Summary of Explainable Artificial Intelligent (xai) For Predicting Asphalt Concrete Stiffness and Rutting Resistance: Integrating Bailey’s Aggregate Gradation Method, by Warat Kongkitkul et al.
Explainable Artificial Intelligent (XAI) for Predicting Asphalt Concrete Stiffness and Rutting Resistance: Integrating Bailey’s Aggregate Gradation Method
by Warat Kongkitkul, Sompote Youwai, Siwipa Khamsoy, Manaswee Feungfung
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study employs XAI techniques to analyze the behavior of asphalt concrete with varying aggregate gradations, focusing on resilience modulus (MR) and dynamic stability (DS). A deep learning model with a multi-layer perceptron architecture is used to predict MR and DS based on aggregate gradation parameters. The model’s performance is validated using k-fold cross-validation, demonstrating superior accuracy compared to alternative machine learning approaches. SHAP values are applied to interpret the model’s predictions, providing insights into the relative importance of different gradation characteristics. Key findings include the identification of critical aggregate size thresholds and size-dependent performance of aggregates. Web-based interfaces are developed for predicting MR and DS, incorporating explainable features. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses artificial intelligence to understand how asphalt concrete behaves when its mix of small stones (aggregate) is changed. The researchers used a special kind of AI called deep learning to predict how well the asphalt would perform based on the size of the stones. They tested their model by comparing it to other ways of doing things, and found that their way worked better. Then, they used another tool called SHAP to figure out which types of stones were most important for making good asphalt. The researchers found some surprising things about how different stone sizes affect how well the asphalt performs. They even made a special website where people can use these AI tools to predict how well their own asphalt will perform. |
Keywords
* Artificial intelligence * Deep learning * Machine learning
