Research graph
References from Time-domain augmented displacement prediction model for data-sparse concrete dams via two-pathway encoding and attentive aggregation. Local targets link to admitted publications; unresolved targets remain external evidence.
Graph based knowledge models for capitalizing, predicting and learning: a proof of concept applied to the dam systems
10.1016/j.aei.2022.101551 · 2022 · External reference
Adaptive multi-point hybrid model for displacement prediction of concrete dams considering sequence cross-relatedness
10.1016/j.measurement.2025.118522 · 2025 · External reference
Hybrid monitoring methodology: a model-data integrated digital twin framework for structural health monitoring and full-field virtual sensing
10.1016/j.aei.2024.102386 · 2024 · External reference
Digital twin-driven intelligent operation and maintenance platform for large-scale hydro-steel structures
2024 · External reference
Displacement observation data-based structural health monitoring of concrete dams: a state-of-art review
10.1016/j.istruc.2024.107072 · 2024 · External reference
A novel method for settlement imputation and monitoring of earth-rockfill dams subjected to large-scale missing data
10.1016/j.aei.2024.102642 · 2024 · External reference
A feature decomposition-based deep transfer learning framework for concrete dam deformation prediction with observational insufficiency
10.1016/j.aei.2023.102175 · 2023 · External reference
Recurrent neural networks for multivariate time series with missing values
10.1038/s41598-018-24271-9 · 2018 · External reference
Hydrostatic, temperature, time-displacement model for concrete dams
10.1061/(asce)0733-9399(2007)133:3(267) · 2007 · External reference
Constructing statistical models for arch dam deformation
10.1002/stc.1575 · 2014 · External reference
A critical review of statistical model of dam monitoring data
2023 · External reference
Mechanism and numerical simulation of reservoir slope deformation during impounding of high arch dams based on nonlinear FEM
10.1016/j.compgeo.2016.08.009 · 2017 · External reference
Evaluation of behaviors of earth and rockfill dams during construction and initial impounding using instrumentation data and numerical modeling
10.1016/j.jrmge.2016.12.003 · 2017 · External reference
The role of artificial intelligence and digital technologies in dam engineering: narrative review and outlook
10.1016/j.engappai.2023.106813 · 2023 · External reference
A similarity-aware ensemble method for displacement prediction of concrete dams based on temporal division and fully bayesian learning
10.1016/j.aei.2024.102921 · 2024 · External reference
Multi-kernel optimized relevance vector machine for probabilistic prediction of concrete dam displacement
10.1007/s00366-019-00924-9 · 2021 · External reference
Representation learning: a review and new perspectives
10.1109/tpami.2013.50 · 2013 · External reference
Safety monitoring model of a super-high concrete dam by using rbf neural network coupled with kernel principal component analysis
10.1155/2018/1712653 · 2018 · External reference
Advancements and emerging trends in integrating machine learning and deep learning for SHM in mechanical and civil engineering: a comprehensive review
10.1007/s40430-025-05697-5 · 2025 · External reference
Structural recovery and damage prediction in GFRP-BFRP patched plates using hybrid SSA-SCA-XGBoost model and vibration data
10.1016/j.compstruct.2026.120638 · 2026 · External reference
A new hybrid PSO-YUKI for double cracks identification in CFRP cantilever beam
10.1016/j.compstruct.2023.116803 · 2023 · External reference
An efficient improved gradient boosting for strain prediction in near-surface mounted fiber-reinforced polymer strengthened reinforced concrete beam
10.1007/s11709-024-1079-x · 2024 · External reference
Prediction in percentage variation of frequencies on damaged pipe repaired by composite materials GFRP, CFRP, and BFRP
2026 · External reference
Pattern classification with missing data: a review
10.1007/s00521-009-0295-6 · 2010 · External reference
Nonrandom missing data can bias principal component analysis inference of population genetic structure
10.1111/1755-0998.13498 · 2022 · External reference
DRLSTM: a dual-stage deep learning approach driven by raw monitoring data for dam displacement prediction
