期刊
STRUCTURE AND INFRASTRUCTURE ENGINEERING
卷 12, 期 12, 页码 1661-1674出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/15732479.2016.1157824
关键词
Reinforced masonry; experimental database; shear strength; neural networks; sensitivity analysis
资金
- Fondo Nacional de Ciencia y Tecnologia de Chile, (Fondecyt de Iniciacion) [11121161]
This paper analyses the accuracy of a selection of expressions currently available to estimate the in-plane shear strength of reinforced masonry (RM) walls, including those presented in some international masonry codes. For this purpose, predictions of such expressions are compared with a set of experimental results reported in the literature. The experimental database includes specimens built with ceramic bricks and concrete blocks tested in partially and fully grouted conditions, which typically present a shear failure mode. Based on the experimental data collected and using artificial neural networks (ANN), this paper presents alternative expressions to the different existing methods to predict the in-plane shear strength of RM walls. The wall aspect ratio, the axial pre-compression level on the wall, the compressive strength of masonry, as well as the amount and spacing of vertical and horizontal reinforcement throughout the wall are taken into consideration as the input parameters for the proposed expressions. The results obtained show that ANN-based proposals give good predictions and in general fit the experimental results better than other calculation methods.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据