Backcalculation of Pavement Layer Moduli, Thickness and Stiff Layer Depth Using a Modified Newton Method

Accurate deflection readings must be obtained when using NDT devices in order to obtain correct backcalculated layer moduli. This paper provides an analysis of the sources of deflection errors and illustrates through a series of examples how random deflection errors and random pavement thickness errors affect backcalculated moduli. The consequence of these errors is most pronounced in the backcalculated modulus of the surface course. The moduli of the base and subbase layers are also affected, and the subgrade moduli are only slightly affected by these errors. To some degree this outcome may be due to the bottom-to-top approach used by the backcalculation program MODCOMP 2. The consequence of the variability of the backcalculated moduli on the required pavement overlay thicknesses was found to be minimal. This was due to the fact that, generally speaking, data errors which cause an overestimate of the base course modulus also cause an approximately equally significant, but opposite, underestimation of the surface course modulus. Thus, the needed overlay requirement remains nearly the same. The paper shows how certain FWD calibration and testing procedures can be used to minimize deflection measurement errors. The accuracy of backcalculated moduli is shown to be determinable from the RMS error of the deflection fit. The examples provide an impression of the extent to which random layer thickness errors and deflection measurement errors will influence the ability to discern point-to-point variability of layer moduli for the specific pavement structure considered in this investigation. This provides a perspective from which the practical accuracy of the backcalculation approach can be judged.

Resilient moduli of pavement layers are the basic input parameters for the design of pavements with multiple layers in the current mechanistic empirical pavement design guides. Field measurements are generally believed to provide accurate values to back-calculate pavement layer moduli, but the tests to measure the relevant parameters are expensive, difficult to perform, and cause disturbances to the public. Therefore, the back-calculation of pavement layer moduli from laboratory scale model testing has been a focus of recent pavement research. This paper presents a back-calculation analysis to evaluate pavement layer moduli by using a three-dimensional numerical model developed using the FLAC3D finite difference software. The pertinent measurements that are required for the back-calculation analysis were collected from a cyclic load testing facility under traffic type cyclic loading conditions, with a typical pavement structure consisting of a granular road base lightly stabilized with cement–fly ...

This paper presents a backcalculation method for pavement layer elastic modulus and thickness. The effect of deflection measurement errors on the backcalculated results is also considered. The falling weight deflectometer (FWD) data are generated by applying a load to the pavement while calculating deflection at various fixed distances from the load centre. The measurement errors in FWD data are simulated by perturbing the theoretical deflections. Using these data, a backcalculation technique based on the improved genetic algorithm is proposed. In order to deal with the measurement errors, besides the common root mean square, a new objective function called area value with correction factor is introduced to the backcalculation algorithm. Numerical examples for two- and four-layer pavement structures are presented, which show the capability of the proposed method in backcalculation of pavement layer modulus and thickness.

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

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.

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

Recent studies have concluded that measured surface deflections can be used as a low-cost pavement monitoring and condition assessment tool to determine remaining structural life and pavement performance. Moving load devices are being used more often to measure continuous surface deflections. They are considered a faster alternative to falling weight deflectometer–based structural condition evaluation applications. The objective of the study was to compare the analytical dynamic simulation of slow-moving deflection measurements with field data. The surface pavement deflections and the pavement structural responses generated by the Euroconsult Curviametro loading at the Minnesota Department of Transportation’s MnROAD facility near Maplewood, Minnesota, were used in the evaluations. Four geophones were embedded near the pavement surface to measure surface deflections during field trials at each of three tested MnROAD cells. In addition, numerous other sensors, such as strain gauges and thermocouple trees, were available at the MnROAD facility. The 3D-Move program was used in the simulations because it can accommodate moving loads and the viscoelastic properties of pavement layers and produce continuous deflection basins. The viscoelastic properties of pavement layers were estimated on the basis of the actual temperatures at the time of the field trials and the appropriate loading frequency of the Curviametro. The proposed dynamic analytical model provided a good match with a variety of independent pavement responses, which included surface deflection basins (measured by embedded geophone sensors) as well as horizontal strains at the bottom of the asphalt concrete layers (measured by MnROAD sensors).

