness.

Effect of training algorithms on neural networks aided pavement diagnosis

Routine pavement maintenance necessitates present structural diagnosis and condition evaluation of pavements by employing non-destructive test equipment such as the Falling Weight Deflectometer (FWD). FWD testing of pavements involves measuring time-domain surface deflections resulting from applied impulse loading on the pavement. Through inverse analysis of FWD deflection data, the stiffness parameters of the individual pavement layers are, in general, determined using iterative optimization routines. In recent years, Neural Networks (NN) aided inverse analysis has emerged as a successful alternative for predicting pavement layer moduli from FWD deflection data in real-time. Especially, the use of Finite Element (FE) based pavement modeling results for training the NN aided inverse analysis is considered to be accurate in realistically characterizing the non-linear stress-sensitive response of underlying pavement layers in real-time. Efficient NN learning algorithms have been developed and proposed to determine the weights of the network, according to the data of the computational task to be performed. In this paper, the effect of training algorithms on the NN aided inversion process is analyzed and discussed. Keywords: Neural networks; Non-destructive testing; Inverse analysis; Finite element; Flexible pavement.

FINITE ELEMENT BASED ADAPTIVE NEURO‐FUZZY INFERENCE TECHNIQUE FOR PARAMETER IDENTIFICATION OF MULTI‐LAYERED TRANSPORTATION STRUCTURES

During the service life of a pavement, it is often required to conduct Non-destructive tests (NDTs) to evaluate its structural condition and bearing capacity and to detect damage resulting from the repeated traffic and environmental loading. Among several currently used NDT methods, the Falling Weight Deflectometer (FWD) is the most commonly used pavement NDT method applied by many transportation agencies all over the world. Non-destructive testing of pavements using FWD is typically accompanied by the prediction of the Young’s modulus of each layer of the pavement structure through an inverse analysis of the acquired FWD deflection data. The predicted pavement layer modulus is both an indicator of the structural condition of the layer as well as a required input for conducting mechanistic-based pavement structural analysis and design. Numerous methodologies have been proposed for backcalculating the mechanical properties of pavement structures from NDT data. This paper discusses the development of an Adaptive-Network-based Fuzzy Inference System (ANFIS) combined with Finite Element Modeling (FEM) for the inverse analysis of the multi-layered flexible pavement structures subjected to dynamic loading.

Soil Stiffness Evaluation for Compaction Control of Cohesionless Embankments

Abstract Mechanistic pavement design procedures based on elastic layer theory require characterization of pavement layer materials including subgrade soil. This paper discusses the subgrade stiffness measurements obtained from a new compaction roller for compaction control on highway embankment projects in Kansas. Three test sections were compacted using a single, smooth steel drum intelligent compaction (IC) roller that compacts and simultaneously, measures stiffness values of the compacted soil. Traditional compaction control measurements such as, density, in-situ moisture content, stiffness measurements using a soil stiffness gage, surface deflection tests using the light falling weight deflectometer (LFWD) and falling weight deflectometer (FWD), and penetration tests using a dynamic cone penetrometer (DCP), were also done. The results show that the IC roller was able to identify the locations of lower soil stiffness in the spatial direction. Thus, an IC roller can be used in proof rolling. IC roller stiffness showed sensitivity to the field moisture content indicating that moisture control during compaction is critical. No universal correlation was observed among the IC roller stiffness, soil gage stiffness, back-calculated subgrade moduli from the LFWD and FWD deflection data, and the California bearing ratio obtained from DCP tests. The discrepancy seems to arise from the fact that different pieces of equipment were capturing response from different volumes of soil on the same test section.

Trends in Deflection with Application of Repeated Loads

Falling weight deflectometer (FWD) testing is now considered routine for the evaluation of pavement structures. Averaging of FWD load and deflection data collected at the same location and similar drop loads is common before further analysis. The stated reason for this averaging is to decrease the effect of the random error inherent in FWD deflection sensors. Implicit in this methodology is the assumption that there is no significant trend in deflection results with successive load applications. FWD data from the Long-Term Pavement Performance program were analyzed to determine whether a significant trend exists between deflection and drop number. In a majority of data sets, a statistically significant trend does exist for at least one deflection sensor. The trends are most common for the highest load level and the center deflection sensor. These trends are less often practically significant but are common enough that averaging deflection data can be expected to increase total error in a large minority of cases.

