摘要
基于中国建筑抗震设计规范(GB50011—2010)建立了中国西部工程结构全寿命期地震成本概率模型,以中国西部具体地区(宁夏银川)为样本选择地区,以该地区简化多层基础隔震框架结构为样本进行结构全寿命期地震成本计算,基于IDA增量动力分析对该地区简化多层隔震结构全寿命期地震成本采用拉丁超立方样本(Latin Hypercube Sampling,LHS)法随机分析研究,结果显示对于多层结构采用隔震技术后地震巨灾保险费率从长期来看低于常规框架结构,通过对统计结果值进行的特征分析,隔震结构的Co V值显著地低于常规框架结构。研究结果对于已有城镇多层隔震建筑与多层钢筋混凝土框架结构地震巨灾保险未来推广具有指导意义。
The objective of the work in evaluating the engineering seismic life-cycle costs of multi-story base-isolated reinforced concrete (RC) buildings located in a high seismic hazardous region (Yinchuan) in Western China. For this purpose, two kinds of multi-story RC buildings are selected. Firstly, the earthquake hazardous probability model is built through nonlinear regression function based China seismic code (GB500011-2010). Secondly, the innovative actual flow framework of seismic life-cycle cost assessment according to particular region in China has been established. The calculation of the life-cycle cost of sample buildings requires the calculation of the structural capacity in multiple earthquake hazard levels. Incremental dynamic analysis (IDA), which is considered as much efficient procedures for estimating damage of structures incorporated into the seismic cost estimation framework. In order to consider the uncertainty of structure, the material properties, the mass and the record-incident angle, the Latin Hypercube Sampling method (LHS) is integrated into the IDA procedure. Finally, the seismic catastrophe insurance premium of simplified multi-story base-isolated RC structures is obtained based on above framework analysis. The suggested seismic premium is accepted based on average income of local habitant. The statistical coefficient of variance (Cov) of structures is also shown in the research.
出处
《自然灾害学报》
CSCD
北大核心
2017年第6期46-60,共15页
Journal of Natural Disasters
基金
国家自然科学基金(51468050)
广州市科技计划项目(201707010333)~~
关键词
工程全寿命周期地震成本
多层隔震结构
增量动力分析
随机分析
拉丁超立方样本法
engineering seismic life-cycle cost assessment
multi-story based-isolated structures
incremental dynamic analysis
stochastic analysis
Latin Hypercube sampling