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The rutting resistance design of asphalt pavement is full of compromise between technology and economy because it is difficult to predict rutting evolution and distribution. In order to investigate the criterion of rutting, two forms yield function based on the Perzyna theory were contrasted and utilised in the finite element model. Besides, temperature and traffic load, the most important factors of rutting, were taken into consideration to make the analysis of yield function closer to the field condition of pavement. Two resistance points were proposed as criterions in this paper. One can be used in pavement maintenance to figure out the depth of serious rutting, the other can be used in pavement design to determine the usage depth of modified asphalt mixture. The analysis illustrates that the key to controlling rutting at intersections is to use better anti-rutting materials at the top layers instead of deepening the usage of modified asphalt mixture. Besides, the depth of rutting varies dramatically with axle weights. It can be inferred that there may be deviations of the prediction for the rutting distribution and evolution when using the method of equivalent axle weight. [ABSTRACT FROM AUTHOR]

Structure indicators of flexible pavements can be categorized into material moduli, deflections, distresses, or responses. Since field measurements of pavement responses, such as strains and stresses, require embedded sensors, predictions from easily measured surface deflections are desired. This study aims to predict critical strains of flexible pavements from traffic speed deflectometer (TSD) measurements. Semi-analytical finite element method (SAFEM) was developed and validated for simulation of truck-pavement system by comparison with traditional three-dimensional (3-D) FEM. Results indicated that SAFEM saved significant calculation time while ensuring the same accuracy. The efficient advantage makes it possible to develop an extensive databased of TSD measurement considering a wide rangte of pavement layer thickness, material moduli, temperature, and speed. Traditional regression analysis and artificial neural networks (ANN) models were applied to predict critical tensile, shear, and compressive strains from surface deflection parameters. The developed models were further validated with field measurements. The regression models with explicit forms are easier to understand but require asphalt layer thickness as an input for prediction of tensile or shear strains. Nevertheless, the ANN model offers greater accuracy and only requires the inputs of TSD-measured deflection slopes, which is more applicable for pavement management applications. • Developed a database of pavement responses under TSD using SAFEM. • Predicted critical pavement strains from TSD measurements. • ANN model shows better prediction accuracy with only inputs of deflection slopes. [ABSTRACT FROM AUTHOR]

• Analyzed dynamic loadingimpact on traffic speed deflectometer (TSD) measurement. • Developed semi-analytical finite element model for dynamic loading on pavement. • Studied the relationship between dynamic loads and deflection measurements. • Quantified effect of pavement roughness on measurement error using AUTC method. Traffic speed deflectometer (TSD) provides an efficient way for evaluating pavement condition. The current principle is to convert measured deflection slopes into the deflection basin using the Area Under The Curve (AUTC) method based on the time-space transform and elastic material theory. This study aims to investigate impact of dynamic loading due to pavement roughness on TSD measurements. Semi-analytical finite element (SAFEM) method was developed to simulate dynamic moving loads on flexible pavements with viscoelastic asphalt layer. The variations of pavement deflection and deflection velocity were found increasing linearly with dynamic loading coefficient (DLC), especially for deflection velocity. The average relative error between the actual deflection basin and the one from the AUTC method could be 15% at DLC of 0.1 with the highest error for the maximum deflection. This indicates the necessity of developing correction method of TSD deflection measurements on rough pavements. [ABSTRACT FROM AUTHOR]