Quantitative Risk Assessment of Solid Waste Landfill Slope Instability-Induced Disasters Based on Depth-Integrated Method and Uncertainty Analysis: A Case Study of Shenzhen's "12•20" Guangming Landslide
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摘要: 城市固废堆填形成的高陡边坡突发失稳常伴随大量建筑损毁和人员伤亡等灾害.为定量评估人工堆填滑坡的致灾风险，以深圳“12•20”光明滑坡为典型案例，采用基于深度积分法的Massflow软件构建动态数值模型，再现了滑坡失稳启动、高速运动及堆积的演进全过程.通过耦合滑坡物质冲击压力计算方法，定量评估了滑坡运动体对周边建筑物的冲击破坏效应，进而引入不确定性分析框架，将岩土体参数及滑动面特性视为随机变量，通过拉丁超立方抽样构建概率分析模型，揭示了多重因素影响下滑坡运动距离的统计特征，并建立了滑坡运动超越概率与建筑物冲击破坏效应间的关联模型.在此基础上，考虑承灾体易损性，对建筑物及其附近人员进行风险评价.研究结果表明：深度积分法有效捕捉了滑坡的动态演化特征，模拟所得滑裂面形态、运动距离（1 139 m）及堆积形态与实际监测数据高度吻合；滑坡运动物质对建筑物的冲击压力呈现先快速增大至峰值后逐渐衰减的规律，且峰值压力随距滑坡后缘距离增加而显著减小；通过基于参数不确定性的滑坡运动距离超越概率分析，实际堆积范围完全落于计算所得95%置信区间内，建筑物损毁区域与危险性区划结果高度匹配.本研究提出的耦合深度积分法与不确定性分析的承灾风险评价框架，为人工堆填滑坡的定量风险评估提供了新方法，对“无废城市”建设中的固废堆填安全保障具有重要理论价值与实践意义.Abstract: Urban solid waste landfill-induced high-steep slope instability often leads to secondary disasters such as extensive building destruction and casualties. To quantitatively evaluate the disaster risk of artificial landfill landslides, this study takes Shenzhen's "12•20" Guangming landslide as a case study, employing the depth-integrated method-based Massflow software to construct a dynamic numerical model that reproduces the entire evolution process of landslide initiation, high-speed movement, and deposition. By coupling with impact pressure calculation methods for landslide masses, the destructive impact effects on surrounding buildings were quantitatively evaluated. An uncertainty analysis framework was introduced, treating geotechnical parameters and sliding surface properties as random variables. A probabilistic analysis model was developed through Latin hypercube sampling, revealing the statistical characteristics of landslide travel distance under multifactorial influences and establishing a correlation model between landslide motion exceedance probability and building impact damage. Building vulnerability was further considered when conducting risk assessments for structures and nearby personnel. The study demonstrates that the depth-integrated method effectively captures the dynamic evolution of landslides, with simulated failure surface morphology, travel distance (1 139 m), and deposition pattern highly consistent with field monitoring data; impact pressure from landslide masses on buildings exhibits a rapid rise to peak values followed by gradual attenuation, with peak pressure decreasing significantly with distance from the landslide source; probabilistic analysis of landslide travel distance based on parameter uncertainty shows the actual deposition zone entirely falls within the 95% confidence interval, and building damage areas align with hazard zoning results. The proposed risk assessment framework integrating depth-integrated modeling and uncertainty analysis provides a novel methodology for quantitative evaluation of engineered landfill landslides, offering significant theoretical and practical implications for solid waste landfill safety management in the context of "zero-waste city" development.
