Backcalculation of pavement layer elastic modulus and thickness with measurement errors

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.

Planning the thickness of pavement structure is one part of the highway engineering case which aims to provide services for the traffic flow in providing thesense of security and comfort for the road users. The road of Tadulako street has significant prospects for economic activities, social, education and so on. The road is one access to the New Biromaru Market, The State Senior High School of Biromaru I, The Irrigation networks of Gumbasa, Bags of production infrastructure and the distribution of agricultural products, thus it requiring the further planning for development. The purpose of this study was to determine the composition and thickness of pavement layers on the road of Tadulako Street, Village of Lolu, Regency of Sigi by comparing the thickness of Asphalt Concrete (Laston) pavement structureand the thickness of the Penetration Layer (Lapen) pavement structure on the same value of CBR. The usefulness of this research is for consideration by the district governments in planning the road of Tadulako Street, Village of Lolu, Regency of Sigi development, especially for the writer himself, he will be able to plan the road pavement thickness. The method used in this research is a method of component analysis (SNI 1732-1989-F). The conclusion of this study is that the pavement structure of penetration layer (Lapen) is D1=7,5 cm D2=15 cm D3=27 cm, whereas laston is D1=5 cm D2=15 cm D3=20 cm. Penetration layer (lapen) is thicker sub base than the Asphalt Concrete (laston) but lapen cheaper than laston, caused mobilization, design and AMP production laston it was so expensive While volume only ± 730 m. Caused the writer recommends lapen more then logic at Tadulako Lolu street District Government Sigi. Keywords : Pavement Thickness Planning, Component Analysis Method, ISO-1732-1989-F

The objective of this work was to evaluate the feasibility and value of expanding the MDT's Ground Penetrating Radar (GPR) program to pavement design and rehabilitation, and to network level evaluation. Phase I of this project concluded that in order to investigate the feasibility and value of these program expansions, a Phase II field evaluation project be designed and implemented to evaluate the accuracy of GPR pavement thickness data on Montana pavements, and to correlate these findings with the accuracy requirements of the individual applications. This field evaluation began with identifying 26 pavement test section of different composition and structure located throughout the diverse climatic regions of Montana. At each site, FWD and GPR data was collected, followed by coring and augering to determine the thickness of the pavement layer structure and base moisture content. This testing was carried out both in the spring of 2010 and in the fall of 2010 to capture seasonal variations. The GPR data was analyzed for thickness, and the GPR thickness data was evaluated for seasonal changes, and compared to core and plan data to investigate thickness accuracy and the effectiveness of calibration methods. Compared to cores, the average GPR bound layer thickness error was 10.3% vs. 15.2% using plan data. A GPR data checking method was developed using FWD and plan data to identify potential analysis layer analysis inconsistencies and suggest alternative interpretation. Implementation of this method reduced the GPR error to 7.6%. A sensitivity study was carried out to investigate the impact of having the more accurate GPR data. This study showed that, on average, the use of GPR reduced the pavement life prediction error by 62% when compared to using as-built plan data.

Flexible pavement utilises asphalt as a binding agent. The pavement layer transmits and distributes the weight from the road or flexible pavement uniformly onto the compacted subgrade. Damage to flexible pavement results from a confluence of multiple connected factors. The purpose of the study is to ascertain the thickness of flexible pavement layers and the various forms of flexible pavement. This research employs the 2017 Bina Marga approach, utilising predicted LHR data over the design life or LHRT, to ascertain the kind and thickness of flexible pavement layers, considering the CESA4 and CESA5 values. The 2044 LHRT value for Passenger Cars and Other Light Vehicles is 33,358.7; Class 5A is 6.2; Class 5B is 4.1; Class 6A is 17.1; Class 6B is 39.1; and Class 8 or non-motorized vehicles is 280.1. The CESA4 value derived from the LHRT value was 4021566.47, whereas the CESA5 value was 469755.421. The most preferred type and thickness of flexible pavement is asphalt with a granular foundation layer, namely AC – WC pavement type at 4 cm thickness, AC – BC at 6 cm thickness, and LFA Class A at 40 cm thickness.