Sound pavement design is important for improving pavement performance, but construction is equally critical. Because of variations in pavement construction, the as-constructed pavement layer thickness varies spatially within a pavement section, and the mean constructed pavement layer thickness often deviates from the designed values. The as-constructed pavement layer thicknesses are compared to their design values by using data from the newly constructed flexible pavement sections in the Long-Term Pavement Performance program. First, the distribution type of the mean layer thickness deviation was investigated, and a combined statistical test for skewness and kurtosis showed that the mean thickness deviations may be assumed to be normally distributed for a given layer type and target thickness. Typical thickness deviation summary statistics values were derived to estimate the extent of deviations from the design thicknesses. Second, the analysis showed that elevation thickness data differ from the core examination data. Furthermore, as-constructed layer thickness values were compared to their design values by looking into the percentage of the individual measurements falling into specified ranges from the design values and by using two-sided and one-sided t-tests to compare the mean constructed thicknesses and the design values. The analyses showed that the mean constructed layer thicknesses tend to be above the design value for the thinner layers and below the design value for the thicker layers for the same layer and material type. Results from the study will be useful as inputs for reliability-based design procedures.

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.

Permanent deformation in asphalt pavement is a function of material properties, loading, environmental conditions, and structural design (e.g., thickness of pavement layers). Because of the large number of effective variables and their nonlinear interrelationships, it is not easy to develop a predictive model for permanent deformation. In this study, a laboratory database containing accumulated strain values (output) and material properties (inputs) from several asphalt pavements has been used to develop a predictive model for permanent deformation. We first show that the inputs are highly correlated, then principal component analysis (PCA) is used to compute a set of orthogonal pseudo-inputs. Two predictive models based on the pseudo-inputs were developed using linear regression analysis and artificial neural networks (ANN) and are compared using statistical analysis. Extrapolation using empirical predictive models is highly risky and discouraged by experienced practitioners, so to guard against extrapolation, a method is developed to determine an input hyper-space. The above-developed model, along with an n-dimensional hyper-space, provides sufficient information for supporting an optimization algorithm for finding the minimum accumulated strain. An asphalt pavement design with accumulated strain value of 1772 micro-strain (0.02 in. in a 10-in.-thick asphalt pavement layer) is obtained by solving the optimization problem and the design parameters meet flexible pavement design specifications. The proposed framework is able to generate accurate predictive models when the original inputs are highly correlated and able to map to an optimal point in the fitted input space.

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 error in modulus backcalculation is a crucial component in validating the rationality and reliability of results for engineering applications. The objective of this study is to identify the theoretical limitations associated with backcalculated modulus errors under typical parameter uncertainties and to determine the primary factors contributing to these errors. Firstly, using the actual measurements or data from the Long-Term Pavement Performance (LTPP) project, the statistical distributions of errors for typical parameters in the modulus backcalculation model were determined. Subsequently, a factor level table for orthogonal experimental design was developed, leading to the construction of 81 orthogonal design experimental schemes and their corresponding theoretical pavement structure models based on the actual error distributions. The deflection responses of 81 theoretical pavement structure models were then computed using an ABAQUS finite element batch analysis method devised in Python. Furthermore, a multi-parameter error model for modulus was established using multiple linear regression and variance analysis. Finally, the theoretical limitations of modulus errors under actual errors were analyzed. The results show that the errors of thickness, load amplitude and load frequency follow a normal distribution, while the distribution of backcalculated modulus errors follows an approximate mixed Gaussian distribution. When the errors of multiple parameters are combined randomly, the modulus errors range from −100% to 595%, and the probability of the modulus errors being less than 15% is highest in the asphalt surface layer, followed by the subgrade, and then the base and subbase layers. Within the same error range, the modulus error is random. However, with different error ranges, the overall level of modulus error increases in proportion to the size of those ranges. Compared to factors such as thickness, load amplitude, and load frequency, the errors in deflections have a highly contribution rate on the modulus errors exceeding 99%.

This paper deals with experience acquired in Argentina in nondestructive testing of pavements and back calculation of moduli from 1967 to 1987 by the research group of the IMAE (Institute of Applied Mechanics and Structures). National University of Rosario. The paper addresses three fundamental areas of engineering research concerning nondestructive testing (NDT): 1. The in situ measurement of material strengths using various NDT devices. 2. The measurement of resilient modulus of soils and granular materials, and dynamic moduli of bound surface materials. 3. Back calculation of moduli using various analytical models on microcomputers. In situ nondestructive testing measurements have included surface wave propagation methods, the Lacroix deflectograph, and a specially fabricated double Benkelman beam which allows the easy calculation of the deflection basin. These measurements have been made on many different types of cross-sections over numerous roadbed soils. Nondestructive laboratory measurements to determine the material strengths have included: ultrasonic pulse, resonance method, resilient moduli by dynamic compression and triaxial test, and dynamic moduli by indirect tension testing. Development efforts have focused on modifying and adapting the ALIZE III elastic-layered program developed by Laboratoire Central des Ponts et Chaussées (LCPC) in France for local conditions in Argentina. Modifications have been made for the unique local soils, climate, and traffic patterns. The program has been adapted for use on handheld notebook microcomputers.