10.1016/j.aei.2021.101510 · 2022 · External reference
Deep learning
10.1038/nature14539 · 2015 · External reference
Deep learning-based structural health monitoring
10.1016/j.autcon.2024.105328 · 2024 · External reference
MR and stacked GRUs neural network combined model and its application for deformation prediction of concrete dam
10.1016/j.eswa.2022.117272 · 2022 · External reference
A novel deep learning prediction model for concrete dam displacements using interpretable mixed attention mechanism
10.1016/j.aei.2021.101407 · 2021 · External reference
A deep learning prediction model of DenseNet-LSTM for concrete gravity dam deformation based on feature selection
10.1016/j.engstruct.2023.116827 · 2023 · External reference
Analysis of concrete dam deformation prediction based on the ResNet-GRU-SGWO model
2024 · External reference
A self-attention LSTM method for dam deformation prediction based on CEEMDAN optimization
10.1016/j.asoc.2024.111615 · 2024 · External reference
Deep learning for irregularly and regularly missing data reconstruction
10.1038/s41598-020-59801-x · 2020 · External reference
A comprehensive survey of deep learning for time series forecasting: architectural diversity and open challenges
10.1007/s10462-025-11223-9 · 2025 · External reference
A review on multiscale-deep-learning applications
10.3390/s22197384 · 2022 · External reference
Multi-scale convolutional neural networks for time series classification
2016 · External reference
Res2Net: a new multi-scale backbone architecture
10.1109/tpami.2019.2938758 · 2021 · External reference
Dynamic stacking ensemble monitoring model of dam displacement based on the feature selection with PCA-RF
10.1007/s13349-022-00557-5 · 2022 · External reference
Predicting piezometric water level in dams via artificial neural networks
10.1007/s00521-012-1334-2 · 2014 · External reference
A separate modeling approach to noisy displacement prediction of concrete dams via improved deep learning with frequency division
10.1016/j.aei.2024.102367 · 2024 · External reference
Multipoint hybrid model for RCC arch dam displacement health monitoring considering construction interface and its seepage
10.1016/j.apm.2022.06.023 · 2022 · External reference
Spatiotemporal hybrid model for concrete arch dam deformation monitoring considering chaotic effect of residual series
10.1016/j.engstruct.2020.111488 · 2021 · External reference
Modified hybrid forecast model considering chaotic residual errors for dam deformation
10.1002/stc.2188 · 2018 · External reference
Deformation similarity characteristics-considered hybrid panel model for multi-point deformation monitoring of super-high arch dams in operating conditions
10.1016/j.measurement.2022.110908 · 2022 · External reference
A new hybrid monitoring model for displacement of the concrete dam
10.3390/su15129609 · 2023 · External reference
Combination forecast model for concrete dam displacement considering residual correction
10.1177/1475921717748608 · 2019 · External reference
Structural health monitoring of civil infrastructure using optical fiber sensing technology: A comprehensive review
2014 · External reference
Development of a structural health monitoring system for Guangzhou New TV Tower
2008 · External reference
Robust smoothing of gridded data in one and higher dimensions with missing values
10.1016/j.csda.2009.09.020 · 2010 · External reference
Unresolved reference
2020 · External reference
Precision forecasting of fertilizer components' concentrations in mixed variable-rate fertigation through machine learning
10.1016/j.agwat.2024.108859 · 2024 · External reference
Research on tool wear prediction for milling high strength steel based on DenseNet-ResNet-GRU
10.1007/s12206-024-0632-9 · 2024 · External reference
Dynamic response reconstruction for structural health monitoring using densely connected convolutional networks
10.1177/1475921720916881 · 2021 · External reference
A modified deep residual network for short-term load forecasting
10.3389/fenrg.2022.1038819 · 2022 · External reference
Dynamic intelligent prediction approach for landslide displacement based on biological growth models and CNN-LSTM
10.1007/s11629-024-9113-y · 2025 · External reference
