An online anomaly recognition and early warning model for dam safety monitoring data

Li, Xing; Li, Yanling; Lü, Xiang; Wang, Yongfei; Zhang, Han; Zhang, Peng

doi:10.1177/1475921719864265

Cited by 50 publications

(22 citation statements)

References 32 publications

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“…Usually, the anomalous data instances are defined as those signals outside the threshold. In the field of SHM, the Pauta criterion (also known as 3 σ criterion) is a well-known statistical method to calculate abnormal boundaries of warning index based on the distribution of parameters under different operational conditions (Li et al, 2019; Liang et al, 2018; Shi et al, 2019; Zhu et al, 2019). The Pauta criterion is applicable to large sample data such as SHM measurements, mean value, and standard deviation of the index are first calculated.…”

Section: Methodology For Cable Anomaly Warningmentioning

confidence: 99%

A cointegration approach for cable anomaly warning based on structural health monitoring data: An application to cable-stayed bridges

Fan

Huang

Ren

et al. 2020

Advances in Structural Engineering

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For long-span cable-stayed bridges, cables are one of the most important components to resist various actions. With the application of structural health monitoring technique, real-time recording of cable forces is achieved, and hence, the warning system on cable anomaly established. However, it is still difficult and there are challenges to conduct the warning system effectively, especially due to the phenomena of false alarm or omission. A practical reason is the warning index’s sensitivity to the ambient environment. Temperature variations, for instance, usually disturb the force-based cable anomaly warning and result in the false evaluation of structural condition. In view of eliminating the effects of environmental temperature, cointegration, a statistical concept from econometrics, is employed in cable anomaly warning studies. An approach that extracts warning index by linear combination of two non-stationary time series using the cointegration algorithm is developed in order to produce a more stationary cointegrated residual series (warning index series). The calculated stationary relationship between two time series is insensitive to the influence of environmental temperature and is capable of cable anomaly warning. Specifically, the framework of the cable anomaly warning system is first proposed. Subsequently, time-series test methods are introduced to check the non-stationary order and calculate the cointegration parameters of measured cable forces and environmental temperature. The computed cointegrated residual series is fed into statistical analysis as a warning index and the procedure of cable anomaly warning under the influence of environmental temperature is illustrated in detail. Finally, a case study for a cable-stayed bridge is demonstrated with results and discussions.

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Section: Methodology For Cable Anomaly Warningmentioning

confidence: 99%

A cointegration approach for cable anomaly warning based on structural health monitoring data: An application to cable-stayed bridges

Fan

Huang

Ren

et al. 2020

Advances in Structural Engineering

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“…Among these, there are 113 concrete dams out of a total of 215 dams with a height above 100 m, and 12 concrete dams out of a total of 20 dams with a height above 200 m (Jia, 2013). A concrete dam failure will cause catastrophic loss to civil infrastructure, property and assets, and human life (X. Li, Li, et al., 2019; Ni, Zhang, & Noori, 2019). Fortunately, such a failure rarely occurs suddenly, but rather evolves over a long period of time; thus, continuous monitoring and evaluation of concrete dam safety is critical in preventing such catastrophic events (Curt, Peyras, & Boissier, 2010; Su, Wu, & Wen, 2007).…”

Section: Introductionmentioning

confidence: 99%

A robust prediction model for displacement of concrete dams subjected to irregular water‐level fluctuations

Ren

et al. 2021

Computer aided Civil Eng

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Monitoring and predicting the displacement of concrete dams is one of the most crucial considerations for ensuring their long-term safe operation. Most existing models are designed for dams subjected to common structural and environmental conditions, with little attention paid to atypical operational conditions, such as structural strengthening or sudden changes in the external environment. The motivation for this work is to develop a reliable prediction model for displacement behavior of concrete dams subjected to irregular upstream water-level fluctuations. In our study, a reconstruction method for unevenly sampled time series is presented to analyze such data. Then, an improved model, factor weighted support vector regression (FWSVR), which differentiates various factors and their effects through a weighting matrix, is theoretically derived. The weights are determined by the RReliefF algorithm, and together with FWSVR form the final RReliefF-based FWSVR (RFWSVR) model. In particular, the hyperparameters involved in the above modeling strategy are optimized by the Grey Wolf Optimizer. Eventually, the prediction robustness of the developed model was verified on the data from four representative monitoring points of a real-world dam, where its accuracy was compared to classical dam behavior modeling methods, FWSVR models using other weighting methods, and an ensemble learning algorithm. Comparative evaluation of the performance of the different methods was conducted with the help of recognized statistical indices. The evaluation results show that the overall performance of the proposed RFWSVR model is optimal for the displacement prediction at the selected points when the dam case is subjected to irregular water-level changes. This novel modeling approach may be generalized for modeling the evolution behavior of other civil or hydraulic structures.

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“…Commonly used approaches include autoregressive (AR) models, neural network regression, and kernel regression models (such as support vector regression). 19,20 The neural network methods have the limitations of the computational complexity and lack of interpretability. Besides, the kernel-based approaches are computationally expensive when handling too many features.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection of structural health monitoring data using the maximum likelihood estimation-based Bayesian dynamic linear model

Zhang

Wang

Wan

et al. 2020

Structural Health Monitoring

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Enormous data are continuously collected by the structural health monitoring system of civil infrastructures. The structural health monitoring data inevitably involve anomalies caused by sensors, transmission errors, or abnormal structural behaviors. It is important to identify the anomalies and find their origin (e.g. sensor fault or structural damage) to make correct interventions. Moreover, online anomaly identification of the structural health monitoring data is critical for timely structural condition assessment and decision-making. This study proposes an online approach for detecting anomalies of the structural health monitoring data based on the Bayesian dynamic linear model. In particular, Bayesian dynamic linear model, consisting of various components, is implemented to characterize the feature of real-time measurements. Expectation maximization algorithm and Kalman smoother are combined to estimate the Bayesian dynamic linear model parameters and generate log-likelihood functions. The subspace identification method is introduced to overcome the initialization issue of the expectation maximization algorithm. The log-likelihood difference of consecutive time steps is then used to determine thresholds without introducing extra anomaly detectors. The proposed Bayesian dynamic linear model-based approach is first illustrated by the simulation data and then applied to the structural health monitoring data collected from two long-span bridges. The results indicate that the proposed method exhibits good accuracy and high computational efficiency and also allows for reconstructing the strain measurements to replace anomalies.

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An online anomaly recognition and early warning model for dam safety monitoring data

Cited by 50 publications

References 32 publications

A cointegration approach for cable anomaly warning based on structural health monitoring data: An application to cable-stayed bridges

A cointegration approach for cable anomaly warning based on structural health monitoring data: An application to cable-stayed bridges

A robust prediction model for displacement of concrete dams subjected to irregular water‐level fluctuations

Anomaly detection of structural health monitoring data using the maximum likelihood estimation-based Bayesian dynamic linear model

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