摘要
基于贝叶斯理论推断既有建筑砌体抗压强度,将现场原位轴压法测试的砌体抗压强度作为先验信息,同时利用块体和砂浆回弹法检测强度的推定值,按照《砌体结构设计规范》(GB 50003—2011)中的砌体抗压强度计算公式构造似然函数,联合先验信息和似然函数,推导既有建筑砌体抗压强度的后验分布,研究结果表明:通过后验分布可得到综合各种信息的既有建筑砌体抗压强度的合理推断值。且已建立的后验分布可作为下一次抗压强度贝叶斯推断的先验信息,可实现既有建筑砌体抗压强度值的动态长期观测,为砌体结构的定期维修和加固提供依据,为砌体结构的可持续发展提供基础。
Based on Bayesian theory,the compressive strength of existing masonry structure was deduced.The compressive strength of masonry tested by the method of axial compression in situ was taken as a prior information.At the same time,the estimated value of strength of block and mortar detected by the rebound method was used to construct the likelihood function,according to the calculation formula of compressive strength of masonry in the"Code for Design of Masonry Structures"(GB 50003-2011).Combining the prior information and the likelihood function,the posterior distribution of compressive strength of existing building masonry was derived.The research results show that the reasonable inferred values of compressive strength of existing masonry structures containing various information can be obtained through the posterior distribution.Moreover,the established posterior distribution can be used as the prior information for subsequent compressive strength Bayesian reference,which can realize the dynamic long-term observation of compressive strength of existing masonry structure.Furthermore,it provides a basis for the regular maintenance and reinforcement of existing masonry structure,and provides a foundation for the sustainable development of existing masonry structure.
作者
倪玉双
蒋耀华
杨春侠
NI Yushuang;JIANG Yaohua;YANG Chunxia(School of Civil Engineering,Changsha University of Science&Technology,Changsha 410114,R.R.China;China Machinery International Engineering Design&Research Institute Co.,Ltd,Changsha 410007,P.R.China)
出处
《土木与环境工程学报（中英文）》
CSCD
北大核心
2021年第6期82-87,共6页
Journal of Civil and Environmental Engineering
基金
国家自然科学基金(51808054、51678067)
湖南省教育厅优秀青年项目(18B141)。
关键词
既有建筑
砌体抗压强度
贝叶斯推断
先验信息
似然函数
后验分布
existing structure
compressive strength of masonry
Bayesian inference
prior information
likelihood function
posterior distribution
