Applying Advanced Data Analytics and Machine Learning to Enhance the Safety Control of Dams

Rico, João; Barateiro, José; Mata, Juan; Antunes, António; Cardoso, Elsa

doi:10.1007/978-3-030-15628-2_10

Cited by 15 publications

(13 citation statements)

References 54 publications

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“…The dam safety monitoring models reviewed above were all formulated under normal service conditions. Specifically, this concerns two aspects: (a) the dam is exposed to a normal environment without unexpected and F I G U R E 1 Internal and external factors affecting operation behavior of concrete dams (Rico, Barateiro, Mata, Antunes, & Cardoso, 2019) abrupt changes (e.g., sudden changes in water level and/or temperature), and (b) the dam body and its foundation meet design code requirements and maintain reasonable structural integrity. However, these two conditions are not always present in real-world dams.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

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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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Section: Background and Literature Reviewmentioning

confidence: 99%

“…Internal and external factors affecting operation behavior of concrete dams (Rico, Barateiro, Mata, Antunes, & Cardoso, 2019)…”

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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“…Deep learning is a sub-area of ML methods that can express much more complex relationships by adding more layers and nonlinear elements in a layer [27]. The feature learning of deep learning is realized through a general-purpose learning mechanism instead of time-consuming human manual feature extraction or expert domain knowledge [28]. Deep learning techniques have been widely utilized to solve practical problems and achieved state-of-the-art or highly competitive results [29].…”

Section: Stacked Long-short Term Memory Neural Networkmentioning

confidence: 99%

The Prediction of Dam Displacement Time Series Using STL, Extra-Trees, and Stacked LSTM Neural Network

Gong

Shu

et al. 2020

IEEE Access

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Dam behavior prediction is a fundamental component of dam structural health monitoring. By comparing the predictions and the observations, anomalies can be detected, and then the remedial measures can be executed in time. As the most intuitive monitoring indicators, deformation is often used to evaluate dam structural health status. In this research, we propose a novel combined model for predicting the dam displacement time series. First, the seasonal-trend decomposition based on Loess (STL)method is utilized to decompose the dam displacement time series into seasonal, trend, and remainder components. Then the extremely randomized trees(extra-trees) model is used to predict seasonal components based on the causal models and influencing factors, whereas the stacked Long-Short Term Memory (LSTM)model is used to predict trend and remainder components based on the numerical models and historical observation data. Finally, the predicted results of the three components are aggregated to obtain the total predicted dam displacement. Seven state-of-the-art methods are introduced as benchmark methods to verify the effectiveness and feasibility of the proposed model. To quantitatively evaluate and compare the prediction results, three evaluation indicators, and a statistic test method are introduced. The experimental results show that the proposed model is the best-performing method compared with other benchmark methods both in prediction accuracy and stability. This indicates the proposed novel combined model STL-extra-trees-LSTM is a promising method for predicting displacement time series. INDEX TERMS Dam behavior prediction, time series decomposition, deep learning, ensemble learning.

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“…Multiple linear regression and stepwise regression are commonly used in statistical models. Multiple linear regression models have a long history and many applications . In multiple linear regression, the output is computed as a linear combination of the inputs .…”

Section: Monitoring Modelsmentioning

confidence: 99%

“…In the process line method, a process line of the monitoring data collected at different measurement points is constructed. Then, it is determined whether there are abnormal values or abnormal procedures in the data according to expert experience . This method is simple, convenient, and is suitable for small monitoring data sets or cases in which the monitoring model has not yet been established.…”

Section: Abnormal Value Detectionmentioning

confidence: 99%

Dam monitoring data analysis methods: A literature review

Yang

2019

Struct Control Health Monit

100

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Dam monitoring usually involves environmental variables (e.g., the water level and temperature) and effect variables (deformation, cracking, seepage, etc.).The associated monitoring data can reflect the trends in these variables over time and are important information for managers to understand the operational status of a dam. Therefore, research on monitoring data analysis methods is very important for monitoring dam safety. Dam monitoring data analysis methods can be divided into monitoring model, monitoring index, and abnormal value detection methods. A monitoring model takes environment variables as independent variables and effect variables as dependent variables. By studying the interactions among variables, the trends of effect variables can be learned for monitoring and prediction. A monitoring index is established to denote warning or extreme value considering the previous changes in effect variables to determine whether future changes are safe.Abnormal value detection is also an important method of finding abnormal changes in the dam state. This paper summarizes the principles, research progress, deficiencies, and development trends of these three types of monitoring data analysis methods. This review promotes research in the field of dam safety monitoring.

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Applying Advanced Data Analytics and Machine Learning to Enhance the Safety Control of Dams

Cited by 15 publications

References 54 publications

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

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

The Prediction of Dam Displacement Time Series Using STL, Extra-Trees, and Stacked LSTM Neural Network

Dam monitoring data analysis methods: A literature review

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