Summary of Developing An Explainable Artificial Intelligent (xai) Model For Predicting Pile Driving Vibrations in Bangkok’s Subsoil, by Sompote Youwai and Anuwat Pamungmoon
Developing an Explainable Artificial Intelligent (XAI) Model for Predicting Pile Driving Vibrations in Bangkok’s Subsoil
by Sompote Youwai, Anuwat Pamungmoon
First submitted to arxiv on: 8 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This study proposes an explainable artificial intelligence (XAI) model for predicting pile driving vibrations in Bangkok’s soft clay subsoil, leveraging a dataset of 1,018 real-world measurements. The deep neural network outperforms traditional empirical methods and other machine learning approaches like XGBoost and CatBoost, achieving a mean absolute error (MAE) of 0.276. SHapley Additive exPlanations (SHAP) analysis reveals complex relationships between input features and peak particle velocity (PPV), highlighting distance from the pile driving location as the most influential factor, followed by hammer weight and pile size. Non-linear relationships and threshold effects are observed, providing new insights into vibration propagation in soft clay. A web-based application is developed to facilitate adoption by practicing engineers, bridging the gap between advanced machine learning techniques and practical engineering applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special kind of computer program that helps predict vibrations caused by pile driving in soft soil. They used real data from 1,018 measurements to train their model, which performed better than other methods. The program can explain its predictions, showing how different factors like distance and hammer weight affect the vibrations. This information can help engineers build safer and more efficient structures. |
Keywords
» Artificial intelligence » Machine learning » Mae » Neural network » Xgboost
