AI Summary & Key Takeaways
AI InsightResults indicate that the ANN can be used for backcalculation of the thickness of layers with great improvement and accuracy.
📌 3 Key Takeaways
Abstract
Flexible pavements are affected by moving vehicles, climate and other environmental factors. As a result of these factors, the pavement starts to deteriorate. In order to prevent further deterioration, a maintenance program should be carried out at right time and right places. For the determination of the structural carrying capacity of the pavement, non-destructive testing equipments are used. These are mainly Benkelman Beam, dynaflect, road rater and falling weight deflectometer (FWD). In such a process, the most important thing is to analyze the collected data. A backcalculation procedure is carried out for back-calculating the elastic modulus for each layer that has an effect on the pavement life. Generally, linear elastic and finite element based programs are used for backcalculation, but they are time consuming. An artificial neural network (ANN) approach is used for the elimination of this drawback during the course of this study. Results indicate that the ANN can be used for backcalculation of the thickness of layers with great improvement and accuracy.
Özet
Flexible pavements are affected by moving vehicles, climate and other environmental factors. As a result of these factors, the pavement starts to deteriorate. In order to prevent further deterioration, a maintenance program should be carried out at right time and right places. For the determination of the structural carrying capacity of the pavement, non-destructive testing equipments are used. These are mainly Benkelman Beam, dynaflect, road rater and falling weight deflectometer (FWD). In such a process, the most important thing is to analyze the collected data. A backcalculation procedure is carried out for back-calculating the elastic modulus for each layer that has an effect on the pavement life. Generally, linear elastic and finite element based programs are used for backcalculation, but they are time consuming. An artificial neural network (ANN) approach is used for the elimination of this drawback during the course of this study. Results indicate that the ANN can be used for backcalculation of the thickness of layers with great improvement and accuracy.