An improved long short-term memory model for dam displacement prediction
2019 · External reference
Multivariate temporal convolutional network: a deep neural networks approach for multivariate time series forecasting
10.3390/electronics8080876 · 2019 · External reference
A novel extreme adaptive GRU for multivariate time series forecasting
2024 · External reference
Multilayer perceptron for short-term load forecasting: from global to local approach
10.1007/s00521-019-04130-y · 2020 · External reference
Constructing statistical models for arch dam deformation
10.1002/stc.1575 · ExternalCitation · doi-reference
Modified hybrid forecast model considering chaotic residual errors for dam deformation
10.1002/stc.2188 · ExternalCitation · doi-reference
Multi-kernel optimized relevance vector machine for probabilistic prediction of concrete dam displacement
10.1007/s00366-019-00924-9 · ExternalCitation · doi-reference
Pattern classification with missing data: a review
10.1007/s00521-009-0295-6 · ExternalCitation · doi-reference
Predicting piezometric water level in dams via artificial neural networks
10.1007/s00521-012-1334-2 · ExternalCitation · doi-reference
Multilayer perceptron for short-term load forecasting: from global to local approach
10.1007/s00521-019-04130-y · ExternalCitation · doi-reference
A comprehensive survey of deep learning for time series forecasting: architectural diversity and open challenges
10.1007/s10462-025-11223-9 · ExternalCitation · doi-reference
Dynamic intelligent prediction approach for landslide displacement based on biological growth models and CNN-LSTM
10.1007/s11629-024-9113-y · ExternalCitation · doi-reference
An efficient improved gradient boosting for strain prediction in near-surface mounted fiber-reinforced polymer strengthened reinforced concrete beam
10.1007/s11709-024-1079-x · ExternalCitation · doi-reference
Research on tool wear prediction for milling high strength steel based on DenseNet-ResNet-GRU
10.1007/s12206-024-0632-9 · ExternalCitation · doi-reference
Dynamic stacking ensemble monitoring model of dam displacement based on the feature selection with PCA-RF
10.1007/s13349-022-00557-5 · ExternalCitation · doi-reference
Advancements and emerging trends in integrating machine learning and deep learning for SHM in mechanical and civil engineering: a comprehensive review
10.1007/s40430-025-05697-5 · ExternalCitation · doi-reference
A novel deep learning prediction model for concrete dam displacements using interpretable mixed attention mechanism
10.1016/j.aei.2021.101407 · ExternalCitation · doi-reference
DRLSTM: a dual-stage deep learning approach driven by raw monitoring data for dam displacement prediction
10.1016/j.aei.2021.101510 · ExternalCitation · doi-reference
Graph based knowledge models for capitalizing, predicting and learning: a proof of concept applied to the dam systems
10.1016/j.aei.2022.101551 · ExternalCitation · doi-reference
A feature decomposition-based deep transfer learning framework for concrete dam deformation prediction with observational insufficiency
10.1016/j.aei.2023.102175 · ExternalCitation · doi-reference
A separate modeling approach to noisy displacement prediction of concrete dams via improved deep learning with frequency division
10.1016/j.aei.2024.102367 · ExternalCitation · doi-reference
Hybrid monitoring methodology: a model-data integrated digital twin framework for structural health monitoring and full-field virtual sensing
10.1016/j.aei.2024.102386 · ExternalCitation · doi-reference
A novel method for settlement imputation and monitoring of earth-rockfill dams subjected to large-scale missing data
10.1016/j.aei.2024.102642 · ExternalCitation · doi-reference
A similarity-aware ensemble method for displacement prediction of concrete dams based on temporal division and fully bayesian learning
10.1016/j.aei.2024.102921 · ExternalCitation · doi-reference
Precision forecasting of fertilizer components' concentrations in mixed variable-rate fertigation through machine learning
10.1016/j.agwat.2024.108859 · ExternalCitation · doi-reference
Multipoint hybrid model for RCC arch dam displacement health monitoring considering construction interface and its seepage
10.1016/j.apm.2022.06.023 · ExternalCitation · doi-reference
A self-attention LSTM method for dam deformation prediction based on CEEMDAN optimization
