Study of retroanalysis of asphaltic pavements resilience modules with the use of artificial neural networks
Knowledge of pavement Resilience Modules (RM) is an important element for the structural assessment of existing infrastructures. One way to determine them is through dynamic repeated load testing in the laboratory; another way is to use the technique of retroanalysis, which consists in obtaining the RM from the thickness of the layers and the deflections measured on the pavement surface. In this sense, this paper aims to present the RM retroanalysis through the technique of Artificial Neural Networks (ANN) as an alternative to the traditional retroanalysis. The results demonstrate that the ANN could predict the RM with results of coefficients of determination (R²) above 99.9% between the reference and predicted values. Thus, ANN are a potential alternative to obtain this important mechanical property of paving materials compared to traditional methods.
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Study of retroanalysis of asphaltic pavements resilience modules with the use of artificial neural networks
Semantic Scholar · Engineering · 2019
Abstract
Knowledge of pavement Resilience Modules (RM) is an important element for the structural assessment of existing infrastructures. One way to determine them is through dynamic repeated load testing in the laboratory; another way is to use the technique of retroanalysis, which consists in obtaining the RM from the thickness of the layers and the deflections measured on the pavement surface. In this sense, this paper aims to present the RM retroanalysis through the technique of Artificial Neural Networks (ANN) as an alternative to the traditional retroanalysis. The results demonstrate that the ANN could predict the RM with results of coefficients of determination (R²) above 99.9% between the reference and predicted values. Thus, ANN are a potential alternative to obtain this important mechanical property of paving materials compared to traditional methods.