文畅平.火灾后混凝土结构损伤的属性识别[J].火灾科学,2016,25(1):40-46.
火灾后混凝土结构损伤的属性识别
Attribute recognition of damage degree of fire-damaged concrete structure
投稿时间:2015-12-17  修订日期:2016-01-21
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DOI:10.3969/j.issn.1004-5309.2016.01.06
基金项目:湖南省重点学科建设项目资助(2013ZDXK006),中南林业科技大学引进高层次人才科研启动基金项目(104–0094)
作者单位
文畅平 中南林业科技大学土木工程与力学学院长沙410004 
中文关键词:  火灾后混凝土  损伤程度分级  属性测度函数  属性识别
英文关键词:Fire-damaged concrete  Classification of damage degree  Attribute measure  Attribute recognition
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中文摘要:
      应用属性数学理论建立火灾后混凝土结构损伤程度评价的属性识别模型。选择影响火灾后混凝土质量的4个参数,如抗压强度损伤系数、抗渗透性损伤系数、爆裂损伤系数以及裂纹损伤系数等作为综合评判指标,通过构造属性测度函数以计算单指标属性测度和样本综合属性测度,应用置信度准则对火灾后混凝土样本的质量状况进行属性识别,从而建立了火灾后混凝土结构损伤程度综合评判的属性识别模型。在工程实例研究中,通过属性测度分析和计算,得到了火灾后混凝土样本损伤程度的评价结果,并分别与模糊综合评价和物元可拓评判法评价结果进行了比较,结果有较好的一致性。由于属性数学理论能很好地解决具有多个模糊属性问题的综合评价,且置信度准则是根据评价集具有有序性这一特点而提出的,因而可使评价结果更为可靠。
英文摘要:
      The attribute recognition model based on attribute mathematical theory is proposed to classify the quality of fire-damaged concrete. According to the analysis of main causes of fire-damaged concrete quality classification, four parameters including strength damage coefficient, anti-penetrability damage coefficient, spalling damage coefficient, and crack damage coefficient are chosen as the criterion indices for evaluation of concrete strength after exposed to fire. Attribute measure functions are constructed to compute attribute measure of single index and synthetic attribute measure. The quality classification of fire-damaged concrete samples is recognized by the confidence criterion, and then attribute recognition model is developed for comprehensive assessment of the classification of fire-damaged concrete strength. Two actual example studies indicate that synthetic assessment results obtained by attribute measure analysis agree well with that obtained by fuzzy evaluation method and the extension method respectively. As attribute mathematical theory can successfully resolve certain issues with a number of fuzzy attributes in comprehensive evaluation, and its confidence criterion is established on the basis of the ordered evaluation sets, it will enable the evaluation results be more reliable.
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