Stiffness Of Pavement Layers Research Articles

Можливості стандартних методів випробування дорожніх одягів навантаженням

The standard methods for testing road pavements by loading have been widely applied to assess the road pavements maintenance during design, construction and survey stages and especially for the Pavement Management System purposes. These test methods were classified by the type of an applied load as static loading, cyclic loading or impulse loading tests. The paper reviews the above mentioned test methods from the aspects of their scopes, advantages, and general requirements for testing in accordance with the world-wide experience in road building industry. Static loading shall be positioned as the simplest and low-cost test method that permits the testing of relatively thick pavement constructions but does not allow the measurements of deflection bowl. Cyclic loading is more convenient for thinner pavements testing and, together with surface moduli, permits the measurements of deflection bowl. Impulse loading is the most representative test method that simulates traffic loading most adequately and permits the deflection bowl measurements as well. Impulse load can be applied using “high-weigh” device such as falling weight deflectometer, or light weight deflectometer thus given the possibility for testing road pavements with a monolithic upper layer or an unbound layer (base layer, sub-base, sub-grade layer). Standard methods for testing road pavements by loading were widely implemented to measure the surface moduli, the deflection bowl values and also, for rigid pavements, the load transfer efficiency in an area of joint or crack. Surface moduli and deflection bowl values are effectively used, mainly through the back-calculation processes, to assess the stiffness of pavement layers, the life span of road pavements and also for the project or network level testing of road pavements. These tasks rise as high actual for Ukraine intended to be integrated in the EU transport system, where trucks weighing up to 40 tonnes are permitted for international hauling. Considering the necessity for expansion of network level testing to assess the bearing capacity of road pavements and to set restrictions for trafficking at some critical conditions especially seasonal those, the conclusions state the need to process related national standards as national normative documents aiming on: – implementation of standard international classification of methods for testing the road pavements by loading considering the type of loading; – widening the requirements for proceeding of tests; – improvement the pavement management system. Keywords: road pavement; non-destructive testing; static loading; cyclic loading; dynamic loading.

Structural Evaluation by FWD In Flexible Pavement

Pavement performance evaluation an prediction are of great importance to facilitate pavement management system. Pavement evaluations are conducted to determine functional and structural conditions of a pavement section either for purposes of routine monitoring or planned corrective action. The Falling Weight Deflectometer is a device that is used to evaluate pavement and pavement layer stiffness. It is a trailer-mounted device that operates by dropping a weight on to the pavement and measuring the resulting pavement deflections. Various computations may be performed on the deflection data to evaluate the pavement and the stiffness of it and its constituent layers. During testing, a FWD subjects the pavement surface to a load pulse which simulates the load produced by a rolling vehicle wheel. The load pulse is produced by dropping a large weight onto a "buffer" which shapes the pulse, and then transmitted to the pavement through a circular load plate. Data are acquired from various sensors for use in post-test analysis of pavement properties. Data collected from FWD can be used for determination of structural capacity of in-service pavements for analytical analysis of pavement performance, predicting layer moduli for pavement component layers, remaining life of pavements and in deciding strengthening and rehabilitation measures to be adopted for meeting the requirements. Analyses of data are being carried out using standard software as supplied by the manufacturer. Information such as load, layer types and thicknesses, range of moduli and Poisson’s ratio etc. for different layer materials would also be needed for FWD deflection data analysis. While carrying out analyses of data, it assumes material to be homogeneous, isotropic, linear elastic, weightless and semi-infinite. This report contains evaluation on flexible pavement by FWD and according to resulting data, design of overlay will be done accordingly.

