[目的]大断面隧道开挖易对上部建筑物的稳定性带来安全隐患,为实现对地表建筑物风险等级的准确预测,构建大断面隧道下穿建筑物风险评估模型.[方法]基于静态贝叶斯网络,建立包含地质、隧道、建筑物结构、隧道与建筑物位置关系4个一级指标及14个二级指标的风险评估模型,通过风险状态划分、专家语言模糊化处理得到先验风险概率值,并通过运用变异系数和弧间距算法得到影响地表建筑物的关键风险因素及其影响强度.进一步建立动态贝叶斯网络模型,运用Genie软件中的"Noisymax node""Strength of influence"等模块,结合白塔山隧道下穿百花亭工程案例中现场监测数据,更新模型推理结果,进而计算整个施工过程中的地表建筑物风险变化趋势.[结果]土体因素是最为关键的风险因素,隧道直径及洞顶沉降速率、周边收敛速率、洞顶累计沉降和周边累计收敛等隧道因素次之,接下来为地下水和不良地质,且地下水、不良地质和施工管理为影响强度最大的风险因素.从动态贝叶斯网络中得到的风险变化趋势数据与施工现场监测数据相比,误差仅为5.0%.[结论]本文提出的大断面隧道下穿建筑物风险评估方法,能够定量分析关键风险因素及其影响强度,结合工程监测数据,可以实现地表建筑物风险的动态预测,为类似工程提供一定理论和实践指导.
Risk assessment model and application for large cross-section tunnel underneath buildings
[Purposes]Excavation of large cross-section tunnels is prone to pose safety hazards to the stability of superstructures.In order to accurately predict the risk level of surface buildings,a risk assessment model of a large cross-section tunnel underneath buildings was constructed.[Methods]Based on the static Bayesian network,a risk assessment model was established,which included four first-level indicators including geology,tunnels,building structures,and relationship between tunnels and building locations,as well as 14 second-level indicators.Through the classification of risk status and the fuzziness of expert language,the prior risk probability values were obtained.By using the coefficient of variation(CoV)and arc spacing algorithm,the key risk factors affecting surface buildings and their strength of influence were obtained.Furthermore,the dynamic Bayesian network model was established,and the Genie software modules such as"Noisymax node"and"Strength of influence"were used to update the model reasoning results by analyzing the on-site monitoring data from the Baitashan Tunnel project underneath the Baihua Pavilion.Then,the risk change trend of surface buildings in the whole construction process was calculated.[Findings]The soil factor is the most critical risk factor,followed by the tunnel diameter,settlement rate of cave roof,convergence rate of surrounding area,cumulative settlement of cave roof,cumulative convergence of surrounding area,and other tunnel factors,with groundwater and poor geology at the lowest rank.In addition,groundwater,poor geology,and construction management are risk factors with the largest strength of influence.The risk change trend data obtained from the dynamic Bayesian network has an error of only 5.0%compared with the on-site monitoring data of construction.[Conclusions]The risk assessment method for large cross-section tunnels underneath buildings proposed in this paper can quantitatively analyze the key risk factors and their strength of influence.Combined with the engineering monitoring data,the dynamic prediction of the risk of surface buildings can be realized,which can provide some theoretical and practical guidance for similar projects.
large cross-section tunnelrisk assessmentsurface buildingBayesian networkdynamic risk evolution
万方数据
维普
