Research graph
References from Integrating spatio-temporal graph convolution and adaptive embedded structural features via self-attention for bridge condition prognostics. Local targets link to admitted publications; unresolved targets remain external evidence.
Seismic collapse assessment of deteriorating RC bridges under multiple hazards during their life-cycle
10.1007/s10518-019-00647-8 · 2019 · External reference
Reliability-based life-cycle management of highway bridges
10.1061/(asce)0887-3801(2001)15:1(27) · 2001 · External reference
Life-cycle performance, management, and optimisation of structural systems under uncertainty: Accomplishments and challenges 1
10.1080/15732471003594427 · 2011 · External reference
Adaptive optimisation methods in system-level bridge management
10.1080/15732479.2014.920038 · 2015 · External reference
A state-of-the-art review of bridge inspection planning: Current situation and future needs
10.1061/(asce)be.1943-5592.0001812 · 2022 · External reference
Integrated structural health monitoring in bridge engineering
10.1016/j.autcon.2022.104168 · 2022 · External reference
Development of a wireless sensor network system for suspension bridge health monitoring
10.1016/j.autcon.2011.06.008 · 2012 · External reference
Sustainable life-cycle maintenance policymaking for network-level deteriorating bridges with a convolutional autoencoder–structured reinforcement learning agent
10.1061/jbenf2.beeng-6159 · 2023 · External reference
Synergetic-informed deep reinforcement learning for sustainable management of transportation networks with large action spaces
10.1016/j.autcon.2024.105302 · 2024 · External reference
Deep learning–based analytics of multisource heterogeneous bridge data for enhanced data-driven bridge deterioration prediction
10.1061/(asce)cp.1943-5487.0001018 · 2022 · External reference
Comparison of Markovian-based bridge deterioration model approaches
10.1061/jbenf2.beeng-5920 · 2023 · External reference
Stochastic modeling of bridge deterioration using classification tree and logistic regression
10.1061/(asce)is.1943-555x.0000466 · 2019 · External reference
Prediction of bridge component ratings using ordinal logistic regression model
10.1155/2019/9797584 · 2019 · External reference
Load-capacity rating of bridge populations through machine learning: Application of decision trees and random forests
10.1061/(asce)be.1943-5592.0001103 · 2017 · External reference
Xgboost application on bridge management systems for proactive damage estimation
10.1016/j.aei.2019.100922 · 2019 · External reference
Diagnosis algorithms for indirect bridge health monitoring via an optimized AdaBoost-linear SVM
10.1016/j.engstruct.2022.115239 · 2023 · External reference
Interpreting impact echo data to predict condition rating of concrete bridge decks: A machine-learning approach
10.1061/(asce)be.1943-5592.0001744 · 2021 · External reference
Model updating for Nam O bridge using particle swarm optimization algorithm and genetic algorithm
10.3390/s18124131 · 2018 · External reference
Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings
10.1007/s00521-016-2190-2 · 2017 · External reference
Efficient prediction of bridge conditions using modified convolutional neural network
10.1007/s11277-022-09539-8 · 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
Particle swarm optimization model to predict scour depth around a bridge pier
10.1007/s11709-020-0619-2 · 2020 · External reference
10.1016/j.istruc.2021.03.011
10.1016/j.istruc.2021.03.011 · External reference
Predicting bridge condition index using an improved back-propagation neural network
10.1016/j.aej.2024.07.029 · 2024 · External reference
Bridge type classification: Supervised learning on a modified NBI data set
10.1061/(asce)cp.1943-5487.0000712 · 2017 · External reference
Data-driven recognition and modelling of deterioration patterns in the US National Bridge Inventory: A genetic algorithm-artificial neural network framework
10.1016/j.advengsoft.2022.103148 · 2022 · External reference
Bridge condition rating data modeling using deep learning algorithm
10.1080/15732479.2020.1712610 · 2020 · External reference
