基于组合赋权云模型的砖混结构自建房安全评估
Safety Assessment of Brick-Concrete Structure Self-built House Based on Combination Weighting Cloud Model
杨沫涵 1陶忠 2陶燕 2吴磊2
作者信息
- 1. 昆明理工大学公共安全与应急管理学院,云南 昆明 650500
- 2. 昆明理工大学建筑工程学院,云南 昆明 650500
- 折叠
摘要
为准确实现对砖混结构自建房安全状态的科学评估,考虑到结构体系的复杂性和评估指标的模糊性,本文提出一种基于组合赋权云模型的安全评估方法.首先,从地基基础、结构承载力、抗震构造措施、结构外观质量、建筑材料强度、结构相关尺寸、围护结构7个方面构建砖混结构自建房安全评估指标体系,并根据标准规范给出评估指标的衡量准则.其次,选用G1法和熵权法主客观综合确定指标权重,并构建砖混结构自建房安全评估云模型.最后,应用该模型对云南省罗平县某砖混结构自建房进行安全评估,得到的评估结果与实际评定结果一致.研究表明:所构建的组合赋权云模型较为真实全面地反映砖混结构自建房安全状态,可为我国砖混结构自建房安全鉴定与隐患排查工作提供参考.
Abstract
Considering the complexity of the structural system and the fuzziness of the evaluation indi-cators,a safety assessment method based on the combination weighting cloud model is proposed to accurately evaluate the security of brick-concrete structure self-built house.Firstly,the safety assess-ment indicator system is constructed from seven aspects:foundation,structural bearing capacity,seis-mic measures,appearance quality,material strength,structure size and envelope system.The evalua-tion criteria are given according to standard specifications.Secondly,the G1 method and entropy weight method are used to determine the subjective and objective weights of indicators,and a cloud model for safety assessment of brick-concrete structure self-built house is constructed.Finally,the model is used to evaluate the safety of a brick-concrete structure self-built house in Luoping County,Yunnan Province,and the results are consistent with the actual evaluation results.The combination weighting cloud model can comprehensively evaluate the security of brick-concrete structure self-built house,providing reference for the safety identification and hidden trouble investigation of brick-con-crete structure self-built house in our country.
关键词
G1法/熵权法/云模型/砖混结构/自建房/安全评估Key words
G1 method/entropy weight method/cloud model/brick-concrete structure/self-built house/safety assessment引用本文复制引用
基金项目
国家重点研发计划(2017YFC0703603)
国家自然科学基金(52268051)
云南省科技厅重点专项(202003AC100001)
乡村建设评价(云南省)(12020230273)
出版年
2024
国家科技期刊平台
NSTL
万方数据