Abstract
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Qiubing Ren, Ruizhe Liu, Yinpeng He, Mingchao Li, Yantao Yu, Yingbo Chen, Shun Qin
Abstract
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Graph based knowledge models for capitalizing, predicting and learning: a proof of concept applied to the dam systems
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Hybrid monitoring methodology: a model-data integrated digital twin framework for structural health monitoring and full-field virtual sensing
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Provenance
crossref
Confidence 100%
ror
Confidence 99%
openalex
Confidence 95%
datacite
Confidence 0%
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10.1016/j.aei.2024.102367 · doi-reference
Predicting piezometric water level in dams via artificial neural networks
10.1007/s00521-012-1334-2 · doi-reference
Dynamic stacking ensemble monitoring model of dam displacement based on the feature selection with PCA-RF
10.1007/s13349-022-00557-5 · doi-reference
Res2Net: a new multi-scale backbone architecture
10.1109/tpami.2019.2938758 · doi-reference
A review on multiscale-deep-learning applications
10.3390/s22197384 · doi-reference
A comprehensive survey of deep learning for time series forecasting: architectural diversity and open challenges
10.1007/s10462-025-11223-9 · doi-reference
Deep learning for irregularly and regularly missing data reconstruction
10.1038/s41598-020-59801-x · doi-reference
A self-attention LSTM method for dam deformation prediction based on CEEMDAN optimization
10.1016/j.asoc.2024.111615 · 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 · doi-reference
A novel deep learning prediction model for concrete dam displacements using interpretable mixed attention mechanism
10.1016/j.aei.2021.101407 · 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 · doi-reference
Deep learning-based structural health monitoring
10.1016/j.autcon.2024.105328 · doi-reference
Deep learning
10.1038/nature14539 · doi-reference
DRLSTM: a dual-stage deep learning approach driven by raw monitoring data for dam displacement prediction
10.1016/j.aei.2021.101510 · doi-reference
Nonrandom missing data can bias principal component analysis inference of population genetic structure
10.1111/1755-0998.13498 · doi-reference
Pattern classification with missing data: a review
10.1007/s00521-009-0295-6 · 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 · doi-reference
A new hybrid PSO-YUKI for double cracks identification in CFRP cantilever beam
10.1016/j.compstruct.2023.116803 · 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 · 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 · 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 · doi-reference
Representation learning: a review and new perspectives
10.1109/tpami.2013.50 · doi-reference
Multi-kernel optimized relevance vector machine for probabilistic prediction of concrete dam displacement
10.1007/s00366-019-00924-9 · 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 · doi-reference
The role of artificial intelligence and digital technologies in dam engineering: narrative review and outlook
10.1016/j.engappai.2023.106813 · 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 · doi-reference