Review of Physical Vulnerability Assessment of Building Based on Indicator Method
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摘要: 承灾体脆弱性评估是科学进行灾害风险评估和预测的基础，房屋建筑作为面大量广的承灾体，众多学者对建筑物理脆弱性指标模型进行了研究。基于单灾种和多灾种2个维度，针对指标模型构建的各环节，全面梳理了几种典型单灾种物理脆弱性指标体系和评估模型构建情况，发现指标选取理论依据不明确，模型构建主观性较强，不能准确表征建筑特点与抗灾能力间的内在联系。系统总结了多灾种指标体系和耦合物理脆弱性指标模型研究现状，发现多灾种之间及其对承灾体影响的复杂耦合效应在现有指标模型中未得到充分体现。研究结果表明，明晰指标依据、优化模型构建是提升单灾种物理脆弱性评估准确性的关键；改进脆弱性耦合模型、拓展综合脆弱性评估方法是健全多灾种脆弱性评估研究的核心。Abstract: Vulnerability assessment of disaster bearing bodies is the basis of disaster risk assessment and prediction. Building is a kind of disaster bearing body vast in number and wide in scale, the research of physical vulnerability index model of buildings has attracted the interest of many researchers. The construction process of indicator-based method was introduced from the two dimensions of single-hazard and multi-hazard. The physical vulnerability index system and the index model construction of several typical single-hazard are comprehensively sorted out, and it is recognized that the scientific basis of model construction is not clear and the model construction is highly subjective which cannot accurately represent the internal relationship between the building characteristics and the ability to resist disasters. The research status of the physical vulnerability coupling index model for the triggering effect of multi-hazards was summarized, and it is found that coupling effects and their impacts on disaster bearing body are not fully reflected in the existing model. The research shows that optimizing index model and elaborating basis of indicators are the key to improve the effectiveness of single-hazard physical vulnerability assessment. Perfecting indicator system, improving coupled vulnerability model and developing the comprehensive vulnerability assessment method are the core of the research on multi-hazard vulnerability assessment.
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Key words:
- Physical vulnerability /
- Index model /
- Single-hazard /
- Multi-hazard /
- Coupling effect /
- Comprehensive vulnerability
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表 1 脆弱性等级划分标准
Table 1. Classification rule of vulnerability level
等级划分 脆弱性指数划分 涉及灾种 划分特点 来源 低、中、高 [0—0.33]、[0.34—0.66]、[0.67—1.00] 洪水 等间隔划分脆弱性指数，无脆弱等级描述 Yankson等（2017） 轻微破坏、中等破坏、大量破坏、完全毁坏 [0.1—0.3]、[0.4—0.6]、[0.7—0.8]、[0.9—1.0] 泥石流 按照破坏等级分配脆弱性指数，破坏等级与HAZUS地震方法中定义的破坏状态相似 Kang等（2016） 基本完好、轻微破坏、中等破坏、严重破坏、完全破坏 [0.1—0.2]、[0.2—0.4]、[0.4—0.6]、[0.6—0.8]、[0.8—1.0] 滑坡 等间隔划分脆弱性指数，根据专家调查确定破坏等级 guillard-gonalves等（2016） 极低、低、中、高、极高 [0—0.2]、[0.2—0.4]、[0.4—0.6]、[0.6—0.8]、[0.8—1.0] 多灾种 等间隔划分脆弱性指数，脆弱等级描述结合已有研究及专家意见 卢颖等（2017） 