Stiffness identification method for asphalt pavement layers and interfaces using monitoring data from built-in sensors

The bearing capacity of asphalt pavement gradually deteriorates due to repeated traffic loads. As the crucial mechanical index to evaluate the bearing capacity, the evolution of the stiffness of pavement layers and interfaces is necessary to be mastered. This paper proposes a stiffness identification method for the asphalt pavement layers and interfaces by using monitoring data from built-in sensors. First, the analytical solution of multi-layered elastic/viscoelastic medium with imperfect interlayer subjected to moving load is derived, and the theoretical relationship between stiffness and mechanical response can be obtained. The sensor layout is optimized on the basis of the theoretical relationship. Then, the stiffness identification method is developed, and its feasibility is theoretically explored through an example of three-layered elastic/viscoelastic medium. Finally, the proposed stiffness identification method is applied to a realistic asphalt pavement to validate its reliability. Results show that the proposed method can evaluate the stiffness of pavement layers (including elastic and viscoelastic properties) and interfaces. It is noteworthy that the stiffness identification method by using monitoring data from built-in sensors can be performed in real-time under each passing vehicle load, and is helpful to understand the damage behavior of pavement and guide maintenance decision-making.

Assessing impacts of climate change on flexible pavement service life based on Falling Weight Deflectometer measurements

Flexible pavements are typically designed using historical climate but are challenged by future climate change. Quantifying impacts of climate change on pavement service life can assist road authorities in planning for climate adaptation and, eventually, build climate resilience into road infrastructure design and management. In this study, a novel data-driven methodology is developed in order to quantify impacts of climate change on pavement service life in locations where Falling Weight Deflectometer (FWD) data are continuously measured, by means of: 1) training a supervised model (linear regression or Artificial Neural Networks, ANN) using historical climate data, maintenance, and traffic data as the candidate inputs and pavement layer stiffness back calculated from FWD testing as the outputs; 2) predicting layer stiffness using statistically downscaled future climate projections for three Coupled Model Intercomparison Project Phase 5 global climate models and three greenhouse gas concentration scenarios for four future 20-year periods; and 3) estimating changes in pavement stiffness and service lives due to climate change. A case study performed on a pavement section in Minnesota has shown that pavement layer stiffness will have a long-term reduction under future climate and the investigated pavement will lose up to 22.5% service life at the end of the century (2080–2099) from the 20 years’ service life compared to the baseline climate (1979–1998).

Pavement performance assessment using a cost‐effective wireless accelerometer system

AbstractPavement condition monitoring is required to identify pavements in need of maintenance or rehabilitation. Early identification of reduction in pavement's structural resistance and improving the structural resistance by minor repairs can lead to significantly lower maintenance costs for transportation agencies. In this study, a cost‐effective wireless sensor that can be embedded in the road to measure the transient vibrations due to different applied loads was tested to determine its effectiveness in terms of pavement displacement measurements. Test results show that the vibration sensor, combined with the algorithms, can be embedded in new or existing pavements and used as an accurate wireless displacement sensor. The low cost of the sensor system allows the use of these sensors at high densities for monitoring the performance of an entire road network. Outputs from the developed system can be directly used to evaluate the condition and performance of pavement structure (increasing displacement over time indicating increasing pavement damage). In addition, displacement data from the system can be used to backcalculate pavement layer stiffnesses, which can be used to predict long‐term performance of the pavement structure. Reduction in pavement layer stiffness over time can be used to determine long‐term damage accumulation.

Probabilistic prediction of asphalt pavement performance

Variability of pavement design parameters has always been a concern to pavement designers and highway agencies. A robust pavement design should take into account the variability of the design inputs and its impact on the reliability of the design. In this study, the variability effect of thickness and stiffness of pavement layers was investigated. The variability of these parameters was described by their mean values, standard deviations and probability distribution functions. Monte Carlo Simulation method was utilised to incorporate variability of the design parameters and to construct the probability distribution function of the outputs. KENLAYER software was used to calculate pavement response at predetermined critical locations; pavement reponse was then used to predict pavement performance regarding permanent deformation, bottom-up and top-down fatigue cracking by using the mechanistic empirical pavement design guide (MEPDG) models. A Matlab code was developed to run that analysis and obtain the probability distribution function of pavement performance indicators over time. It was found that the variability of pavement layer thickness and stiffness has a significant impact on pavement performance. Also, it was found that not only the mean of the predicted performance indicators is increasing over time, but the variance of these indicators is also increasing. This means that pavement condition cannot be described by the mean values of the indicators but by the probability distribution function which can describe pavement condition at any reliability level.

