Three-Dimensional Finite Element Analysis of Jointed Plain Concrete Pavement with EVERFE2.2, Transportation Research Record 1853: 92–99. Measured data of deflection and load transfer efficiency (LTE) of the tested joints verify the curling effect on the backcalculated structural capacity of the concrete pavements.ĭavids, W. A basic concept to adjust the cyclic variation in the backcalculated elastic modulus due to the slab curling using the temperature difference between the top and bottom of the slab is presented. The elastic moduli of the concrete pavement layers were backcalculated using the measured deflection basins, the AREA method and the method of equivalent thickness (MET). The curling effect on the backcalculated elastic modulus of the concrete slab was investigated using the different temperature gradients and slab curl conditions arising at different measurement times. In the present work, deflections of jointed concrete pavement under a Falling Weight Deflectometer (FWD) loading were measured for 48 h at the KEC (Korea Expressway Corporation) test road.

However, variation in the backcalculated elastic modulus, even within a pavement section, is a significant issue in the concrete pavement evaluation. Accordingly, the elastic moduli backcalculated by various backcalculation algorithms have been evaluated using the elastic moduli of cores of concrete pavement layers measured in the laboratory. It is shown that CP can be used to derive reliable pavement moduli predictions without compromising the accuracy of the NNR decision rule but with control of the risk of error.Different backcalculation algorithms produce different elastic moduli which are used as a key indicator of structural capacity of concrete pavements. A CP is then implemented for the NNR decision rule and tested on an independent data set to demonstrate its error calibration properties.

In this paper, a NN Regression (NNR) model is used to derive a decision rule for the inverse prediction of non-linear pavement layer moduli from Non-Destructive Test (NDT) deflection data. CP can be thought of as a strategy built on top of a machine learning algorithm to hedge its predictions with valid confidence. However, in real-world problem domains, it is highly desirable to have predictions complemented with a tolerance interval to assess their credibility and accuracy. output simple, bare predictions without an indication (confidence) of how likely each prediction is of being correct. Traditional machine learning algorithms, such as Neural Networks (NN), Support Vector Machines (SVM), etc. Conformal Prediction (CP) is a novel machine learning concept which uses past experience to determine precise levels of confidence in new predictions.

Pavement backcalculation