Modelling of masonry infills in existing steel moment-resisting frames: Nonlinear force-displacement relationship

被引:6
|
作者
Wu, Jing-Ren [1 ]
Di Sarno, Luigi [1 ]
Freddi, Fabio [2 ]
D'Aniello, Mario [3 ]
机构
[1] Univ Liverpool, Dept Civil Eng & Ind Design, Liverpool L69 3GH, England
[2] UCL, Dept Civil Environm & Geomat Engn, London WC1E 6BT, England
[3] Univ Naples Federico II, Dept Struct Engn & Architecture, I-80125 Naples, Italy
关键词
Moment Resisting Frames; Steel Structures; Masonry Infills; Equivalent strut models; Genetic Algorithm; REINFORCED-CONCRETE FRAME; OF-THE-ART; INPLANE BEHAVIOR; STRUT MODEL; STRENGTH;
D O I
10.1016/j.engstruct.2022.114699
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The study described and summarised in this paper was aimed at developing a framework for the definition of force-displacement relationships for single-strut models for masonry infill walls within steel moment-resisting frames. The methodology is based on a genetic algorithm optimisation and can be used for the calibration of force-displacement curves based on databases from either experiments or numerical simulation. A case study is also tested to demonstrate the framework in detail. Due to limited available experimental data on the seismic response of existing steel frames with masonry infills, a set of comprehensive finite element micro-models developed in Abaqus are used to generate a database. The optimal values of the parameters to feed a force-displacement relationship of the single-strut model of the masonry infills are obtained for each micro-model by solving optimisation problems with a genetic algorithm. The optimisation problem involves the minimisation of the discrepancies between the global responses from the database and their corresponding single-strut models through least square minimisation. With the optimal values as the input variables, a generalised quadrilinear model of the masonry strut is obtained through regression analysis and is validated against additional micromodels of infilled steel frames.
引用
收藏
页数:16
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