Faulting Prediction Model in Jointed Plain Concrete Pavement and determining the parameters affecting this failure with Artificial Neural Networks

Document Type : Research Article


1 a Department of Civil & Environmental Engineering, Amirkabir University of Technology, Tehran,

2 Dep. of Civil Engineering, Amirkabir University of Technology

3 Department of Civil & Environmental Engineering, Amirkabir University of Technology, Tehran


Faulting is one of the most common functional failures in concrete pavements. Pavement design and pavement management systems can both benefit from predicting this failure. Therefore, predicting this failure can be very useful. Artificial neural networks, a powerful technique, were utilized in this study to predict this failure. The artificial neural network architecture was first determined by trial and error using 32 input variables such as traffic, weather, and structural data, and then the defined architecture was appropriately trained. New input factors that have not been explored before, such as Poisson's ratio and elastic modulus of concrete slabs, have been considered among these 32 variables, in addition to the variables utilized in earlier studies. After that, 19 input variables were discovered using a new method, and a new neural network model with 19 variables was created. Notably, the feature selection method used in this study has been developed using the metaheuristic optimization algorithm. For the model with 32 variables and 19 variables, the correlation coefficient, mean square error, and mean absolute error are 0.97, 0.45, 0.43, 0.95, 0.54, and 0.6, respectively. Random forest is recognized in data mining as a powerful technique for identifying the importance of input variables. Finally, the importance of 19 variables was assessed using the random forest approach, with the four most important variables being the yearly cumulative number of days with precipitation more than 12.7 mm (24%), elastic modulus (14%), pavement life (12%), and base thickness (10%). It is found that elastic modulus is an essential input factor that has not been considered in prior studies.


Main Subjects

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