针对当前大坝安全监控连续时空监测能力弱、单测点馈控范围小的不足,在充分挖掘大坝原型监测数据的基础上,发展了一种弱化主观干扰的RFM(Recency Frequency Magnitude)自适应大坝性态评价模型。首先,模型结合大坝行为的强周期性时序特征,提出“中层型”和“底层型”监测序列的概念;其次,引入K-means聚类算法实现自适应划分监测序列类别;最后,基于RFM指标评分体系,明确各类别所表征的工程健康状态,建立大坝性态的安全评价体系。以某大坝水平位移监测资料为例,详细展示了所提出的大坝运行性态评价模型的应用流程。工程实例表明,该模型评价合理,客观反映了大坝服役状态,有效减少了评价过程中的经验性活动。

Present dam safety monitoring has shortcomings of weak continuous spatio-temporal monitoring ability and small feed-control range of single measuring point. In view of this, a dam performance evaluation RFM (Recency Frequency Magnitude) model with weakened subjective interference is developed on the basis of fully mining the dam’s prototype monitoring data. First, the concept of “middle type” and “bottom type” monitoring sequence is proposed based on the strong periodicity of dam behavior. Second, K-means clustering algorithm is introduced to classify monitoring sequence adaptively. Finally, the safety evaluation system of dam behavior is established on the basis of defining the project health status represented by various categories in line with the RFM index scoring system. The application of RFM model is illustrated with the horizontal displacement monitoring data of a dam as an example. The project example demonstrate that the evaluation of this model is reasonable and objectively reflects the service state of the dam, and also effectively reduces experiential activities in the evaluation process.