Advanced Approaches to Characterizing Nonlinear Pavement System Responses

Abstract

The use of falling weight deflectometer–based backcalculation techniques to determine pavement layer moduli is a cost-effective and widely used method for the structural evaluation of an existing pavement. The nonlinear stress-sensitive response of pavement geomaterials has been well established, and mechanistic-based pavement design can be improved by inclusion of these nonlinear material properties. To further the science of nonlinear backcalculation, the TRB Strength and Deformation Characteristics of Pavement Sections Committee has assembled four data sets that can be used to demonstrate the ability to derive stress-dependent moduli for pavement layers. In this study, validated artificial neural network (ANN)–based backcalculation-type flexible pavement analysis models were used to evaluate the TRB Nonlinear Pavement Analysis Project data sets. The Illi-Pave finite element (FE) model, considering nonlinear stress-dependent geomaterials characterization, was utilized to generate a solution database for developing the ANN-based structural models. Such use of ANN models enables the incorporation of needed sophistication in structural analysis, such as FE modeling with proper materials characterization, into routine practical design. This study illustrated the complexities associated with interpreting the backcalculated modulus values. In general, the predicted strains agreed reasonably well with the measured strain values, whereas the predicted stresses did not.

Application of ANN in Pavement Engineering: State-of-Art

“…Very good results were obtained, namely average and maximum errors lower than 2.5% and 8.7%, respectively. Ceylan et al (2007) validated ANN models to predict layer moduli as a function of FWD deflections and layer thicknesses. A total of 24093 runs in a FE program were carried out for data generation (1000 points used for validation and 23094 for training).…”

Estimation of Specific Gravity with Penetration and Penetration Index Parameters by Artificial Neural Network

“…Pavement researchers have been widely using ANNs in diverse subjects during the past decade. ANNs have been successfully used by researchers to predict/back-calculate pavement moduli by training the ANNs using deflection data from falling-weight deflectometers [1][2][3]. Tutumluer and Seyhan [4] estimated the horizontal and shear moduli by using confining and deviator stresses of triaxial test, deformation and aggregate properties.…”

Study on High Temperature Stability Index of Asphalt Mixture

“…Even for the index of DS, there exits little value on comparability among the result come from different rutting test apparatuses. Nowadays most research abroad and home focus on rut test process, with little attention to the way of data calculation for rutting test results [3,4].…”