Quantitative Vulnerability Analysis of Buildings Based on Landslide Intensity Prediction
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摘要: 针对目前建筑物易损性定量评价中缺乏滑坡致灾强度预测研究，创新性地提出了一种基于InSAR技术的致灾强度经验曲线与ABAQUS二次开发的空间化位移预测相结合的建筑物易损性定量评价方法.以三峡库区石龙门滑坡为例，利用PS-InSAR解译的2017-2020年间滑坡年平均位移，通过函数反演获取了滑坡累积位移‒致灾强度经验曲线；使用ABAQUS编写荷载和孔隙水压力子程序模拟了极端工况下（库水位下降+强降雨）的滑坡累积位移，用于建筑物易损性预测计算.建筑物抗灾能力由PSO-Fuzzy AHP模型加权赋值8个指标构成，与滑坡致灾强度两部分相结合即可定量评价建筑物易损性.研究结果表明：（1）本文提出的抗灾能力评价体系能够很好表征三峡库区农村建筑物的结构特征，具备较高的评价精度；（2）基于PS-InSAR得到的上限致灾强度曲线为Ipu = 0.065 × Dtot0.236，具备更高的预测精度，有效减少了假阴性误报；（3）通过ABAQUS模拟的极端工况致灾强度随着降雨量增加而增加，预测的房屋易损性等级随之提高，并且成功预警了前期调查有明显变形的房屋.可见提出的致灾强度预测方法和易损性评价模型具有很高的空间辨识度和预警精度，通过滑坡强度信息能够开展实时建筑物易损性制图.Abstract: In view of the lack of research about landslide intensity prediction in the current quantitative vulnerability analysis of buildings, in this paper it innovatively proposes a quantitative analysis method of the combination of intensity empirical curve based on InSAR technology and spatial displacement prediction of secondary development of ABAQUS.Taking the Shilongmen landslide in the Three Gorges Reservoir area as an example, the PS-InSAR method was adopted to calculate the cumulative displacement of the landslide in 2017‒2020 and obtained the empirical curve of landslide intensity. The ABAQUS software was used to compile the subroutine of load and pore water pressure to calculate the cumulative displacement under extreme scenarios (reservoir water level drop with heavy rainfall) and predicted the vulnerability of buildings. The evaluation system of resistance was constructed by weighting eight indicators of PSO-Fuzzy AHP model, which can be combined with the landslide intensity to quantitatively evaluate the vulnerability of buildings. The results indicate follows: (1) The evaluation system of resistance proposed in this paper can well present the structural characteristics of rural buildings in the Three Gorges Reservoir area, and has high evaluation accuracy. (2) The retrieved upper-intensity curve obtained by PS-InSAR is Ipu=0.065×Dtot0.236 which has higher prediction accuracy and effectively reduces false-negative errors. (3) The landslide intensity of extreme conditions simulated by ABAQUS increases with the increase of rainfall, the predicted vulnerability level of buildings increases, and the buildings with obvious deformation in the previous investigation are successfully warned. It is concluded that the landslide intensity prediction method and vulnerability analysis method proposed in this paper has high spatial identification and early warning accuracy, and real-time vulnerability mapping of buildings can be obtained through landslide intensity information.
