The successful application of ANNs in the back-calculation process can make the incorporation thereof possible in a PMS due to the relatively fast execution speed associated with ANN solutions. Solutions that are obtained in real-time are required for PMS applications due to the large volume of data that is to be processed on a regular (yearly) basis. Application of back-calculated moduli has therefore not yet been applied on a grand scale in Pavement Management Systems (PMS) in South Africa due to the complexity of the process to be followed. With all the conventional back-calculation programs available user intervention is required to obtain results that can be considered representative of the pavement structure. Theoretical deflection basins are calculated using static, linear-elastic analysis of multi-layer systems. Back-calculation is usually accomplished by comparing measured deflection basins with calculated theoretical deflection basins for a specific road structure or an assumed road structure if the pavement structure is not known. Evaluation of FWD test results entails back-calculation of in-situ pavement layer moduli from measured deflections. INTRODUCTION The Falling-Weight Deflectometer (FWD) is used to non-destructively assess the structural properties of flexible pavements. Keywords: Back-calculation, artificial neural networks, feedforward, learning, training, back-propagation, gradient descent, moduli, Falling-Weight Deflectometer, FWD, non-destructive testing, flexible pavements 1. It is better to identify representative pavement types and train ANNs for each of these pavements. It is also shown that it is very difficult to design a single ANN that can be successfully used on all the possible pavement types.

Improvements in the data generation process using a theory that accommodates non-linear and stress dependent behaviour of materials may result in improved performance of the ANNs. ANNs can back-calculate E-moduli very successfully from ideal deflection basins at lightning fast speeds. The main problem identified is the basis on which the dataset used to train ANNs, is generated using linear elastic theory. It is shown that ANNs can back-calculate E-moduli, but with different degrees of success. Back-calculation is used to back-calculate E-moduli of pavement layers that can be used in a mechanistic approach to estimate remaining pavement life from pavement response. Deflection measurements with a FWD are meaningful indicators of pavement strength. An ANN is a network of interconnected elements with the function to produce an output pattern when presented with an input pattern. ABSTRACT Artificial Neural Networks (ANN) are introduced in this paper with an example application given demonstrating the back calculation of layer elastic moduli (E-moduli) form Falling Weight Deflection (FWD) measurements.

van de Ven 2 1 Entech Consultants (Pty) Ltd South Africa. APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN THE BACK-CALCULATION OF FLEXIBLE PAVEMENT LAYER MODULI FROM DEFLECTION MEASUREMENTS S.J.

Modcomp backcalculation software