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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Bayesian multi-task learning methodology for reconstruction of structural health monitoring data
Hua‐Ping Wan, Yi‐Qing Ni
Structural Health Monitoring (2018) Vol. 18, Iss. 4, pp. 1282-1309
Open Access | Times Cited: 185

Showing 1-25 of 185 citing articles:

Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice
Enrico Zio
Reliability Engineering & System Safety (2021) Vol. 218, pp. 108119-108119
Open Access | Times Cited: 372
Toward data anomaly detection for automated structural health monitoring: Exploiting generative adversarial nets and autoencoders
Jianxiao Mao, Hao Wang, Billie F. Spencer
Structural Health Monitoring (2020) Vol. 20, Iss. 4, pp. 1609-1626
Closed Access | Times Cited: 163
Lost data reconstruction for structural health monitoring using deep convolutional generative adversarial networks
Xiaoming Lei, Limin Sun, Ye Xia
Structural Health Monitoring (2020) Vol. 20, Iss. 4, pp. 2069-2087
Closed Access | Times Cited: 138
State-of-the-art review on advancements of data mining in structural health monitoring
Meisam Gordan, Saeed-Reza Sabbagh-Yazdi, Zubaidah Ismail, et al.
Measurement (2022) Vol. 193, pp. 110939-110939
Closed Access | Times Cited: 126
Forecasting tunnel boring machine penetration rate using LSTM deep neural network optimized by grey wolf optimization algorithm
Arsalan Mahmoodzadeh, Hamid Reza Nejati, Mokhtar Mohammadi, et al.
Expert Systems with Applications (2022) Vol. 209, pp. 118303-118303
Closed Access | Times Cited: 81
Bayesian dynamic regression for reconstructing missing data in structural health monitoring
Yiming Zhang, Hao Wang, Yu Bai, et al.
Structural Health Monitoring (2022) Vol. 21, Iss. 5, pp. 2097-2115
Closed Access | Times Cited: 79
Corrosion fatigue crack growth prediction of bridge suspender wires using Bayesian gaussian process
Yafei Ma, Yu He, Guodong Wang, et al.
International Journal of Fatigue (2022) Vol. 168, pp. 107377-107377
Closed Access | Times Cited: 72
Missing measurement data recovery methods in structural health monitoring: The state, challenges and case study
Jianwei Zhang, Minshui Huang, Neng Wan, et al.
Measurement (2024) Vol. 231, pp. 114528-114528
Closed Access | Times Cited: 47
Reconstruction of structural long-term acceleration response based on BiLSTM networks
Yonghui Lu, Liqun Tang, Chengbin Chen, et al.
Engineering Structures (2023) Vol. 285, pp. 116000-116000
Closed Access | Times Cited: 46
Real-time remote measurement of distance using ultra-wideband (UWB) sensors
Yiming Liu, Yi Bao
Automation in Construction (2023) Vol. 150, pp. 104849-104849
Closed Access | Times Cited: 41
Probabilistic method for wind speed prediction and statistics distribution inference based on SHM data-driven
Yang Ding, Xiao‐Wei Ye, Yong Guo, et al.
Probabilistic Engineering Mechanics (2023) Vol. 73, pp. 103475-103475
Closed Access | Times Cited: 41
Lost data recovery for structural health monitoring based on convolutional neural networks
Gao Fan, Jun Li, Hong Hao
Structural Control and Health Monitoring (2019) Vol. 26, Iss. 10
Open Access | Times Cited: 127
Probabilistic Framework with Bayesian Optimization for Predicting Typhoon-Induced Dynamic Responses of a Long-Span Bridge
Yiming Zhang, Hao Wang, Jianxiao Mao, et al.
Journal of Structural Engineering (2020) Vol. 147, Iss. 1
Closed Access | Times Cited: 100
Data driven structural dynamic response reconstruction using segment based generative adversarial networks
Gao Fan, Jun Li, Hong Hao, et al.
Engineering Structures (2021) Vol. 234, pp. 111970-111970
Closed Access | Times Cited: 96
Modeling and forecasting of temperature-induced strain of a long-span bridge using an improved Bayesian dynamic linear model
Hao Wang, Yiming Zhang, Jianxiao Mao, et al.
Engineering Structures (2019) Vol. 192, pp. 220-232
Closed Access | Times Cited: 90
Convolutional neural network–based data recovery method for structural health monitoring
Byung Kwan Oh, Branko Glišić, Yousok Kim, et al.
Structural Health Monitoring (2020) Vol. 19, Iss. 6, pp. 1821-1838
Closed Access | Times Cited: 87
Dynamic response reconstruction for structural health monitoring using densely connected convolutional networks
Gao Fan, Jun Li, Hong Hao
Structural Health Monitoring (2020) Vol. 20, Iss. 4, pp. 1373-1391
Closed Access | Times Cited: 87
A probabilistic approach for short-term prediction of wind gust speed using ensemble learning
Hao Wang, Yiming Zhang, Jianxiao Mao, et al.
Journal of Wind Engineering and Industrial Aerodynamics (2020) Vol. 202, pp. 104198-104198
Closed Access | Times Cited: 85
Anomaly detection of structural health monitoring data using the maximum likelihood estimation-based Bayesian dynamic linear model
Yiming Zhang, Hao Wang, Hua‐Ping Wan, et al.
Structural Health Monitoring (2020) Vol. 20, Iss. 6, pp. 2936-2952
Closed Access | Times Cited: 78
Continuous missing data imputation with incomplete dataset by generative adversarial networks–based unsupervised learning for long-term bridge health monitoring
Huachen Jiang, Chunfeng Wan, Kang Yang, et al.
Structural Health Monitoring (2021) Vol. 21, Iss. 3, pp. 1093-1109
Closed Access | Times Cited: 76
Sparse Gaussian process regression for multi-step ahead forecasting of wind gusts combining numerical weather predictions and on-site measurements
Hao Wang, Yiming Zhang, Jianxiao Mao
Journal of Wind Engineering and Industrial Aerodynamics (2021) Vol. 220, pp. 104873-104873
Closed Access | Times Cited: 69
A large-scale sensor missing data imputation framework for dams using deep learning and transfer learning strategy
Yangtao Li, Tengfei Bao, Hao Chen, et al.
Measurement (2021) Vol. 178, pp. 109377-109377
Closed Access | Times Cited: 68
Deep learning and data augmentation based data imputation for structural health monitoring system in multi-sensor damaged state
Jiale Hou, Huachen Jiang, Chunfeng Wan, et al.
Measurement (2022) Vol. 196, pp. 111206-111206
Closed Access | Times Cited: 46
Lost data neural semantic recovery framework for structural health monitoring based on deep learning
Kejie Jiang, Qiang Han, Xiuli Du
Computer-Aided Civil and Infrastructure Engineering (2022) Vol. 37, Iss. 9, pp. 1160-1187
Closed Access | Times Cited: 41
Towards high-precision data modeling of SHM measurements using an improved sparse Bayesian learning scheme with strong generalization ability
Qiang Wang, Yang Dai, Zhanguo Ma, et al.
Structural Health Monitoring (2023) Vol. 23, Iss. 1, pp. 588-604
Closed Access | Times Cited: 27
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