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Key words:
- landslide intensify /
- vulnerability /
- building /
- PS-InSAR /
- ABAQUS /
- hazard geology
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图 2 石龙门滑坡平面图(a)、建筑物裂缝(b1‒b4)、滑坡前缘滑塌(c1‒c2)、地表裂缝(c3)、滑坡前缘广场裂缝特征(d‒e)
Fig. 2. Topographical map (a), cracks on building facades (b1‒b4), some small collapses (c1‒c2) and a ground fissure (c3), deformation characteristics of public land in Shilongmen landslide (d‒e)
图 3 建筑物易损性评价流程
Fig. 3. Flow chart of the applied methodology for the vulnerability analysis of buildings
图 4 建筑物观测易损性值(a)、建筑物抗灾能力计算值(b)、2020年PS-InSAR解译位移点(c)、2017-2020四年建筑物平均位移(d)
Fig. 4. Observed vulnerability value of buildings (a), calculated value of building resistance indicators (b), PS-InSAR interpretation displacement in 2020 (c) and average displacement of buildings in 2017‒2020 (d)
图 5 致灾强度与地表总位移的拟合模型(a)、Ipu易损性值(b)、Ipm易损性值(c)、Ipd易损性值(d)
Fig. 5. Posterior intensity values and intensity model versus total ground displacement (a), vulnerability value of Ipu (b), vulnerability value of Ipm (c) and vulnerability value of Ipd (d)
图 6 库水位年变动情况(a)和Pearson Ⅲ分布重现期降雨量(b)
Fig. 6. Annual variation of reservoir water level (a) and extreme rainfall obtained from a Pearson type Ⅲ distribution (b)
图 8 模型运算结果
a.初始孔隙水压力分布（kPa）；b.等效塑性应变；c.位移矢量图；d.10年重现期降雨工况x方向位移；e.10年重现期降雨工况y方向位移；f.10年重现期降雨工况z方向位移；g.10年重现期降雨工况总位移；h.50年重现期降雨工况总位移；i. 100年重现期降雨工况总位移
Fig. 8. Model calculation results
图 9 10年重现期降雨工况总位移(a)、10年重现期降雨工况易损性值(b)、50年重现期降雨工况总位移(c)、50年重现期降雨工况易损性值(d)、100年重现期降雨工况总位移(e)、100年重现期降雨工况易损性值(f)
Fig. 9. Total displacement under 10, 50, 100-year return period rainfall scenarios (a, c, e) and vulnerability under 10, 50, 100-year return period rainfall scenarios (b, d, f)
表 1 数据收集
Table 1. Data collection
数据 尺度 来源 目的 工程地质图 1∶1 000 重庆地质勘探局及实验室试验 三维数值模拟及致灾强度计算 地形图 1∶2 000 岩土体水力力学参数 / 地勘报告 / 重庆市地勘局 建筑物抗灾能力评价体系建设 谷歌影像 0.3 m分辨率 无人机影像 4 000×3 000像素 无人机航拍 野外调查(裂缝、承灾体现状、照片和岩土取样) / 野外调查 Sentinel-1A雷达影像111景 / ASF data search 致灾强度经验曲线反演 
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表 2 建筑物现场观测易损性分类法(Del Soldato, 2017)
Table 2. Classification of observable field vulnerability affecting buildings (Del Soldato, 2017)
损失程度 裂缝宽度(CW, mm) 裂缝描述 地表损伤 观测易损性 无损伤 - 完好无损 无 0.0 可忽略的损伤 ≤1 细裂缝，通常位于不影响结构抗力的内墙或饰面中，从外面看不见. 无 0.2 轻微损伤 1 < CW≤5 基础没有变形，建筑物内的墙壁和隔墙有几处轻微裂缝，很难从外部发现. 道路、混凝土路面等坚硬表面上的薄裂缝.植被覆盖的地面上没有可见的破裂. 0.4 可承受的损伤 5 < CW≤15或存在多个CW > 3 可能影响结构强度的墙体裂缝，墙体脱节，门窗变形，排出和供水管道破裂. 从外部能够发现. 道路和结构的轻微损坏，出现裂缝、变形、分离或相对沉降. 0.6 严重损伤 15 < CW≤25 在承重结构中存在扩展裂缝，砖块错动，地板倾斜，墙壁不垂直.门窗变形过大，无法使用，墙壁明显倾斜或凸起. 地面隆起或凹陷，在土壤和草地中出现多组的张拉裂缝.沉降可能导致墙体倾斜、结构、供水管道和电缆断裂. 0.8 极度严重损伤 > 25 地板局部塌陷和承重结构存在多组贯通裂缝.墙体不垂直，结构严重变形，地板和墙壁严重开裂，门窗破损.建筑物可能发生倾倒，部分附属结构倒塌. 多组地面开裂，出现陡坎、地面隆起和沉陷.沉降会导致结构和道路出现裂缝、倾倒和变形. 1.0
