原文传递
Probabilistic Machine-Learning Methods for Performance Prediction of Structure and Infrastructures through Natural Gradient Boosting
| 题名： | Probabilistic Machine-Learning Methods for Performance Prediction of Structure and Infrastructures through Natural Gradient Boosting |
| 正文语种： | eng |
| 作者： | Shi-Zhi Chen;De-Cheng Feng;Wen-Jie Wang;Ertugrul Taciroglu |
| 作者单位： | Chang’an Univ.;Southeast Univ.;Southeast Univ.;Univ. of California |
| 关键词： | Probabilistic prediction;Structural behaviors;Natural gradient;Gradient boosting;Machine learning (ML) |
| 摘要： | Abstract The capabilities of data-driven models based on machine learning (ML) algorithms in offering accurate predictions of structural responses efficiently have been demonstrated in numerous recent studies. However, efforts to date have relied on essentially deterministic approaches, and prediction confidence measures were either derived from verification data sets or completely ignored. This study examined the potential of a new algorithm—natural gradient boosting (NGBoost)—that directly produces probabilistic predictions. This type of output fits the reliability and performance analysis frameworks naturally, and also opens the pathways to utilization of self-learning algorithms and optimal design of experiments and field measurement campaigns in engineering applications. After introducing NGBoost’s fundamentals, two representative problems in structural engineering were investigated to examine NGBoost’s feasibility: (1) prediction of the strengths of squat shear walls, and (2) classification of the seismic damage levels in ordinary bridges. The results indicate that NGBoost attains mean prediction accuracy levels comparable to those of conventional ML algorithms while providing robust estimates of prediction uncertainties. |
| 出版年： | 2022 |
| 期刊名称： | Journal of structural engineering |
| 卷： | 148 |
| 期： | 8 |
| 页码： | 04022096.1-04022096.12 |
相关文献
- Machine Learning–Based Failure Mode Recognition of Circular Reinforced Concrete Bridge Columns: Comparative Study
- Framework for Development and Comprehensive Comparison of Empirical Pavement Performance Models
- Machine Learning for Enhanced Regional Seismic Risk Assessments
- Interpretable XGBoost-SHAP Machine-Learning Model for Shear Strength Prediction of Squat RC Walls
- Machine learning techniques to predict reactionary delays and other associated key performance indicators on British railway network
- Snowplow Truck Performance Assessment and Feature Importance Analysis Using Machine-Learning Techniques
检索历史
应用推荐