10.1016/j.asoc.2024.111615 · ExternalCitation · doi-reference
Deep learning-based structural health monitoring
10.1016/j.autcon.2024.105328 · ExternalCitation · doi-reference
Mechanism and numerical simulation of reservoir slope deformation during impounding of high arch dams based on nonlinear FEM
10.1016/j.compgeo.2016.08.009 · ExternalCitation · doi-reference
A new hybrid PSO-YUKI for double cracks identification in CFRP cantilever beam
10.1016/j.compstruct.2023.116803 · ExternalCitation · doi-reference
Structural recovery and damage prediction in GFRP-BFRP patched plates using hybrid SSA-SCA-XGBoost model and vibration data
10.1016/j.compstruct.2026.120638 · ExternalCitation · doi-reference
Robust smoothing of gridded data in one and higher dimensions with missing values
10.1016/j.csda.2009.09.020 · ExternalCitation · doi-reference
The role of artificial intelligence and digital technologies in dam engineering: narrative review and outlook
10.1016/j.engappai.2023.106813 · ExternalCitation · doi-reference
Spatiotemporal hybrid model for concrete arch dam deformation monitoring considering chaotic effect of residual series
10.1016/j.engstruct.2020.111488 · ExternalCitation · doi-reference
A deep learning prediction model of DenseNet-LSTM for concrete gravity dam deformation based on feature selection
10.1016/j.engstruct.2023.116827 · ExternalCitation · doi-reference
MR and stacked GRUs neural network combined model and its application for deformation prediction of concrete dam
10.1016/j.eswa.2022.117272 · ExternalCitation · doi-reference
Displacement observation data-based structural health monitoring of concrete dams: a state-of-art review
10.1016/j.istruc.2024.107072 · ExternalCitation · doi-reference
Evaluation of behaviors of earth and rockfill dams during construction and initial impounding using instrumentation data and numerical modeling
10.1016/j.jrmge.2016.12.003 · ExternalCitation · doi-reference
Deformation similarity characteristics-considered hybrid panel model for multi-point deformation monitoring of super-high arch dams in operating conditions
10.1016/j.measurement.2022.110908 · ExternalCitation · doi-reference
Adaptive multi-point hybrid model for displacement prediction of concrete dams considering sequence cross-relatedness
10.1016/j.measurement.2025.118522 · ExternalCitation · doi-reference
Deep learning
10.1038/nature14539 · ExternalCitation · doi-reference
Recurrent neural networks for multivariate time series with missing values
10.1038/s41598-018-24271-9 · ExternalCitation · doi-reference
Deep learning for irregularly and regularly missing data reconstruction
10.1038/s41598-020-59801-x · ExternalCitation · doi-reference
Hydrostatic, temperature, time-displacement model for concrete dams
10.1061/(asce)0733-9399(2007)133:3(267) · ExternalCitation · doi-reference
Representation learning: a review and new perspectives
10.1109/tpami.2013.50 · ExternalCitation · doi-reference
Res2Net: a new multi-scale backbone architecture
10.1109/tpami.2019.2938758 · ExternalCitation · doi-reference
Nonrandom missing data can bias principal component analysis inference of population genetic structure
10.1111/1755-0998.13498 · ExternalCitation · doi-reference
Safety monitoring model of a super-high concrete dam by using rbf neural network coupled with kernel principal component analysis
10.1155/2018/1712653 · ExternalCitation · doi-reference
Combination forecast model for concrete dam displacement considering residual correction
10.1177/1475921717748608 · ExternalCitation · doi-reference
Dynamic response reconstruction for structural health monitoring using densely connected convolutional networks
10.1177/1475921720916881 · ExternalCitation · doi-reference
A modified deep residual network for short-term load forecasting
10.3389/fenrg.2022.1038819 · ExternalCitation · doi-reference
Multivariate temporal convolutional network: a deep neural networks approach for multivariate time series forecasting
10.3390/electronics8080876 · ExternalCitation · doi-reference
A review on multiscale-deep-learning applications
10.3390/s22197384 · ExternalCitation · doi-reference
A new hybrid monitoring model for displacement of the concrete dam
10.3390/su15129609 · ExternalCitation · doi-reference
