Fast prediction of the mechanical response for layered pavement under instantaneous large impact based on random forest regression

The layered pavements usually exhibit complicated mechanical properties with the effect of complex material properties under external environment.In some cases,such as launching missiles or rockets,layered pavements are required to bear large impulse load.However,traditional methods cannot non-destructively and quickly detect the internal structural of pavements.Thus,accurate and fast prediction of the mechanical properties of layered pavements is of great importance and necessity.In recent years,machine learning has shown great superiority in solving nonlinear problems.In this work,we present a method of predicting the maximum deflection and damage factor of layered pavements under instantaneous large impact based on random forest regression with the deflection basin parameters obtained from falling weight deflection testing.The regression coefficient R^(2)of testing datasets are above 0.94 in the process of predicting the elastic moduli of structural layers and mechanical responses,which indicates that the prediction results have great consistency with finite element simulation results.This paper provides a novel method for fast and accurate prediction of pavement mechanical responses under instantaneous large impact load using partial structural parameters of pavements,and has application potential in non-destructive evaluation of pavement structure.

Road pavement condition diagnostics using smartphone-based data crowdsourcing in smart cities

The purpose of the paper is to analyse the effectiveness of a solution known as road condition tool(RCT)based on data crowdsourcing from smartphones users in the transport system.The tool developed by the author of the paper,enabling identification and assessment of road pavement defects by analysing the dynamics of vehicle motion in the road network.Transport system users equipped with a smartphone with the RCT mobile application on board record data of linear accelerations,speed,and vehicle location,and then,without any intervention,send them to the RCT server database in an aggregated form.The aggregated data are processed in the combined time and location criterion,and the road pavement condition assessment index is estimated for fixed 10 m long measuring sections.The measuring sections correspond to the sections of roads defined in the pavement management systems(PMS)used by municipal road infrastructure administration bodies.Both the research in question and the results obtained by the method proposed for purposes of the road pavement condition assessment were compared with a set of reference data of the road infrastructure administration body which conducted surveys using highly specialised measuring equipment.The results of this comparison,performed using binary classifiers,confirm the potential RCT solution proposed by the author.This solution makes it possible to global monitor the road infrastructure condition on a continuous basis via numerous users of the transport system,which guarantees that such an assessment is kept up to date.