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图 2 计算模型(改编自Sun et al.2021)
Fig. 2. Calculation model (adapted from Sun et al., 2021)
图 3 滑坡运动过程模拟结果
a.滑坡运动过程；b.运动距离与平均滑速随时间变化；c.最终堆积形态对比；物质点法结果改编自王升等（2022）
Fig. 3. Simulation results of landslide movement process
图 4 滑坡物质冲击建筑物时的速度与堆积厚度时程变化
a.滑坡物质冲击建筑物的速度；b.滑坡物质冲击建筑物的堆积厚度
Fig. 4. Temporal variations in velocity and deposition thickness of landslide material passing against buildings
图 5 建筑物受冲击响应时程曲线及荷载累积效应
a.建筑物受瞬时冲击力时程曲线；b.总冲击荷载累积曲线
Fig. 5. Temporal evolution of impact forces and cumulative load effects on buildings
图 6 承灾体易损性风险评价框架主要实施步骤
Fig. 6. Implementation framework for vulnerability assessment of elements at risk
图 7 不确定模型假设检验
a.正态分布检验（h=1，拒绝原假设）；b.广义极值分布检验
Fig. 7. Uncertainty quantification of model predictions via hypothesis testing
图 8 基于超越概率的滑坡危险性区划与抽样方法验证
a.超越概率及风险区划；b.抽样方法有效性检验
Fig. 8. Landslide hazard zonation based on probability of exceedance and sampling strategy validation
图 9 基于承灾体易损性的风险评估
a.建筑物易损性评价；b.建筑物风险评价；c.建筑物附近的人口应急响应效率系数；d.建筑物附近的人口伤亡风险评价
Fig. 9. Multidimensional risk assessment incorporating vulnerability of elements at risk
表 1 模拟堆积结果对比
Table 1. Comparison of simulated stacking results
滑坡堆积对比 最大滑距(m) 堆积坡角(°) 最大堆高(m) 平均堆高(m) 本研究模拟 1 139 3.99 27.9 8.4 物质点法模拟 1 260 5.2 25.2 10.3 实际堆积 1 100 6.1 21.3 12.9 注：物质点法模拟来自王升等(2022). 
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表 2 建筑物冲击响应参数对比
Table 2. Impact response parameters of buildings under simulated landslide loading
建筑物 最大冲击速度(m/s) 首次受到冲击的时间(s) 达到最大冲击速度的时间(s) 建筑物 最大冲击速度
(m/s)首次受到冲击的时间(s) 达到最大冲击速度的时间(s) A 21.2 15 16 E 7.66 39 40 B 21.8 19 19 F 0.003 50 55 C 17.4 27 27 G - - - D 13.3 32 32
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表 3 建筑物受冲击荷载参数对比及滑坡稳定堆积特征
Table 3. Impact load parameters and stable accumulation characteristics of buildings under simulated landslide loading
建筑物 稳定堆积厚度(m) 最大冲击压力(kPa) 最大冲击荷载(MN/m) 稳定冲击荷载(MN/m) 建筑物 稳定堆积厚度(m) 最大冲击压力(kPa) 最大冲击荷载(MN/m) 稳定冲击荷载(MN/m) A 17.1 349.9 2.9 2.5 E 3.0 20.32 0.1 0.1 B 16.6 285.7 2.5 2.4 F - - - - C 8.4 133.1 0.6 0.4 G - - - - D 6.1 81.89 0.4 0.3
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表 4 随机变量的统计特征
Table 4. Statistical parameters of random variables including probability distributions and correlation coefficients
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表 5 建筑物风险等级评估参数取值
Table 5. Parameters for building risk level assessment
参数名称 参数符号 取值依据 计算公式/说明 示例值(建筑物A) 特征高度 L 层高×层数(假设均匀荷载分布) L =层高×层数 6 m×3层=18 m 临界挠度 $ {\omega }_{\mathrm{m}\mathrm{a}\mathrm{x}} $ 《建筑抗震设计规范》临界挠度比1/150 $ {\omega }_{\mathrm{m}\mathrm{a}\mathrm{x}}=L\times \left(\frac{1}{150}\right) $ 18 m×1/150=0.12 m 抗冲击