Automatic asphalt layer interface detection and thickness determination from ground-penetrating radar data

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.

The primary objective of this research was to develop a model to accurately predict the modulus of flexible pavement layers from surface deflections measured using the falling weight deflectometer (FWD) device. To do so, a synthetic dataset consisting of 10,000 flexible pavements was created using the layered elastic theory. The developed dataset contained the moduli values for different pavement sections, and deflections at known distances from the load center. Next, the moduli of different asphalt pavement layers consisting of a surface course, base course, and subgrade were calculated using the Artificial Neural Network (ANN) methodology through backcalculation. The inputs for the neural network are thicknesses and deflection values at seven distances from the load center. The outputs are the moduli for different layers. The optimum neural network consists of two hidden layers and has a general architecture of 9-36-18-3. The transfer function is sigmoid for hidden layers and linear for the output layer. Results indicate that the ANN model can predict the modulus of different layers accurately with a coefficient of determination (R2) of more than 0.999 in all cases. For validation, the results from the developed model were compared with typical backcalculation software of ISSEM4, MODCOMP, MODULUS, WESDEF, and BAKFAA as well as 386 LTPP sections. Moreover, an analysis was conducted to assess the contribution of inputs in moduli prediction for each of the pavement layers using the Garson algorithm and the Connection Weight methods. It was concluded that the results from the latter method are in better agreement with the theories. Finally, the significance of different input variables was assessed using an indirect method by excluding each from the analysis and checking of the model predictability power. It was concluded that in order of significance, the base layer thickness, 1st geophone deflection, and the AC layer thickness are the variables that their exclusion causes large errors.

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.

The University of Florida, Electronic Communications Laboratory (ECL), as part of a project with the Air Force Research Laboratory (AFRL), is investigating the utility of ground penetrating radar (GPR) for airfield evaluations. It is expected that the GPR and the results of current research will assist the Air Force in assessing airfields with less coring and allow more rapid and accurate airfield evaluations by providing continuous estimates of the pavement and subsurface conditions. AFRL has conducted airfield measurements using a high-resolution GPR mounted on a test van. Using time domain data from a pair of ground-coupled antennas, one in bistatic mode and the other in monostatic mode, the thickness of pavement and other subsurface layers can be estimated. In order for GPR to be useful for airfield evaluations, practical considerations such as automated data processing capability and efficient data analysis procedures have also been addressed. This paper will describe novel approaches and signal processing techniques developed for detecting layer interface boundaries and estimating the layer properties using GPR. In addition, the software tools developed to facilitate airfield evaluations will be discussed. Finally, surface layer thickness estimates will be compared to measured cores from a recent airfield assessment.

The Falling Weight Deflectometer (FWD) is one of the most important non-destructive tests (NDT) used for pavement structural evaluation based on deflection measurements. Inverse analysis of FWD measurements—back-calculation—is a common process for obtaining the pavement layer moduli, including the subgrade soil, which are useful for estimating remaining pavement life for overlay design purposes. Based on a proficiency test (PT) involving testing devices from different manufacturers, the objective of this study is to analyze the influence of FWD uncertainty on the back-calculated flexible pavement and foundation layers moduli. A parametric study was performed considering the back-analysis of different asphalt pavements with regard to layer thickness and resilient modulus (unbound granular and asphalt concrete layers, including subgrade). It was concluded, in general, that FWD uncertainty could have an important influence on the layers modulus obtained from back-calculations. This influence was less significant in the case of asphalt concrete layers (±25%). The variation in resilient modulus was greater in the layers of the subgrade soil (−33 to +83%) and the unbound granular materials (−42 to +80%).

