MR and stacked GRUs neural network combined model and its application for deformation prediction of concrete dam

Wen, Zhiping; Zhou, Renlian; Su, Huaizhi

doi:10.1016/j.eswa.2022.117272

Cited by 36 publications

(11 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The specific expressions of each component are shown in the literature [31,32]. According to Equation ( 6), the input and output variables of the SSA-BiLSTM model can be determined.…”

Section: Dam Safety Evaluation Modelmentioning

confidence: 99%

Dam Safety Evaluation Method after Extreme Load Condition Based on Health Monitoring and Deep Learning

Song

Liu

Yang

2023

Sensors

View full text Add to dashboard Cite

The safety operation of dams after extreme load is an important frontier research topic in the field of dam engineering. The dam health monitoring provides a reliable data basis for a safety evaluation after extreme loads. This study proposes a novel data-driven fusion model for a dam safety evaluation after extreme load based on monitoring data derived by sensors. First, the relationship between dam environmental quantity and effect quantity is deeply excavated based on bidirectional long short-term memory (BiLSTM) network, which is a deeply improved LSTM model. Aiming at the parameter optimization problem of BiLSTM model, sparrow search algorithm (SSA), which is an advanced optimization algorithm, is integrated. Second, conducting the constructed SSA-BiLSTM model to estimate the change law of dam effect quantity after the extreme load. Finally, the Mann–Whitney U-test theory is introduced to establish the evaluation criterion of the dam safety state. Project case shows that the multiple quantitative prediction accuracy evaluation indicators of the proposed method are significantly superior to the comparison method, with mean absolute percentage error (MAPE) and mean absolute error (MAE) values decreasing by 30.5% and 27.8%, respectively, on average. The proposed model can accurately diagnose the dam safety state after the extreme load compared with on-site inspection results of the engineering department, which provides a new method for dam safety evaluation.

show abstract

“…The specific expressions of each component are shown in the literature [31,32]. According to Equation ( 6), the input and output variables of the SSA-BiLSTM model can be determined.…”

Section: Dam Safety Evaluation Modelmentioning

confidence: 99%

Dam Safety Evaluation Method after Extreme Load Condition Based on Health Monitoring and Deep Learning

Song

Liu

Yang

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Given that environmental factors may exhibit a lag effect on dam deformation, overlooking time dependence can detrimentally impact prediction outcomes. To address these challenges, deep learning algorithms tailored for time series analysis have been increasingly utilized in the dam health monitoring field [15][16][17][18][19]. Their application has facilitated the realization of long-term predictions for deformation series.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Point Joint Prediction Model for High-Arch Dam Deformation Considering Spatial and Temporal Correlation

Cao,

Wen,

Feng

et al. 2024

Water

Self Cite

View full text Add to dashboard Cite

Deformation monitoring for mass concrete structures such as high-arch dams is crucial to their safe operation. However, structure deformations are influenced by many complex factors, and deformations at different positions tend to have spatiotemporal correlation and variability, increasing the difficulty of deformation monitoring. A novel deep learning-based monitoring model for high-arch dams considering multifactor influences and spatiotemporal data correlations is proposed in this paper. First, the measurement points are clustered to capture the spatial relationship. Successive multivariate mode decomposition is applied to extract the common mode components among the correlated points as spatial influencing factors. Second, the relationship between various factors and deformation components is extracted using factor screening. Finally, a deep learning prediction model is constructed with stacked components to obtain the final prediction. The model is validated based on practical engineering. In nearly one year of high-arch dam deformation prediction, the root mean square error is 0.344 and the R2 is 0.998, showing that the modules within the framework positively contribute to enhancing prediction performance. The prediction results of different measurement points as well as the comparison results with benchmark models show its superiority and generality, providing an advancing and practical approach for engineering structural health monitoring, particularly for high-arch dams.

show abstract

“…It has been demonstrated that gated recurrent units (GRUs) and the transformer model have excellent modelling capabilities for time-series data with storage units capable of handling dynamic properties. [28][29][30][31][32][33][34][35] For the first application scenario of soft sensor, Wang and Liu proposed a model order based-GRU (MOb-GRU) neural network soft sensor model that relies on model order. [36] The total number of network modules is determined by the complexity of the dynamic characteristics of the industrial process, and the number of reverse update units can be adjusted flexibly.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of neural network models and quality forecasting based on process time‐series data

Wang,

Liu,

Dong

et al. 2023

Can J Chem Eng

View full text Add to dashboard Cite

Complex industrial process modelling is critically important within the context of industrial intelligence. In recent years, soft sensor techniques based on neural networks have become increasingly popular for modelling nonlinear industrial processes. This paper proposes an integrated framework of neural network modelling and evaluation for nonlinear dynamic processes. This framework achieves an integrated solution for modelling, prediction, evaluation, and network structure parameter selection. It can be applied to noisy sensors and dense data in the time domain. The framework's proposed evaluation mechanism employs two novel evaluation metrics, the variational auto‐encoder (VAE)‐based Kullback–Leibler (KL) divergence metric and the maximum likelihood estimation‐based J metric, which both evaluate the model by mining the statistical properties of the residuals. The framework models the dynamic process with a model order based‐gated recurrent units (MOb‐GRU) neural network and a modified transformer model. Numerical experiments demonstrate that the evaluation mechanism functions properly in scenarios with multiple signal‐to‐noise ratios and multiple noise statistical properties and that the framework produces accurate modelling results.

show abstract

MR and stacked GRUs neural network combined model and its application for deformation prediction of concrete dam

Cited by 36 publications

References 48 publications

Dam Safety Evaluation Method after Extreme Load Condition Based on Health Monitoring and Deep Learning

Dam Safety Evaluation Method after Extreme Load Condition Based on Health Monitoring and Deep Learning

A Multi-Point Joint Prediction Model for High-Arch Dam Deformation Considering Spatial and Temporal Correlation

Evaluation of neural network models and quality forecasting based on process time‐series data

Contact Info

Product

Resources

About