Development of artificial neural network for condition assessment of bridges based on hybrid decision making method–Feasibility study
10.1016/j.eswa.2020.114271 · 2021 · External reference
Bridge infrastructure management system: Autoencoder approach for predicting bridge condition ratings
10.1061/jitse4.iseng-2123 · 2023 · External reference
Condition Assessment of Highway Bridges using Textual Data and Natural Language Processing-(NLP-) based Machine Learning Models
2023 · External reference
Alternative sequence classification of neural networks for bridge deck condition rating
10.1061/jpcfev.cfeng-4390 · 2023 · External reference
Mapping textual descriptions to condition ratings to assist bridge inspection and condition assessment using hierarchical attention
10.1016/j.autcon.2021.103801 · 2021 · External reference
A deep learning-based approach for assessment of bridge condition through fusion of multi-type inspection data
10.1016/j.engappai.2023.107468 · 2024 · External reference
A spatio-temporal cluster analysis of structurally deficient bridges in the contiguous USA
10.1016/j.dibe.2020.100034 · 2020 · External reference
A refined DS-InSAR technique for long-term deformation monitoring of low-coherence bridge groups
10.1016/j.engstruct.2025.120335 · 2025 · External reference
Damage localization for bridges monitored within one cluster based on a spatiotemporal correlation model of strain monitoring data
10.1177/14759217221078766 · 2023 · External reference
Active Learning–Enhanced Ensemble Method for Spatiotemporal Correlation Modeling of Neighboring Bridge Behaviors to Girder Overturning
10.1155/stc/6047080 · 2025 · External reference
10.24963/ijcai.2019/264
10.24963/ijcai.2019/264 · External reference
Transformers for tabular data representation: A survey of models and applications
10.1162/tacl_a_00544 · 2023 · External reference
Predicting the urban stormwater drainage system state using the Graph-WaveNet
10.1016/j.scs.2024.105877 · 2024 · External reference
Adaptive Dual-View WaveNet for urban spatial–temporal event prediction
10.1016/j.ins.2021.12.085 · 2022 · External reference
Spatial-temporal attention WaveNet: A deep learning framework for traffic prediction considering spatial-temporal dependencies
10.1049/itr2.12044 · 2021 · External reference
Multimodal data fusion based on mutual information
10.1109/tvcg.2011.280 · 2011 · External reference
Mutual information maximization based similarity operation for 3D point cloud completion network
10.1109/lsp.2022.3162139 · 2022 · External reference
Maximizing mutual information across feature and topology views for representing graphs
10.1109/tkde.2023.3264512 · 2023 · External reference
Probably approximately correct Nash equilibrium learning
10.1109/tac.2020.3030754 · 2020 · External reference
An approach to one-bit compressed sensing based on probably approximately correct learning theory
2019 · External reference
Revisiting deep learning models for tabular data
2021 · External reference
Transtab: Learning transferable tabular transformers across tables
10.52202/068431-0210 · 2022 · External reference
Asphalt pavement health prediction based on improved transformer network
10.1109/tits.2022.3229326 · 2022 · External reference
Unresolved reference
External reference
Temporal convolutional networks for action segmentation and detection
2017 · External reference
Long-term temporal convolutions for action recognition
10.1109/tpami.2017.2712608 · 2017 · External reference
Bidirectional gated temporal convolution with attention for text classification
10.1016/j.neucom.2021.05.072 · 2021 · External reference
A multi-objective genetic optimization for fast, fuzzy rule-based credit classification with balanced accuracy and interpretability
10.1016/j.asoc.2015.11.037 · 2016 · External reference
Unresolved reference
External reference
Squeeze-and-excitation networks
2018 · External reference
EmbraceNet: A robust deep learning architecture for multimodal classification
10.1016/j.inffus.2019.02.010 · 2019 · External reference
Using soft computing to analyze inspection results for bridge evaluation and management
10.1061/(asce)be.1943-5592.0000072 · 2010 · External reference
Deep residual learning for image recognition
2016 · External reference
