Laboratory Study to Determine the Relationship Between Rutting and Dynamic Modulus of Asphalt Concrete Mixture

Le, Thanh-Hai; Nguyen, May Huu; Nguyen, Hoang-Long; Nguyen, Ngoc-Lan

doi:10.1007/978-981-16-7160-9_74

Cited by 2 publications

(2 citation statements)

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“…Dynamic modulus of asphalt concrete mixtures (|E*|) is one of the essential input factors of asphalt mixture used to analyze pavement structure using the mechanistic-empirical method. Dynamic modulus, |E*|, has not only been increasingly acknowledged as a vital material property in mechanistic-empirical design and analysis [1], but also reflects the pavement structures induced by loading rate and temperature [2][3][4]. Based on experimental studies, the United States developed predictive models for |E*| and other factors such as volume of air voids (Va), voids in mineral aggregate (VMA), effective binder content by volume (Vbeff) for the analysis of flexible pavement structure using the mechanisticempirical method.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence approach to predict the dynamic modulus of asphalt concrete mixtures

Nguyen

Pham

2022

JSTT

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This paper develops an Artificial Neural Network (ANN) model based on 96 experimental data to forecast the dynamic modulus of asphalt concrete mixtures. The accuracy of the models was assessed using numerous performance indexes such as the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2). In addition, this study applied the repeated K-Fold cross-validation technique with 10 folds on the training data set to make the simulation results more reliable and find a model with more general predictive power. According to the research findings, the ANN model accurately predicts the dynamic modulus |E*| of asphalt concrete mixtures. Furthermore, the ANN model could successfully predict the dynamic modulus |E*| of asphalt concrete mixtures with a remarkable R2 = 0.989.

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Section: Introductionmentioning

confidence: 99%