Rolling Weight Deflectometer for Stiffer Pavements: Combining Machine Learning and Field Data

Abstract

Pavement structures are assessed based on its functional and structural capacity in order to evaluate the safety of its use. Structural pavement evaluation is often carried out by road authorities or consulting companies that use non-destructive testing (NDT) methods to assess the remaining life and structural capacity of a road structure at project or network level. In Western Europe and in most parts of the world, the most common equipment used as part of the NDT method is the use of the Falling Weight Deflectometer (FWD). The FWD is able to capture deflection values that can be used for the backcalculation of moduli and to determine remaining life of a structure by mechanistic-empirical equations.
In the Netherlands, like in many other countries, motorways have an increased number traffic flow based on high population densities accumulating in major cities. Due to this, pavement evaluation with stationary equipment such as the FWD can become relatively expensive due to the disruption of traffic. To solve this problem, research institutions and consulting companies have developed different versions of continuous evaluation equipment that are able to perform the structural analysis in a similar way to the FWD. In 2018, Dynatest® launched the Rapid Pavement Tester (RPT or RAPTOR) which aims to perform functional and structural evaluation of road networks at traffic speed. Continuous evaluation devices such as the RAPTOR are in constant development in order to achieve the quality and guaranty of use that equipment such as the FWD have in the pavement engineering industry. Currently this type of device uses technology that is less accurate than the sensors (Geophones) used in FWD equipment. This becomes a significant impediment in the evaluation of high stiffness pavement structures as the calculated deflections are in the lower end of the spectrum. This research aims to find a methodology that can be used in order to find limitation stiffness parameter values for which it is viable to use continuous evaluation equipment. Additionally, this research aims to find a method that can be used in the pavement engineering industry and research that is able to aid the data collection process of pavement layer information by means of machine learning tools. This was a problem faced during the elaboration of this research as the data collected to perform the analysis was incomplete in terms of layer thicknesses information. This process is carried by means of an artificial neural network (ANN) that is able to predict layer thicknesses and moduli based on deflection values and deflection parameters that obtained with the FWD.
The analysis of this research is carried by comparing deflection values in different road networks collected with the FWD and the RAPTOR under similar weather conditions, where the predicted layer information is used to assess the cut-off values and limitations that the current version of the RAPTOR has when compared to the FWD. From the presented results it was found that the asphalt layer modulus showed the highest correlation to the limitation values where the RAPTOR is able to present reliable results when compared to the FWD. Additionally, promising results were found in the use of ANN method to predict missing layer information which are assumed to improve with a specific build in the ANN architecture.