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

Abstract

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.

Identification and reconstruction of anomalous data in dam monitoring considering temporal correlation

“…Such anomalous data is an important indicator of feedback with the condition of the dam itself and should not be removed. There is some correlation between the different monitoring parameters of the dam [14]. If two monitoring parameters that correlate with each other both show anomalies, this type of error is not due to human measurement error or inaccurate monitoring instruments [15], but to anomalous data that actually reflects the sudden change in system condition.…”

An Explainable Probabilistic Model for Health Monitoring of Concrete Dam via Optimized Sparse Bayesian Learning and Sensitivity Analysis

“…Various ML methods have been utilized for data-driven modeling of dam structural behavior, such as feed-forward neural networks [6][7][8][9], extreme learning machines [10][11][12], recurrent neural network (RNN) [13][14][15], support vector regression (SVR) [16][17][18][19], Gaussian process regression [20][21][22], and decision treesbased ensemble models [23][24][25]. Besides, some novel datadriven methods or models have been proposed for dam health monitoring, including switching Kalman flter [26], dynamic time warping [27], panel data model [28], cloud model [29], correlated multi-target stacking [30], and spatiotemporal association mining [31]. Recently, the concept of automated machine learning (AutoML) has been also applied in dam response prediction.…”

A Spatial Model for Repairing of the Dam Safety Monitoring Data Combining the Variable Importance for Projection (VIP) and Cokriging Methods

“…However, affected by factors such as short-term abnormalities in monitoring instruments, monitoring instrument replacement, measurement errors, and external environmental disturbances [2], dam safety monitoring data is prone to data omissions, oscillation fluctuations, response misalignment, and other data anomalies. The occurrence of these anomalies will affect the continuity and reliability of the monitoring sequence, cause misjudgment of the dam's operational state, and even endanger the safe operation of the dam [3]. Therefore, it is essential to build a high-precision model for repairing dam safety monitoring data to control the general law and development trend of dams in real time for the intelligent control of dam safety operation [4,5].…”