Change in Stiffness of Pavement Layers in the Linear Discontinuous Deformation Area

The underground mining exploitation causes deformations on the surface of the area which are classified as continuous or discontinuous. Mining deformations cause loosening or compression of the subsoil. Loosening has an impact on the reduction of the subsoil stiffness. As a result the reduction of subsoil stiffness causes loosening of construction layers built in that subsoil. Pavement is a specific case. If there happens to be loosening then the fatigue life of pavement is reduced and premature damages can be observed such as fatigue cracks or/and structural deformation. Discontinuous deformations are an especially interesting case. They not only cause the reduction of the stiffness of the subsoil and pavement layers but also cause rapid deterioration in roughness. Change of roughness is very dangerous especially on fast roads such as a highway. Lately there can be observed the so called linear discontinuous surface deformations in the lanes in the mining area. Unfortunately, the ‘in situ’ research, presenting experiments on the effect of linear discontinuous deformations on the pavement, is in short supply. It is especially crucial with regard to the design of pavement reinforcement and the specification of optimal length of the reinforced part of the road. The article presents the results of ‘in situ’ tests carried out on the chosen pavements where the so called linear discontinuous surface deformation has appeared. The genesis of the damage is connected with the underground mining exploitation. Falling Weight Deflectometer (FWD) has been used in researches. Measuring points were carried out with high frequency which helped to acquire a very interesting distribution of deflections. The distribution of deflections well shows the impact of linear discontinuous deformation on the changes in stiffness pavement layers. In the analysis of data from FWD there has been used back calculation which worked modulus of layers out. The results of researches and analysis have allowed to specify the scale of stiffness reduction of subsoil and pavement layers and, above all, to specify a minimal area of reinforcement. Therefore, the results of the analysis can be very helpful in determining the range of reinforcement as well as designing reinforcement. Of course, researches should be continued for better knowledge about the impact of discontinuous deformations on pavement.

초박층 폴리머콘크리트 포장적용을 위한 매개변수 해석

경제성장 및 물류증가로 인하여 장대교량에 대한 수요가 증가하고 있다. 장지간을 가진 교량 건설을 위해서는 자중 감소가 필수적이며, 이를 위한 노력의 일환으로 초박층 포장재에 대한 연구 및 개발이 진행되고 있다. 국내 설계기준을 살펴보면, 이전 도로교설계기준(2010)에서는 포장이 가지는 강성을 강바닥판에 고려하지 않아 보수적인 설계가 이루어져 왔으나, 최근 개정된 설계기준인 도로교설계기준(2012, 한계상태설계법)에서는 일정한 조건 하 포장이 가지는 강성을 강바닥판에 반영하는 것을 허용하고 있다. 하지만 초박층 교면포장이 강바닥판에 미치는 영향에 대한 연구는 미비한 실정이며, 진행된 연구 또한 거동 특성 및 피로응력에 대한 연구로 한정되어 왔다. 본 연구에서는 포장강성의 반영/미반영, 교면 포장재료, 포장 두께 및 강바닥판 두께를 매개변수로 하여 이에 따른 강바닥판의 처짐과 전단응력의 변화 및 추세를 알아보고 또한 이를 바탕으로 강바닥판에 초박층 교면포장 강성 적용의 효율성을 알아보았다. Base on Korean design code, previous design code had not considered the effect of pavement on the orthotropic steel deck, however recent design code (Limit State Design Method, 2012) allowed to consider the effect of pavement on the orthotropic steel deck, and efforts to apply the stiffness of pavement to the deck continue. Meanwhile, research on the effect of ultra thin bridge deck overlay on the orthotropic steel deck is inadequate, previous study was limited in about fatigue stress and performance between pavement layer and the orthotropic steel deck. In this study, according to changing of pavement layer stiffness application, pavement materials, pavement thickness and steel deck thickness, analysis of deflection. In addition to base on this result, consider effectiveness of ultra-thin pavement stiffness application on the orthotropic steel deck.

