当前位置: 首页> 国外交通期刊数据库 >详情
原文传递 MID 1.1: Database for Characterization of the Lateral Behavior of Infilled Frames
题名: MID 1.1: Database for Characterization of the Lateral Behavior of Infilled Frames
正文语种: eng
作者: Blasi, Gianni;De Luca, Flavia;Perrone, Daniele;Greco, Antonella;Antonietta Aiello, Maria
作者单位: Univ Salento Dept Engn Innovat Via Monteroni I-73100 Lecce Italy;Univ Bristol Dept Civil Engn Univ Walk Bristol BS8 1TR Avon England;Univ Salento Dept Engn Innovat Via Monteroni I-73100 Lecce Italy|Univ Sch Adv Studies IUSS Pavia Piazza Vittoria 15 I-27100 Pavia Italy;Univ Salento Dept Engn Innovat Via Monteroni I-73100 Lecce Italy;Univ Salento Dept Engn Innovat Via Monteroni I-73100 Lecce Italy
关键词: Reinforced concrete;Infilled frames;Laboratory test;Database;Empirical model
摘要: Research on infilled reinforced concrete frames is fundamental for the vulnerability assessment of existing buildings. The analysis of the interaction between infill and frame is an open issue in performance-based earthquake engineering due to its importance in predicting the dynamic behavior and failure modes of buildings. This study provides an open access database of laboratory tests on masonry infilled reinforced concrete frames, collected from the literature and harmonized in a consistent framework. The database is named Masonry Infill Database 1.1 (MID 1.1). The data were grouped in categories to calibrate a piecewise linear curve representing the lateral response of the infill depending on the masonry wall and the frame details. The gathered data were used to assess analytical models and numerical studies from the literature with the aim of revising the formulations currently used in the equivalent strut approach. An empirical model for the equivalent strut was developed through a power-law multiple regression of the database. The open access database in its spreadsheet form is intended to provide a useful tool for the analysis of infilled reinforced concrete frames.
出版年: 2021
期刊名称: Journal of structural engineering
卷: 147
期: 10
检索历史
应用推荐