Prediction of the intermediate block displacement of the dam crest using artificial neural network and support vector regression models

Tabari, Mahmoud Mohammad Rezapour; Sanayei, Hamed Reza Zarif

doi:10.1007/s00500-018-3528-8

Cited by 45 publications

(14 citation statements)

References 45 publications

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“…In this study, a nonequal interval time-series reconstruction method (NTRM) is presented for data homogenization. In addition, some researchers suggested that SVR has a clear advantage in solving small-sample, high-dimensional, and nonlinear approximation problems due to its solid theoretical basis (Ranković et al, 2014;Su et al, 2017;Tabari & Sanayei, 2019;Wei et al, 2020). Thus, the SVR-based approach can provide a promising alternative for dam displacement modeling.…”

Section: Research Ideas and Contributionsmentioning

confidence: 99%

“…In this study, a nonequal interval time‐series reconstruction method (NTRM) is presented for data homogenization. In addition, some researchers suggested that SVR has a clear advantage in solving small‐sample, high‐dimensional, and nonlinear approximation problems due to its solid theoretical basis (Ranković et al., 2014; Su et al., 2017; Tabari & Sanayei, 2019; Wei et al., 2020). Thus, the SVR‐based approach can provide a promising alternative for dam displacement modeling. The existing SVR‐based models are mostly customized for dams that are subject to common environmental conditions, with little attention paid to irregular water‐level changes (Kang, Li, & Dai, 2019; M. Li, Ren, et al., 2019; Su, Li, et al., 2016; Su et al., 2018; Su, Wen, Chen, & Tian, 2016).…”

Section: Introductionmentioning

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: Research Ideas and Contributionsmentioning

confidence: 99%

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

View full text Add to dashboard Cite

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“…ANNs are also categorized based on the direction of information flow and processing (Maier et al 2010). For instance, in a feed-forward neural network, also known as multi-layer perceptrons (MLP) information passes from the input nodes to the output nodes only (Hornik 1989;Tabari and Zarif Sanayei 2019). This is in contrast to a recurrent ANN in which information flows through the nodes in both directions, from the input to the output nodes and vice versa (Malekpour and Tabari 2020).…”

Section: Step Ii: Selection Of the Network Architecturementioning

confidence: 99%

An Innovative Approach to Determination of Double-Porosity Fractured Aquifers Hydraulic Parameters Using Artificial Neural Network

Azari

Tabari

2021

Preprint

Self Cite

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Accurate determination of hydraulic parameter values is the first step to the sustainable development of an aquifer. Since Theis (1935), type curve matching technique (TCMT) has been used to estimate the aquifer parameters from pumping test data. The TCMT is subjected to graphical error. To eliminate the error an Artificial Neural Network (ANN) is developed as an alternative to the conventional TCMT by modeling the Bourdet-Gringaten’s well function for the determination of the fractured double porosity aquifer parameters. The neural network model is developed in a six-step protocol based on multi-layer perceptron (MLP) networks architecture and is trained for the well function of double porosity aquifers by the back propagation method and the Levenberg-Marquardt optimization algorithm. By applying the principal component analysis on the training input data and through a trial-and-error procedure the optimum structure of the network is fixed with the topology of [3×6×3]. The replicative, predictive and structural validity of the developed network are evaluated with synthetic and real field data. The developed network provides an automatic and fast procedure for the double porosity aquifer parameter determination that eliminates graphical errors inherent in the conventional TCMT. The network receives pumping test data and provides the user with the aquifer parameter values.

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“…Compared with BPNNs, SVMs are based on statistical theory and have a simple structure. SVMs have many advantages in solving small‐sample, nonlinear, and high‐dimensional learning problems . Song introduced the least squares method in SVM and combined it with the harmony search algorithm to establish the harmony search least squares‐SVM model, which greatly improved upon the convergence speed of the traditional SVM.…”

Section: Monitoring Modelsmentioning

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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Prediction of the intermediate block displacement of the dam crest using artificial neural network and support vector regression models

Cited by 45 publications

References 45 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

An Innovative Approach to Determination of Double-Porosity Fractured Aquifers Hydraulic Parameters Using Artificial Neural Network

Dam monitoring data analysis methods: A literature review

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