Neural Network-Based Approach for Analysis of Rigid Pavement Systems Using Deflection Data

This paper focuses on the development of backcalculation models based on artificial neural networks (ANNs) for predicting the layer moduli of the jointed plain concrete pavements, that is, the elastic modulus of the portland cement concrete (PCC) layer and the coefficient of subgrade reaction for the pavement foundation. The ANN-based models were trained to predict the layer moduli by using the falling-weight deflectometer (FWD) deflection basin data and the thickness of the concrete pavement structure. The ISLAB2000 finite element program, extensively tested and validated for more than 20 years, has been employed as an advanced structural model for solving the responses of the rigid pavement systems and generating a knowledge database. ANN-based backcalculation models trained with the results from the ISLAB2000 solutions have been found to be viable alternatives for rapid assessment (capable of analyzing 100,000 FWD deflection profiles in a single second) of the rigid pavement systems. The trained ANN-based models are capable of predicting the concrete pavement parameters with very low (<0.4%) average absolute error values. The ANN model predictions and closed-form solutions were compared through the use of the FWD deflection data, and the results are summarized in the paper. In addition, a sensitivity study was conducted to verify the significance of the layer thicknesses and the effect of bonding between the PCC and the base layer in the backcalculation procedure. The results of this study demonstrated that the ANN-based models are capable of successfully predicting the rigid pavement layer moduli with high accuracy.

Deflectometer-based analysis of ballasted railway tracks

The falling weight deflectometer (FWD) device is used in the UK for the dynamic testing of ballasted railway track. Using the deflection data obtained from the test, elastic modulus values of the track substructure required to build a numerical model of the track can be determined using a procedure known as back-analysis. The numerical model so calibrated can be used to determine the effect of the traffic loads on the stresses, strains and deformations in the railway track system, and is an important component of an analytical approach to track substructure design. In this paper, the use of dynamic finite-element analysis to back-analyse the material properties of the railway substructure from FWD deflection data has been demonstrated by means of an example. The numerical approach presented is the rational method for FWD-based inverse analysis and condition evaluation of ballasted railway tracks, and becomes practicable owing to continued advances in finite-element and computer technologies.

Assessment of Pavement Layer Condition with Use of Multiload-Level Falling Weight Deflectometer Deflections

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.

Assessment of Pavement Layer Condition with Use of Multiload-Level Falling Weight Deflectometer Deflections

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.

It’s 10 o’Clock: Do You Know Where Your Sensors Are?

More than 400 falling weight deflectometer (FWD) devices are presently in use throughout the world, and deflection reading accuracy is very important. Deflections are measured in microns, or hundredths of a mil, and even very small errors in the deflection readings can have a profound effect on the results of backcalculation. One question that has somehow escaped scrutiny is the one alluded to in the title to this paper—exactly where along the deflection basin are the FWD deflection sensors positioned? This is an extremely important issue for proper definition of the deflection basin as a function of distance from the center of the loading plate. A review of the FWD load-deflection data in the Long Term Pavement Performance (LTPP) study found that in at least 7 percent of some 4 million lines of FWD deflection data in the National Information Management System (NIMS) database, the sensors were not positioned as reported. This problem is not limited to LTPP and NIMS, and it occurs all too frequently on FWDs everywhere. How sensor positioning errors influence backcalculated moduli, even if all other facets of the FWD data are 100 percent correct, is described. Examples of the errors found in NIMS are also presented—real-life illustrations of what can go wrong and how much influence these errors can have on pavement analysis. A method of scanning for sensor positioning errors without carrying out backcalculation is presented. By use of the suggested transform, sensor positioning errors are clearly evident when suspect data are compared with correct data along the same, or other, pavement sections.

Direct Analysis Methods for Falling Weight Deflectometer Deflection Data

Two direct methods of analyzing falling weight deflectometer deflection data are presented. One method, which involves dividing the deflection 1828.8 mm (72 in.) away from the load plate by the deflection underneath the load plate, relates directly to the ratio between the pavement modulus and subgrade modulus for a two-layer system. The other method described in the paper gives a qualitative evaluation of the stiffness of the base material as it relates to the pavement surface. Examples of using these methods are described and compared with results from the Modulus Version 5.0 computer program developed by the Texas Transportation Institute.