도로포장 반응모형에 대한 통계모형 개발

The Falling Weight Deflectormeter has been widely used in evaluating the structural adequacy of pavement structures. The deflections measured from the FWD are capable of estimating the stiffness of pavement layers and measuring the pavement responses in the pavement structure. The objective of paper is to develop the pavement response model using a partial least square regression technique based on the FWD deflection data. The partial least square regression method enables to solve the multicollinearity problem occurred in multiple regression model. It is also found that the pavement response model can be developed using the raw data when a partial least square regression was used.

Development of an intelligent pavement analysis toolbox

An intelligent pavement analysis toolbox was developed for backcalculating the mechanical properties of flexible pavements from non-destructive test data through an adaptive, multi-hybrid, integrated non-linear systems approach. The problem involves searching for the optimal combination of pavement layer stiffness solutions in an unsmooth, multi-modal, complex search space which makes it amenable to the application of differential evolution, a stochastic parallel direct search evolution strategy optimisation method. This innovative approach takes advantage of the combined efficiency and accuracy achieved by integrating advanced pavement numerical modelling schemes such as finite-element methods, computational intelligence-based surrogate mapping techniques such as artificial neural networks, and heuristics-based global optimisation strategies such as differential evolution, and yet provides a user-friendly automated pavement evaluation toolbox for the highway engineer to use on a real-time basis for accurate infrastructure evaluation. The main aim of the present study was to demonstrate the feasibility of using such an integrated modular systems approach to the complex non-linear parameter identification problem in pavement engineering.

Global Optimization of Pavement Structural Parameters during Back-Calculation Using Hybrid Shuffled Complex Evolution Algorithm

In this paper, the use of a hybrid evolutionary optimization algorithm is proposed for global optimization of pavement structural parameters through inverse modeling. Shuffled complex evolution (SCE) is a population-based stochastic optimization technique combining the competitive complex evolution with the controlled random search, the implicit clustering, and the complex shuffling. Back-calculation of pavement layer moduli is an ill-posed inverse engineering problem, which involves searching for the optimal combination of pavement layer stiffness solutions in an unsmooth, multimodal, complex search space. SCE is especially considered a robust and efficient approach for global optimization of multimodal functions. A desirable characteristic of the SCE algorithm is that it uses information about the nature of the response surface, extracted using the deterministic Simplex geometric shape, to direct the search into regions with higher posterior probability. The hybrid back-calculation system described in this paper combines the robustness of the SCE in global optimization with the computational efficiency of neural networks and advanced pavement system characterization offered by employing finite-element models. This is the first time the SCE approach is applied to real-time nondestructive evaluation of pavement systems required in the routine maintenance and rehabilitation activities for sustainable transportation infrastructure.

Developing Artificial Neural Network Models to Automate Spectral Analysis of Surface Wave Method in Pavements

The spectral analysis of surface waves (SASW) method is a nondestructive testing method of pavements based on the dispersive characteristic of seismic surface waves. The method can provide the thickness and stiffness of pavement layers. One of the more complex aspects of the SASW method is an iterative process to estimate the pavement parameters, called the inversion procedure. In this paper, the feasibility of completely automating the inversion process and substituting it with artificial neural network (ANN) models was explored. A number of different ANN models were developed using various ANN training strategies. To improve the performance of some ANN models, a sequential modeling technique was implemented. In the sequential modeling, some pavement parameters are estimated first from an initial set of ANN models, which is then the input to subsequent ANN models to estimate other parameters of interest. Furthermore, the performance of the ANN models was evaluated using a number of well-characterized pavement sections. The results illustrated that ANN models could estimate the upper layers’ parameters so well that they could replace the inversion process. The ANN models for other layers were capable of generating robust initial estimates for a well-constrained formal inversion that can be readily automated.

