Fragility analysis for performance-based blast design of FRP-strengthened RC columns using artificial neural network. (15th July 2022)

• Record Type: Journal Article
• Authors: Zhang, Xuejie; Li, Zhong-Xian; Shi, Yanchao; Wu, Chengqing; Li, Jun
• Is Part Of: Journal of building engineering. Volume 52(2022)
• Publication Date: 2022-07-15
• Subjects: Performance-based blast design -- Fragility analysis -- FRP-strengthened column -- Artificial neural network; Building -- Periodicals; 690.05
• DOI: 10.1016/j.jobe.2022.104364
• Languages: English
• ISSNs: 2352-7102

Abstract

Abstract: In this paper, fragility analysis for performance-based blast design of FRP-strengthened RC columns is carried out. The blast intensity levels, performance levels and performance objectives of the RC columns are defined. A simplified probabilistic risk assessment framework incorporating the performance-based design concept and fragility analysis is established. Since fragility analysis is the most important but time-consuming process of probabilistic assessment risk, an artificial neural network (ANN) based fragility analysis framework is proposed to improve its computational efficiency. Based on the rapid fragility analysis method, fragility curves of several typical RC columns with or without FRP strengthening are calculated to analyze their damage probabilities. This study provides avenues for engineers to estimate the failure probabilities of RC columns with or without FRP strengthening under blast loads and make decisions quickly. Highlights: Fragility analysis for performance-based blast design of FRP-strengthened RC columns is carried out. An artificial neural network (ANN) based fragility analysis method is proposed. Fragility curves of several typical RC columns strengthened with or without FRP are calculated and analyzed.