Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan–Firuzkuh road

Accurate prediction of the remaining service life (RSL) of pavement is essential for the design and construction of roads, mobility planning, transportation modeling as well as road management systems. However, the expensive measurement equipment and interference with the traffic flow during the tests are reported as the challenges of the assessment of RSL of pavement. This paper presents a novel prediction model for RSL of road pavement using support vector regression (SVR) optimized by particle filter to overcome the challenges. In the proposed model, temperature of the asphalt surface and the pavement thickness (including asphalt, base and sub-base layers) are considered as inputs. For validation of the model, results of heavy falling weight deflectometer (HWD) and groundpenetrating radar (GPR) tests in a 42-km section of the Semnan–Firuzkuh road including 147 data points were used. The results are compared with support vector machine (SVM), artificial neural network (ANN) and multi-layered perceptron (MLP) models. The results show the superiority of the proposed model with a correlation coefficient index equal to 95%.

pavement management; remaining service life (RSL); support vector regression (SVR); support vector machine (SVM); particle filter; multi-layered perceptron (MLP); artificial neural network (ANN); prediction; forecasting; optimization; road maintenance and management; machine learning (ML); soft comp