【作者】 綦甲松； 姚爱军；

【Author】 QI Jiasong;YAO Aijun;Faculty of Architecture, Beijing University of Technology;

【机构】 北京工业大学城建学部；

【摘要】 基坑施工风险因素复杂且动态性变化,考虑评价指标的模糊性和实时性评估才更符合施工需求。同时,现监测预警以单一超限作为判断依据仍存有缺陷,给基坑整体风险的影响程度不明,实现多源化信息的客观融合亟需研究。为此,本文构建了云模型-改进D-S证据理论(包括冲突信息)风险评估方法,利用重点风险源监测数据进行正态云模型指标转化,得到多指标与各风险等级的关联度,避免主观判断;再标准化为各证据基本可信度分配概率并权重优化冲突,规避多源证据之间的冲突性;最终以D-S证据理论完成多源化信息融合。以北京城市副中心01标段超大逆作基坑为实例,应用结果表明:在监测反馈存有冲突且模糊性的情况下,该算法仍可客观评判案例基坑综合风险为Ⅱ级,与实际相符,为基坑多源监测数据的风险评估提供有效途径。更多还原

【Abstract】 The risk factors of pit construction are complex and dynamic, and the fuzzy and real-time assessment of evaluation indexes is considered to be more in line with the construction needs. At the same time, the current monitoring and early warning to a single exceeding the limit as the basis for judgment is still defective, to the overall risk of the pit to the extent of the impact of the unknown, the realization of the objective integration of multi-source information urgently needs to be researched. Therefore, this paper constructs a cloud model-improved D-S evidence theory(including conflict information) risk assessment method, and uses the monitoring data of key risk sources to transform the normal cloud model indicators to obtain the correlation degree between multiple indicators and each risk level, avoiding subjective judgment. Re-standardization is used to assign probability to the basic credibility of each evidence and optimize the conflict of weight, so as to avoid the conflict between multi-source evidence. Finally, D-S evidence theory is used to complete multi-source information fusion. Taking the super-large top-down foundation pit of No.01 section of Beijing Sub-Center as an example, the application results show that under the condition of conflicting and ambiguous monitoring feedback, the algorithm still comprehensively and objectively evaluates the foundation pit risk of the case as grade II,which is consistent with the actual situation, and provides an effective way for the risk assessment of multi-source monitoring data of foundation pit.更多还原