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

Le, Thanh-Hai; Nguyen, Hoang-Long; Pham, Cao-Thang

doi:10.58845/jstt.utt.2022.en.2.2.22-31

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Previous studies have shown that machine learning evaluation performance can be improved when an optimization approach is applied to fine-tune model hyperparameters instead of performing manual trial-and-error calibration [5][6][7]. This is evident in the results reported in [8][9][10][11][12] in the prediction of E*.…”

Section: Introductionmentioning

confidence: 99%

A Metaheuristic Approach of predicting the Dynamic Modulus in Asphalt Concrete

Amir,

Yusuf,

Uwanuakwa

2024

Eng. Technol. Appl. Sci. Res.

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The prediction of the asphalt dynamic modulus (E*), which measures the material's ability to withstand changes in shape or structure, is important. Previous studies indicated that the well-known Witczak 1-40D model for E* is outperformed by machine learning models. Additionally, the application of machine learning algorithms requires manual fine-tuning of their hyperparameters. In this study, the artificial Hummingbird and Harris Hawks optimization algorithms were employed in the automatic calibration of the Random Forest and Gradient Boost algorithms' hyperparameters for modeling E* using the Witczak 1-40D model and additional parameters. In addition, the model was interpreted using the Shapley value and permutation feature importance. The results indicate that the optimized artificial hummingbird algorithm model performed better, with R² reaching 0.97. The interpretability of the model suggests that the binder parameters exhibited the highest effect on the variance of E*.

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Section: Introductionmentioning

confidence: 99%

A Metaheuristic Approach of predicting the Dynamic Modulus in Asphalt Concrete

Amir,

Yusuf,

Uwanuakwa

2024

Eng. Technol. Appl. Sci. Res.

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Evaluation of asphalt mixtures modified with low-density polyethylene and high-density polyethylene using experimental results and machine learning models

Junaid,

Jiang,

Gazder

et al. 2024

Sci Rep

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The widespread use of low-density polyethylene (LDPE) and high-density polyethylene (HDPE) plastics has resulted in a large amount of waste plastic that requires appropriate disposal or reuse. One potential solution is to use them in the modification of asphalt concrete (AC) mixtures for more sustainable highways. To study this possibility, permanent deformation and dynamic modulus (DM) of the LDPE and HDPE modified AC mixtures was investigated by conducting flow number (FN), flow time (FT) and DM tests on Superpave gyratory compacted specimens. Machine learning models; multi-layer perceptron (MLP), radial basis function neural network (RBFNN), generalized regression neural network (GRNN) and support vector machine (SVM) were used to predict the DM on the basis of frequency and temperature parameters. The model’s performance was gauged by analyzing the root mean square error, mean relative error, and coefficient of determination. The study findings revealed that the LDPE and HDPE modified AC mixtures provide 2.07 times and 1.27 times better resistance to permanent deformation, respectively, than their counterpart. It was also found that the LDPE and HDPE modified AC mixtures have 2.1 times and 1.4 times higher DM values, respectively, than the Control AC mixtures. Among the machine learning models, MLP (R 2 = 0.98) showed best accuracy in predicting DM and thus is recommended to be used in similar studies due to its robustness. Additionally, the feature importance analysis revealed that frequency has the highest impact on DM predictions, followed by temperature and the inclusion of the LDPE. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-74657-1.

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

Cited by 2 publications

References 15 publications

A Metaheuristic Approach of predicting the Dynamic Modulus in Asphalt Concrete

A Metaheuristic Approach of predicting the Dynamic Modulus in Asphalt Concrete

Evaluation of asphalt mixtures modified with low-density polyethylene and high-density polyethylene using experimental results and machine learning models

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