Development of Dynamic Modulus Prediction Model Using Artificial Neural Networks for Colombian Mixtures
Publication: Journal of Transportation Engineering, Part B: Pavements
Volume 150, Issue 1
Abstract
Dynamic modulus is a key material property to predict the performance of flexible pavements. Several prediction models have been developed worldwide to predict the dynamic modulus from aggregate, binder, and mixture properties with corresponding frequency and temperature. Artificial neural network (ANN)-based prediction models have shown better accuracy than regression-based models. In this study, two ANN-based prediction models were developed for the Colombian hot-mix asphalt (HMA) mixtures. One model (W-ANN) has similar input variables as the Witczak model, and the other model (H-ANN) has similar input variables as the Hirsch model. The ANN-based dynamic modulus prediction models were trained, validated, and tested using 972 data points. The coefficient of determination (), RMS error (RMSE), absolute average error (AAE), and indicated that the two ANN-based models performed better than the previous models. The W-ANN had slightly better performance than the H-ANN model. The parameters for both ANN-based models are reported to reproduce dynamic modulus values for future use and testing with high accuracy. A standalone closed-form equation was extracted from each ANN model, which makes the developed models easier for and more accessible to practitioners. Sensitivity analysis showed that both models are sensitive to binder and mixture properties. These models can be utilized in Colombia for the existing and future development of pavement design packages, and will reduce the necessity of extensive testing in the future.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors confirm their contribution to the paper as follows: study conception and design: M. Souliman and P. Acharjee; data collection: F. Freyle and L. Fuentes; analysis and interpretation of results: P. Acharjee, M. Souliman, and L. Fuentes; and manuscript draft preparation: P. Acharjee, M. Souliman, L. Fuentes, and F. Freyle. All authors reviewed the results and approved the final version of the manuscript.
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Information & Authors
Information
Published In
Journal of Transportation Engineering, Part B: Pavements
Volume 150 • Issue 1 • March 2024
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Feb 27, 2023
Accepted: Sep 26, 2023
Published online: Nov 24, 2023
Published in print: Mar 1, 2024
Discussion open until: Apr 24, 2024
ASCE Technical Topics:
- Analysis (by type)
- Artificial intelligence and machine learning
- Computer programming
- Computing in civil engineering
- Dynamic models
- Dynamic modulus
- Engineering fundamentals
- Errors (statistics)
- Material mechanics
- Material properties
- Materials characterization
- Materials engineering
- Mathematics
- Mechanical properties
- Mixtures
- Model accuracy
- Models (by type)
- Neural networks
- Sensitivity analysis
- Statistics
Authors
Metrics & Citations
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