期刊
ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS
卷 13, 期 1, 页码 188-198出版社
HONG KONG POLYTECHNIC UNIV, DEPT CIVIL & STRUCTURAL ENG
DOI: 10.1080/19942060.2018.1563829
关键词
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 computing (SC)
资金
- Technical and Soil Mechanics Laboratory Company of the Ministry of Roads and Urban Planning
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 ground-penetrating 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%.
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