阈值$ {F}_{crit} $ 悬臂梁模型(EI为抗弯刚度，$ {\beta }_{d} $为荷载放大系数) $ {F}_{crit}=\frac{3\times EI\times {\omega }_{\mathrm{m}\mathrm{a}\mathrm{x}}}{{L}^{3}\times {\beta }_{d}} $ $ {F}_{crit}=\frac{3\times 8\times {10}^{9}\times 0.12}{{18}^{3}\times 2.0}=0.25\mathrm{ }\mathrm{M}\mathrm{N}/\mathrm{m} $ 资产价值系数 $ {C}_{i} $ 《深圳市建筑造价经济指标》、光明区2015年商品房均价(2.8万元/m²) $ {C}_{i} $ =基准单价×结构系数×功能系数×(实际面积/基准面积) 建筑物A的基准单价为2.8万元/$ {\mathrm{m}}^{2} $，实际建筑面积为1.58万$ {\mathrm{m}}^{2} $，基准建筑面积为1万$ {\mathrm{m}}^{2} $.则修正后的资产价值系数为2.8×0.8×0.9×1.58=3.185万元/$ {\mathrm{m}}^{2} $ 损伤累积效应系数 $ \mathit{γ} $ 砖混结构脆性大($ \mathit{γ} =2.0 $)，框架结构延性好($ \mathit{γ} =1.5 $) 定性赋值(基于材料力学特性) 砖混结构$ \mathit{γ} =2.0 $
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表 6 建筑物风险等级评估结果汇总
Table 6. Summary of building risk assessment results including vulnerability analysis
建筑物 位置
(m)类型 结构
类型层高
(m)层数 建筑面积
(万$ {\mathrm{m}}^{2} $)$ {10}^{4} $次抽样受到的滑坡冲击力$ {F}_{a,i}( $MN/m) 建筑物抗冲击阈值$ {F}_{crit,i} $(MN/m) 损伤累计效应系数$ \mathit{γ} $ 易损性系数$ {D}_{i} $ 资产价值系数$ {C}_{i} $ 风险值
$ {R}_{b} $A 670 工业厂房 砖混 6 3 1.58 2.3 0.25 2.0 43.24 3.2 116.87 B 745 工业厂房 砖混 6 3 1.58 2.0 0.25 2.0 25.60 3.2 52.95 C 917 仓储设施 框架 4 4 0.32 0.8 0.47 1.5 1.71 1.0 0.63 D 992 仓储设施 框架 4 4 0.32 0.7 0.47 1.5 2.08 1.0 0.56 E 1080 宿舍楼 砖混 3 5 0.60 0.4 0.36 1.8 1.31 1.5 0.35 F 1135 宿舍楼 框架 3 6 0.60 0.2 0.37 1.5 0.33 2.2 0.10 G 1172 宿舍楼 框架 3 5 0.60 0.1 0.53 1.5 0.13 2.2 0.03
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表 7 建筑物周边人口脆弱性评估参数取值
Table 7. Parameters for building risk level assessment
参数名称 参数符号 取值依据 计算公式/说明 示例值
(建筑物A)人口密度 $ \rho $ 《建筑设计防火规范》GB50016-2014及《办公建筑设计标准》JGJ/T67-2019，结合休息日傍晚场景和使用功能确定 工业厂房$ 1.0\mathrm{人}/{\mathrm{m}}^{²} $ $ 1.0\mathrm{人}/{\mathrm{m}}^{²} $ 仓储$ 0.5\mathrm{人}/{\mathrm{m}}^{²} $ 宿舍楼$ 2\mathrm{人}/{\mathrm{m}}^{²} $ 人口暴露系数 $ {V}_{i} $ 人口密度$ \rho $×空间分布系数(对于空间分布系数，假设人口分布服从正态分布，采用变异系数(CV)衡量人口分布的波动性) 低密度(工业厂房)：CV = 0.3(人员分布较分散，波动性适中) $ 1.0\times 0.3=0.30 $ 极低密度(仓储设施)：CV = 0.2(人员分布极分散，波动性低) 高密度(宿舍楼)：CV = 0.4(人员分布集中，波动性较高) 预警响应时间 $ {t}_{resp} $ $ {10}^{4} $次抽样平均首次受冲击时间(未冲击则取滑坡总时间) 不确定分析模型统计值 $ 13.9\mathrm{ }\mathrm{s} $ 撤离效率 $ {\eta }_{evac} $ 结合实际撤离速率与理论最大速率的比值设定.假设理论最大撤离速率为100人/分钟，实际平均速率为65人/分钟，则效率系数为0.65 0.65 0.65 时间敏感系数 k 假设每秒决策延误会导致撤离效率下降2% 0.02 0.02 决策延误时间 $ {t}_{d} $ 关键决策阈值常设定为20 s 20 s 20 s
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表 8 建筑物周边人口脆弱性评估结果
Table 8. Population vulnerability assessment near buildings
建筑物 位置(m) 类型 人口密度
(人/$ {\mathrm{m}}^{2} $)人口分布特征 人口暴露系数$ {V}_{i} $ 预警响应时间$ {t}_{resp,i} $(s) 应急响应效率系数$ {E}_{i} $ 风险值$ {R}_{p} $ A 670 工业厂房 1.0 低密度 0.30 13.9 74.19 18.78 B 745 工业厂房 1.0 低密度 0.30 17.0 60.66 11.77 C 917 仓储设施 0.5 极低密度 0.10 23.3 44.26 1.62 D 992 仓储设施 0.5 极低密度 0.10 27.3 37.78 1.01 E 1080 宿舍楼 2.0 高密度 0.80 32.8 31.44 4.52 F 1135 宿舍楼 2.0 高密度 0.80 41.5 24.85 2.62 G 1172 宿舍楼 2.0 高密度 0.80 45.3 22.77 1.86
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