Neural Network–Swarm Intelligence Hybrid Nonlinear Optimization Algorithm for Pavement Moduli Back-Calculation

This paper describes a novel hybrid intelligent system approach to inversion of nondestructive pavement deflection data and back-calculation of nonlinear stress-dependent pavement layer moduli. Particle swarm optimization (PSO), a population-based stochastic optimization technique inspired by social behavior of bird flocking or fish schooling, is fast emerging as an innovative and powerful computational metaphor for solving complex problems in design, optimization, control, management, business, and finance. Back-calculation of pavement layer moduli is an ill-posed inverse engineering problem which involves searching for the optimal combination of pavement layer stiffness solutions in an unsmooth, multimodal, complex search space. PSO is especially considered a robust and efficient approach for global optimization of multimodal functions. The hybrid back-calculation system described in this paper integrates finite element modeling, neural networks, and PSO in an efficient manner to mitigate the limitations and take advantages of the strengths to produce a system that is more effective and powerful than those which could be built with single technique. This is the first time the PSO approach is applied to real-time nondestructive evaluation of pavement systems.

Backcalculation of Non-Linear Pavement Moduli Using Finite-Element Based Neuro-Genetic Hybrid Optimization

The determination of pavement layer stiffness is an essential step in evaluating the performance of existing road pavements and in conducting pavement design and analysis using mechanistic approaches. Over the years, several methodologies involving static, dynamic, and adaptive processes have been developed and proposed for obtaining in-situ pavement layer moduli from Falling Weight Deflectometer (FWD) test deflection data through inverse analysis and parameter identification routines. In this paper, a novel pavement analysis toolbox combining the strengths of Finite Element (FE) modeling, Neural Networks (NNs), and Genetic Algorithms (GAs) is described. The developed user-friendly automated pavement evaluation toolbox, referred to as Neuro-Genetic Optimization Toolbox (NGOT) can be used on a real-time basis for accurate and rapid transportation infrastructure evaluation. It is shown that the developed toolbox backcalculates non-linear pavement layer moduli from actual field data with better accuracy compared to regression and conventional backcalculation approaches. About 93% of the paved roads in the US are reported to be composed of flexible pavement (2). Flexible pavements are multi-layered, heterogeneous structures that are designed to flex under repeated traffic loading. A typical flexible pavement structure consists of the surface course (typically Hot-Mix Asphalt) at the top, underlying base and subbase (optional) courses (typically unbound granular material), and a subgrade (typically soil) at the bottom. In the field, Non-Destructive Testing (NDT) of in-service pavements using a Falling Weight Deflectometer (FWD) equipment is carried out to measure the deflection response of the pavement structure to applied dynamic load that simulates a moving wheel. The deflected shape of the basin is predominantly a function of the thickness of the pavement layers, the moduli of individual layers, and the magnitude of the load. The surface deflections are typically measured at radial offsets of 0 mm (D0), 300 mm (D1 or D300), 600 mm (D2 or D600), 900 mm (D3 or D900), 1200 mm (D4 or D1200), and 1500 mm (D5 or D1500) from the center of FWD load plate. Backcalculation is the accepted term used to identify a process whereby the elastic (Young's) moduli of individual

Assessing Variability of In Situ Pavement Material Stiffness Moduli

This paper describes research carried out to investigate pavement material-stiffness modulus variability. Detailed pavement surface deflection data were obtained from two sections of newly constructed flexible pavement in the United Kingdom, by performing tests with a falling weight deflectometer. The deflection data were back-analyzed using the computer program PADAL to obtain estimates of the in situ pavement layer stiffness moduli. The results were statistically analyzed to identify suitable test sampling regimes for end product specifications using the falling weight deflectometer. Results show that the mean values of key deflection parameters are not very sensitive to test spacing for a nominally uniform (as specified) pavement construction. However, more variability in the standard deviations of these parameters, particularly when data were sampled at more than 50 m centers (<10 samples) was found. Results from a statistical analysis of the layer stiffness moduli showed that mean layer stiffness moduli could be reasonably estimated from a low number of samples. However, many more samples would be required for a reasonable estimation of standard deviation of the layer stiffness moduli. For lengths of pavement around 500 m, tests would be required every 10 m.