原文传递
Machine Learning–Based Failure Mode Recognition of Circular Reinforced Concrete Bridge Columns: Comparative Study
| 题名： | Machine Learning–Based Failure Mode Recognition of Circular Reinforced Concrete Bridge Columns: Comparative Study |
| 正文语种： | 英文 |
| 作者： | Sujith Mangalathu, Ph.D., A.M.ASCE1; Jong-Su Jeon, Ph.D.2 |
| 作者单位： | 1Postdoctoral Fellow, Dept. of Civil and Environmental Engineering, Univ. of California, Los Angeles, Los Angeles, CA 90095. 2Assistant Professor, Dept. of Civil and Environmental Engineering, Hanyang Univ., Seoul 04763, Republic of Korea (corresponding author). |
| 关键词： | Failure mode classification; Machine learning; Artificial neural network; Experimental data; Circular reinforced concrete bridge columns. |
| 摘要： | The prediction of failure mode of columns is critical in deciding the operational and recovery strategies of a bridge after a seismic event. This paper contributes to the critical need of failure mode prediction for circular reinforced concrete bridge columns by exploring the capabilities of machine learning methods. Three types of failure mode such as flexure, flexure-shear, and shear are considered in this study, and 311 specimens are compiled from experimental studies on the circular columns. The efficiency of various machine learning models such as quadratic discriminant analysis, K-nearest neighbors, decision trees, random forests, na?ve Bayes, and artificial neural network is evaluated using a randomly assigned test set from the collected data. It is noted that artificial neural network has superior performance amongst all the machine-learning methods, and the comparison of this classification with the existing methods underscores the advantage of the artificial neural network in failure mode recognition. Classification based on artificial neural network is 91% accurate in identifying the failure mode of the collected experimental data. |
| 出版年： | 2019 |
| 期刊名称： | Journal of Structural Engineering |
| 卷： | 145 |
| 期： | 10 |
| 页码： | 1-12 |
相关文献
- Machine Learning–Based Seismic Reliability Assessment of Bridge Networks
- Regional Seismic Risk Assessment of Infrastructure Systems through Machine Learning: Active Learning Approach
- Short-Term Flexural Behavior of Prestressed Glulam Beams Reinforced with Curved Tendons
- Effect of Load Cycling, FRP Jackets, and Lap-Splicing of Longitudinal Bars on the Effective Stiffness and Ultimate Deformation of Flexure-Controlled RC Members
- Experimental Study on Static Strength of Damaged Concrete Arches Reinforced by Corrugated Steel
- New generation of preoast concrete double tees reinforced with carbon-fiber-reinforced polymer grid
检索历史
应用推荐
