管线渗漏是城市道路塌陷事故的重要诱因，明确道路塌陷的演化规律与灾变机理是安全事故防控的关键。针对北京市政道路污水管线，采用离散元-流体力学（DEM-CFD）流固耦合方法，在室内试验基础上标定模型细观参数，模拟管线不同破损位置和埋深条件下，砂土地层变形损失规律和空洞演化规律，重点分析颗粒位移、密实度和介质流量等参数在空洞形成过程中的变化特征。研究结果表明：管线顶部和腰部渗漏时，砂土层随着水和土体的流失会形成漏斗状的空洞。在无交通荷载作用下，路面基本不发生沉降。结合颗粒位移及密实度变化规律，可将地层变形破坏分为稳定区、疏松区和空洞区，并建立了疏松区的椭圆分区模型。根据颗粒损失率将地层渐进破坏过程分为颗粒运移阶段、快速损失阶段和缓慢收敛阶段。在空洞形成时间、沉槽范围、颗粒流失速度和总量等指标方面，腰部渗漏工况为最大值，顶部渗漏次之，底部渗漏为最小值，但底部渗漏引发的疏松区的范围最大。研究结果可对城市道路致塌地下灾害风险检测与识别提供理论支持。

Pipeline leakage is a major cause of urban road collapse accidents. Understanding the evolution process and catastrophic mechanisms of road subsidence is crucial for preventing such safety incidents. Focusing on sewage pipelines in Beijing municipal roads, this study employs DEM-CFD(discrete element method-computational fluid dynamics) coupled flow-solid approach. Microscopic model parameters were calibrated based on laboratory experiments to simulate deformation and cavity evolution in sandy soil layers under various pipeline leakage locations and burial depths. Key parameters, including particle displacement, soil compactness, and medium flow, were analyzed during cavity formation. The results indicate that leakage at the top and middle of the pipeline leads to the formation of a funnel-shaped cavity as water and soil are lost. Without traffic load, the road surface exhibits negligible settlement. By analyzing particle displacement and compactness variations, the soil deformation was divided into stable, loose, and cavity zones, and an elliptical partition model was established for the loose zone. Based on the particle loss rate, the progressive failure process of the soil was classified into three stages: particle migration, rapid loss, and gradual convergence. In terms of cavity formation time, subsidence extent, particle loss rate, and total particle loss, leakage at the pipeline's middle section yielded the highest values, followed by the top section, with the lowest at the bottom section. However, bottom leakage resulted in the largest loose zone. These findings provide theoretical support for detecting and identifying underground risks associated with urban road collapse disasters.