Spatio‐temporal data mining method for joint cracks in concrete dam based on association rules

Summary Structural health monitoring (SHM) has been widely employed to reveal the hidden safety information and to diagnose the safety status in dam engineering fields. As one of the most important parameters of SHM, crack opening displacement (COD) is often used to evaluate the cracks or joints of concrete dams. In this paper, a new dam health analytic perspective is introduced by integrating the data mining method into SHM field, focusing on revealing the association rules in COD monitoring data. The association rules are investigated systematically, considering the cause–effect relations between external loads and structural response, the temporal characteristics of time series for a single sensor, the spatial characteristics of monitoring data for multisensors, and the abnormal characteristics for different items of structural responses. The association relation is quantified by proposing the quantitative indexes, including support degree, confidence degree, and promotion degree. The methods are used in the COD monitoring data of the Baishan concrete gravity‐arch dam, which is located in a severely cold area in northeastern China. Results show that 4 out of 24 cause–effect association rules are extracted by calculating the association degree of monitored COD values, and 21 out of 24 crack sensors present a temporal association relationship, among which the confidence degree of two sensors reaches 100%. The variation trend of COD values is relevant with the locations of the crack sensors. These results are consistent with the dam safety monitoring theories and models, which would be very useful for extracting the SHM information between different sensors, predicting the trend of COD value and repairing the monitoring data series of COD sensors, or even for discovering an abnormal signal for the operation safety of dams.