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表 2 泥石流物理脆弱性评估方法
Table 2. Assessment method of physical vulnerability of debris flow
作者 研究区域 指标体系 模型方法 评估特点 Jean-Claude等（2014） 秘鲁南部阿雷基帕46个城市街区的约1000座建筑物 建筑物类型、建筑物材料、楼层数、维护状态、屋顶类型、位置和角度、基岩类型、冲积阶地的存在、坡度 主成分分析法（PCA） 指标选择较详细，分析方法降低了主观性，消除评估指标间的相关性影响。模型还适用于其他城市泥石流脆弱性研究，可应用性较强 Papathoma-Köhle等（2003） 意大利南蒂罗尔马尔泰地区51座建筑物 建筑物材料、维护状态、楼层数、周围街道树木朝向 专家判断法 指标选择上较宽泛，分析方法人为色彩较浓，但将指标法与脆弱性曲线结合，对于改进物理脆弱性评估方法具有重要作用 Ding等（2012） 云南省昆明市东川区小江流域 结构类型、建设年代、楼层数、住房面积 SOM神经网络法 由于研究目的为风险评估，物理脆弱性评估因子选择较简单，缺乏对灾害作用下环境特征指标的综合考虑 庞金彪（2017） 岷江上游山区各县乡 建筑物功能、建筑物结构、建筑物材料、建筑物面积、建筑物到最近泥石流沟的距离 SOM神经网络法 选择了建筑物功能指标，但建筑物功能、建筑物材料、建筑物结构间存在相关性，其对脆弱性的影响还需进一步探讨。SOM模型在数据分类中主观干扰小，聚类结果合理
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表 3 滑坡物理脆弱性评估模型
Table 3. Physical vulnerability assessment model of landslide
作者 模型类型 指标体系 脆弱性模型 Papathoma- Köhle等（2007） 定性模型 建筑物材料（a）、楼层数（b）、周围环境（c）、滑坡面是否开窗（d）、是否存在潜在滑坡危险（e） $V = 5a + 4b + 3c + 2d + e$
式中，$V$为物理脆弱性，$a-e$为各指标得分Uzielli等（2008） 定量模型 结构类型${\xi _{{\rm{STY}}}}$、维护状态${\xi _{{\rm{SMN}}}}$ $ V=I·S$ $S = 1 - (1 - {\xi _{{\rm{STY}}}})(1 - {\xi _{{\rm{SMN}}}})$
式中，V为物理脆弱性，I为滑坡强度，S为建筑物易感性，${\xi _{{\rm{STY}}}}$为结构类型指标得分，${\xi _{{\rm{SMN}}}}$为维护状态指标得分Li等（2010） 定量模型 建筑物材料${\xi _{{\rm{SFD}}}}$、建筑物高度${\xi _{{\rm{STY}}}}$、建造年代${\xi _{{\rm{Smn}}}}$、基础深度${\xi _{{\rm{Sht}}}}$ $ V=f(I·R)$ $ R=\sqrt[4]{{\xi }_{\rm{SFD}}·{\xi }_{\rm{STY}}·{\xi }_{\rm{Smn}}·{\xi }_{\rm{Sht}}}$
式中，V为物理脆弱性，I为滑坡强度，R为建筑物抵抗力；${\xi _{{\rm{SFD}}}}$、${\xi _{{\rm{STY}}}}$、${\xi _{{\rm{Smn}}}}$、${\xi _{{\rm{Sht}}}}$为各指标得分Silva等（2014） 定量模型 结构类型CT、建筑物材料CM、楼屋面板材料FRS、楼层数NF、维护状态CS $ PV=LM·(1-BR)$
$BR = 0.3CT + 0.3CM + 0.2FRS + 0.1NF + 0.1CS$
式中，PV为物理脆弱性，LM为滑坡强度，BR为建筑物抵抗能力
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表 4 洪水物理脆弱性评估模型
Table 4. Physical vulnerability assessment model of flood
作者 研究目标 指标体系 权重方法 评估特点 Fernandez等（2016） 社会、经济、环境、物理脆弱性 建筑物密度、楼层数、建造年代、结构类型 主成分分析法（PCA） 分析方法提高了指标选择和数量灵活性，但指标选取上略显粗糙 Müller等（2011） 社会、物理脆弱性 建筑物材料、建筑物位置、植被覆盖比例、排水设施 专家判断 进行了指标权重敏感性分析，结果表明指标对权重值的变化不敏感，指标选取具有合理性 Uwakwe（2015） 物理脆弱性 墙体材料、建筑物高度、楼层数、建造年代、保存状况 专家判断 指标权重赋值时对不同领域专家分值进行了综合，其中建筑物高度被认为是影响洪水脆弱性最重要的因素 Yankson等（2017） 物理、社会、环境脆弱性 房屋类型、房屋材料（地板、屋顶、外墙材料）、排水设施、建筑物位置（高程、坡度、到海岸线的距离） 专家判断 指标选取上用高程、坡度、海岸线距离作为量化评估因子，改善了定性评估的不确定性 Krellenberg等（2017） 社会、环境、物理脆弱性 建造质量、结构类型、是否有防护墙、屋顶类型、植被覆盖程度 专家判断 屋顶类型指标选取不合理，因其与洪水脆弱性的相关性较低。研究认为结构类型、植被覆盖程度是影响脆弱性的主要驱动因素 Romanescu等（2016） 物理、人口脆弱性 建筑物距河道的距离、水利工程存在与否、建筑物材料、建筑物质量、建筑物用途 专家判断 建筑物距河道的距离指标被赋予了更高的权重，作为影响脆弱性的主要原因
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