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表 3 建筑物抗性指标模糊判断矩阵
Table 3. The fuzzy judgment matrix of building resistance indicators
ID BM MS RSD FD FT RB FL ANG BM (1, 1, 1) (3, 4, 5) (4, 5, 6) (2, 3, 4) (2, 3, 4) (1, 2, 3) (3, 4, 5) (1, 1, 1) MS (1/5, 1/4, 1/3) (1, 1, 1) (1, 1, 1) (1/3, 1/2, 1) (1/4, 1/3, 1/2) (1/4, 1/3, 1/2) (1, 1, 1) (1/4, 1/3, 1/2) RSD (1/6, 1/5, 1/4) (1, 1, 1) (1, 1, 1) (1/4, 1/3, 1/2) (1/4, 1/3, 1/2) (1/5, 1/4, 1/3) (1/3, 1/2, 1) (1/5, 1/4, 1/3) FD (1/4, 1/3, 1/2) (1, 2, 3) (2, 3, 4) (1, 1, 1) (1, 1, 1) (1/4, 1/3, 1/2) (1, 1, 1) (1/4, 1/3, 1/2) FT (1/4, 1/3, 1/2) (2, 3, 4) (2, 3, 4) (1, 1, 1) (1, 1, 1) (1, 1, 1) (1, 2, 3) (1/3, 1/2, 1) RB (1/3, 1/2, 1) (2, 3, 4) (3, 4, 5) (2, 3, 4) (1, 1, 1) (1, 1, 1) (2, 3, 4) (1, 1, 1) FL (1/5, 1/4, 1/3) (1, 1, 1) (1, 2, 3) (1, 1, 1) (1/3, 1/2, 1) (1/4, 1/3, 1/2) (1, 1, 1) (1/4, 1/3, 1/2) ANG (1, 1, 1) (2, 3, 4) (3, 4, 5) (2, 3, 4) (1, 2, 3) (1, 1, 1) (2, 3, 4) (1, 1, 1) 注：建筑材料(BM)、翻修状态(MS)、使用年限与设计年限比值(RSD)、基础深度(FD)、基础类型(FT)、是否存在地圈梁(RB)、楼层数(FL)、主滑方向与建筑物轴向的夹角(ANG).
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表 4 指标赋值及权重
Table 4. The value and weight of building resistance indicators
指标 分类 Ri取值 权重 建筑结构 木质结构 0.10 0.258 砖石结构 0.30 砖混结构 0.50 钢筋混凝土结构 0.70 钢结构 1.00 翻修状态 无变形 1.00 0.063 轻微损伤 0.75 中度损失 0.50 严重损伤 0.25 使用年限与设计年限比值 < 0.1 1.00 0.046 [0.1, 0.4) 0.80 [0.4, 0.6) 0.70 [0.6, 0.8) 0.50 [0.8, 1.0) 0.30 [1.0, 1.2) 0.10 ≥1.2 0.05 基础深度(m) [0, 0.5) 0.30 0.081 [0.5, 1.0) 0.50 [1.0, 2.0) 0.80 ≥2.0 1.00 基础类型 毛石 0.30 0.100 毛石+泥土 0.60 毛石+混凝土 1.00 是否存在地圈梁 存在 1.00 0.175 不存在 0.00 楼层数 单层 0.30 0.074 双层 0.60 三层及以上 1.00 主滑方向与建筑物轴向的夹角 [0, 5) 0.20 0.202 [5, 15) 0.40 [15, 30) 0.60 [30, 45) 1.00
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表 5 3种指标反演易损性等级比较
Table 5. Comparison vulnerability levels retrieved by three indicators
易损性等级 极低 低 中 高 现场观测易损性 0 2 7 9 Ipu 0 1 6 11 Ipm 0 5 9 4 Ipd 3 12 3 0
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表 6 计算参数
Table 6. Material parameters
名称 干密度(KN/m3) 弹性模量(MPa) 泊松比 孔隙比 渗透系数(m/h) 粘聚力(kPa) 内摩擦角(°) 堆积土 18.99 30 0.3 0.35 0.068 18 20 基岩 27.90 2.7e4 0.25 0.01 1e-7 1e3 30
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表 7 重点房屋不同工况下易损性变化
Table 7. Vulnerability changes of key houses under different scenarios
房屋代号 抗灾能力R 前期累计位移(mm) 10年预测位移(mm) 50年预测位移(mm) 100年预测位移(mm) 10年易损性等级 50年易损性等级 100年易损性等级 b1 0.446 205.81 115.82 245.13 365.20 高 高 高 b2 0.388 300.71 110.32 265.48 321.42 极高 极高 极高 b3 0.430 251.67 112.65 248.72 288.55 高 高 高 b4 0.428 443.01 141.21 276.66 352.32 高 极高 极高 b5 0.669 0 66.89 211.15 249.58 极低 低 低 b6 0.758 0 64.33 185.25 226.35 极低 极低 极低 b7 0.451 0 165.21 311.25 365.52 中 高 高 b8 0.416 0 85.21 225.21 265.21 低 高 高
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