LSTM: A search space odyssey
10.1109/tnnls.2016.2582924 · 2016 · External reference
Unresolved reference
External reference
Advanced predictive control for GRU and LSTM networks
10.1016/j.ins.2022.10.078 · 2022 · External reference
ECA-Net: Efficient channel attention for deep convolutional neural networks
2020 · External reference
Unresolved reference
External reference
Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings
10.1007/s00521-016-2190-2 · ExternalCitation · doi-reference
Seismic collapse assessment of deteriorating RC bridges under multiple hazards during their life-cycle
10.1007/s10518-019-00647-8 · ExternalCitation · doi-reference
Efficient prediction of bridge conditions using modified convolutional neural network
10.1007/s11277-022-09539-8 · ExternalCitation · doi-reference
Particle swarm optimization model to predict scour depth around a bridge pier
10.1007/s11709-020-0619-2 · ExternalCitation · doi-reference
Data-driven recognition and modelling of deterioration patterns in the US National Bridge Inventory: A genetic algorithm-artificial neural network framework
10.1016/j.advengsoft.2022.103148 · ExternalCitation · doi-reference
Xgboost application on bridge management systems for proactive damage estimation
10.1016/j.aei.2019.100922 · ExternalCitation · doi-reference
Predicting bridge condition index using an improved back-propagation neural network
10.1016/j.aej.2024.07.029 · ExternalCitation · doi-reference
A multi-objective genetic optimization for fast, fuzzy rule-based credit classification with balanced accuracy and interpretability
10.1016/j.asoc.2015.11.037 · ExternalCitation · doi-reference
Development of a wireless sensor network system for suspension bridge health monitoring
10.1016/j.autcon.2011.06.008 · ExternalCitation · doi-reference
Mapping textual descriptions to condition ratings to assist bridge inspection and condition assessment using hierarchical attention
10.1016/j.autcon.2021.103801 · ExternalCitation · doi-reference
Integrated structural health monitoring in bridge engineering
10.1016/j.autcon.2022.104168 · ExternalCitation · doi-reference
Synergetic-informed deep reinforcement learning for sustainable management of transportation networks with large action spaces
10.1016/j.autcon.2024.105302 · ExternalCitation · doi-reference
A spatio-temporal cluster analysis of structurally deficient bridges in the contiguous USA
10.1016/j.dibe.2020.100034 · ExternalCitation · doi-reference
A deep learning-based approach for assessment of bridge condition through fusion of multi-type inspection data
10.1016/j.engappai.2023.107468 · 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
Diagnosis algorithms for indirect bridge health monitoring via an optimized AdaBoost-linear SVM
10.1016/j.engstruct.2022.115239 · ExternalCitation · doi-reference
A refined DS-InSAR technique for long-term deformation monitoring of low-coherence bridge groups
10.1016/j.engstruct.2025.120335 · ExternalCitation · doi-reference
Development of artificial neural network for condition assessment of bridges based on hybrid decision making method–Feasibility study
10.1016/j.eswa.2020.114271 · ExternalCitation · doi-reference
EmbraceNet: A robust deep learning architecture for multimodal classification
10.1016/j.inffus.2019.02.010 · ExternalCitation · doi-reference
Adaptive Dual-View WaveNet for urban spatial–temporal event prediction
10.1016/j.ins.2021.12.085 · ExternalCitation · doi-reference
Advanced predictive control for GRU and LSTM networks
10.1016/j.ins.2022.10.078 · ExternalCitation · doi-reference
10.1016/j.istruc.2021.03.011
10.1016/j.istruc.2021.03.011 · ExternalCitation · doi-reference
Bidirectional gated temporal convolution with attention for text classification
10.1016/j.neucom.2021.05.072 · ExternalCitation · doi-reference
Predicting the urban stormwater drainage system state using the Graph-WaveNet
10.1016/j.scs.2024.105877 · ExternalCitation · doi-reference
Spatial-temporal attention WaveNet: A deep learning framework for traffic prediction considering spatial-temporal dependencies
10.1049/itr2.12044 · ExternalCitation · doi-reference
Reliability-based life-cycle management of highway bridges
10.1061/(asce)0887-3801(2001)15:1(27) · ExternalCitation · doi-reference