Accurate structural evaluation of flexible pavements is important for cost-efficient maintenance and rehabilitation (M&R) strategies. Traditional back-calculation methods based on Falling Weight Deflectometer (FWD) data assume elastic behavior, overlooking the viscoelastic nature of asphalt materials and subsequently leading to inaccuracies. This study introduces a mechanistic-based back-calculation technique to estimate the dynamic modulus (E*) master curve directly from FWD time-history data. By integrating layered elastic theory with viscoelastic principles, the proposed method first determines the relaxation modulus, E(t), using Differential-Time Fourier Transform (DTFT) analysis. This time-domain response is then converted into the frequency domain to construct the E* master curve. Validation against laboratory-measured dynamic modulus values from field-cored specimens demonstrates that the proposed technique generates highly accurate E* master curves for all asphalt layers. Moreover, the findings highlighted the importance of accurate pavement structural information, such as layer thickness and Poisson’s ratio, for achieving reliable E* master curve.

Pavement deflection data are often used to evaluate a pavement’s structural condition nondestructively. Pavement layers are important parameters in view of bearing capacity. Pavement layer thickness may be known from the design project or site investigation. At the same time, using backcalculation analysis, flexible pavement layer thicknesses together with in situ material properties can also be backcalculated from the measured field data through appropriate analysis techniques. Data mining (DM) process has not been used as a backcalculation tool before. In this study, DM process is used in backcalculating the pavement layer thickness from deflections measured on the surface of the flexible pavements. Experimental deflection data groups from NDT are used to show the capability of the DM process in backcalculating the pavement layer thickness and compared each other. Performing the study, modeling with Kstar method gives fine results with respect to other DM modeling techniques. Backcalculation of pavement layer thickness using DM process has been carried out for the first time.

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.

Abstrack Roads Pangelesran – Leuwiliang has a length of 2,800 km and is located in Parakanlima Village, Cikembar District, Sukabumi Regency. The problem that arises in this road section is the poor condition of this road, plus this road is an alternative road to Jampang Tengah and is the road to a tourist attraction, namely Situ Cipiit. With the traffic volume that increases every year on the Pangleseran - Leuwiliang Road, it is necessary to re-plan the thickness of the flexible pavement layer, so to determine the re-planning of the flexible pavement thickness, secondary data and primary data are needed. For primary data collection, namely conducting DCP testing to determine the CBR value of the subgrade and calculating the LHR field survey, as for collecting primary data, namely rainfall data. As for the calculation method that will be used to determine the thickness of this flexible pavement is the component analysis method. From the results of planning the thickness of this flexible pavement, it was obtained for the surface layer using laston with a thickness of 10 cm, for the upper foundation layer using class A crushed stone aggregate with a thickness of 20 cm and for the lower foundation layer using class B gravel with a thickness of 31 cm with a design life of 10 year

In order to improve the performance of the bridge deck pavement structure, improve the stress state of the bridge deck pavement structure layer, reduce the disease of old concrete bridge asphalt pavement, grasp the stress state of the bridge deck pavement structure layer and material performance, the asphalt pavement material and pavement structure are optimized. Based on the finite element analysis method, the finite element model of asphalt pavement on cement concrete bridge deck is established. The interlaminar shear stress, interlaminar tensile stress, and surface shear stress are selected as the main mechanical control indicators, and the stress variation rule of different thickness and modulus of pavement layer under traffic load is analyzed. The result shows that (1) shear failure is the main failure form of asphalt pavement on bridge deck, and the thickness of asphalt pavement is the main factor affecting the stress distribution inside the pavement, while the modulus of asphalt pavement material has a little effect on the stress distribution. The proper thickness of asphalt pavement can improve the distribution of internal stress, therefore, two kinds of bridge deck pavement structure design schemes, SMA composite structure and AC composite structure are proposed. (2) Based on the indoor test, the high-temperature stability, low-temperature crack resistance, water stability and water permeability of SMA-10, SMA-13 and Ac-13 asphalt mixtures are studied, it is known that all performance indicators of the three asphalt mixtures meet the requirements of the design code, but each has its own advantages and disadvantages in different performance. (3) Finally, according to the results of the performance test and the principle of optimizing the material of the bridge deck pavement, under the condition that the performance can meet the requirements of the project, combined with the requirements of the project cost economy and the safety of the bridge structure, a composite structure is recommended as the old concrete bridge deck pavement structure, AC-13 is selected as the old concrete bridge deck pavement upper and lower layer pavement material.