Moduli Of Individual Layers Research Articles

Knowledge discovery and data mining in pavement inverse analysis

Abstract This paper describes the use of data mining tools for predicting the non-linear layer moduli of asphalt road pavement structures based on the deflection profiles obtained from non-destructive deflection testing. The deflected shape of the pavement under vehicular loading is predominantly a function of the thickness of the pavement layers, the moduli of individual layers, and the magnitude of the load. The process of inverse analysis, more commonly referred to as ‘backcalculation’, is used to estimate the elastic (Young's) moduli of individual pavement layers based upon surface deflections. A comprehensive synthetic database of pavement response solutions was generated using an advanced non-linear pavement finite-element program. To overcome the limitations associated with conventional pavement moduli backcalculation, data mining tools such as support vector machines, neural networks, decision trees, and meta-algorithms like bagging were used to conduct asphalt pavement inverse analysis. The resul...

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