Using soft computing to analyze inspection results for bridge evaluation and management
10.1061/(asce)be.1943-5592.0000072 · ExternalCitation · doi-reference
Load-capacity rating of bridge populations through machine learning: Application of decision trees and random forests
10.1061/(asce)be.1943-5592.0001103 · ExternalCitation · doi-reference
Interpreting impact echo data to predict condition rating of concrete bridge decks: A machine-learning approach
10.1061/(asce)be.1943-5592.0001744 · ExternalCitation · doi-reference
A state-of-the-art review of bridge inspection planning: Current situation and future needs
10.1061/(asce)be.1943-5592.0001812 · ExternalCitation · doi-reference
Bridge type classification: Supervised learning on a modified NBI data set
10.1061/(asce)cp.1943-5487.0000712 · ExternalCitation · doi-reference
Deep learning–based analytics of multisource heterogeneous bridge data for enhanced data-driven bridge deterioration prediction
10.1061/(asce)cp.1943-5487.0001018 · ExternalCitation · doi-reference
Stochastic modeling of bridge deterioration using classification tree and logistic regression
10.1061/(asce)is.1943-555x.0000466 · ExternalCitation · doi-reference
Comparison of Markovian-based bridge deterioration model approaches
10.1061/jbenf2.beeng-5920 · ExternalCitation · doi-reference
Sustainable life-cycle maintenance policymaking for network-level deteriorating bridges with a convolutional autoencoder–structured reinforcement learning agent
10.1061/jbenf2.beeng-6159 · ExternalCitation · doi-reference
Bridge infrastructure management system: Autoencoder approach for predicting bridge condition ratings
10.1061/jitse4.iseng-2123 · ExternalCitation · doi-reference
Alternative sequence classification of neural networks for bridge deck condition rating
10.1061/jpcfev.cfeng-4390 · ExternalCitation · doi-reference
Life-cycle performance, management, and optimisation of structural systems under uncertainty: Accomplishments and challenges 1
10.1080/15732471003594427 · ExternalCitation · doi-reference
Adaptive optimisation methods in system-level bridge management
10.1080/15732479.2014.920038 · ExternalCitation · doi-reference
Bridge condition rating data modeling using deep learning algorithm
10.1080/15732479.2020.1712610 · ExternalCitation · doi-reference
Mutual information maximization based similarity operation for 3D point cloud completion network
10.1109/lsp.2022.3162139 · ExternalCitation · doi-reference
Probably approximately correct Nash equilibrium learning
10.1109/tac.2020.3030754 · ExternalCitation · doi-reference
Asphalt pavement health prediction based on improved transformer network
10.1109/tits.2022.3229326 · ExternalCitation · doi-reference
Maximizing mutual information across feature and topology views for representing graphs
10.1109/tkde.2023.3264512 · ExternalCitation · doi-reference
LSTM: A search space odyssey
10.1109/tnnls.2016.2582924 · ExternalCitation · doi-reference
Long-term temporal convolutions for action recognition
10.1109/tpami.2017.2712608 · ExternalCitation · doi-reference
Multimodal data fusion based on mutual information
10.1109/tvcg.2011.280 · ExternalCitation · doi-reference
Prediction of bridge component ratings using ordinal logistic regression model
10.1155/2019/9797584 · ExternalCitation · doi-reference
Active Learning–Enhanced Ensemble Method for Spatiotemporal Correlation Modeling of Neighboring Bridge Behaviors to Girder Overturning
10.1155/stc/6047080 · ExternalCitation · doi-reference
Transformers for tabular data representation: A survey of models and applications
10.1162/tacl_a_00544 · ExternalCitation · doi-reference
Damage localization for bridges monitored within one cluster based on a spatiotemporal correlation model of strain monitoring data
10.1177/14759217221078766 · ExternalCitation · doi-reference
10.24963/ijcai.2019/264
10.24963/ijcai.2019/264 · ExternalCitation · doi-reference
Model updating for Nam O bridge using particle swarm optimization algorithm and genetic algorithm
10.3390/s18124131 · ExternalCitation · doi-reference
Transtab: Learning transferable tabular transformers across tables
10.52202/068431-0210 · ExternalCitation · doi-reference
