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OpenAlex Citation Counts

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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:

A novel structural damage identification scheme based on deep learning framework
Xinwei Wang, Xun’an Zhang, Muhammad Moman Shahzad
Structures (2021) Vol. 29, pp. 1537-1549
Closed Access | Times Cited: 49

Showing 1-25 of 49 citing articles:

Machine learning for structural engineering: A state-of-the-art review
Huu‐Tai Thai
Structures (2022) Vol. 38, pp. 448-491
Closed Access | Times Cited: 377
Damage identification of steel bridge based on data augmentation and adaptive optimization neural network
Minshui Huang, Jianwei Zhang, Jun Li, et al.
Structural Health Monitoring (2024)
Closed Access | Times Cited: 21
Towards vibration-based damage detection of civil engineering structures: overview, challenges, and future prospects
Ali Zar, Zahoor Hussain, Muhammad Akbar, et al.
International Journal of Mechanics and Materials in Design (2024) Vol. 20, Iss. 3, pp. 591-662
Open Access | Times Cited: 14
Structural Damage Identification Using Ensemble Deep Convolutional Neural Network Models
Mohammad Sadegh Barkhordari, Danial Jahed Armaghani, Panagiotis G. Asteris
Computer Modeling in Engineering & Sciences (2022) Vol. 134, Iss. 2, pp. 835-855
Open Access | Times Cited: 58
Review of Machine-Learning Techniques Applied to Structural Health Monitoring Systems for Building and Bridge Structures
Alain Gomez-Cabrera, Ponciano Jorge Escamilla-Ambrosio
Applied Sciences (2022) Vol. 12, Iss. 21, pp. 10754-10754
Open Access | Times Cited: 55
A Feature Extraction & Selection Benchmark for Structural Health Monitoring
Tadhg Buckley, Bidisha Ghosh, Vikram Pakrashi
Structural Health Monitoring (2022) Vol. 22, Iss. 3, pp. 2082-2127
Closed Access | Times Cited: 44
Automated location of steel truss bridge damage using machine learning and raw strain sensor data
Fulvio Parisi, Agostino Marcello Mangini, Maria Pia Fanti, et al.
Automation in Construction (2022) Vol. 138, pp. 104249-104249
Closed Access | Times Cited: 42
Deep Learning for Structural Health Monitoring: Data, Algorithms, Applications, Challenges, and Trends
Jing Jia, Ying Li
Sensors (2023) Vol. 23, Iss. 21, pp. 8824-8824
Open Access | Times Cited: 27
An ultrafast and robust structural damage identification framework enabled by an optimized extreme learning machine
Xinwei Wang, Yinghao Zhao, Zhihao Wang, et al.
Mechanical Systems and Signal Processing (2024) Vol. 216, pp. 111509-111509
Closed Access | Times Cited: 9
Probabilistic reconstruction for spatiotemporal sensor data integrated with Gaussian process regression
Yafei Ma, Yu He, Lei Wang, et al.
Probabilistic Engineering Mechanics (2022) Vol. 69, pp. 103264-103264
Closed Access | Times Cited: 35
Current and future role of data fusion and machine learning in infrastructure health monitoring
Hao Wang, Giorgio Barone, Alister Smith
Structure and Infrastructure Engineering (2023) Vol. 20, Iss. 12, pp. 1853-1882
Open Access | Times Cited: 21
From data to insight, enhancing structural health monitoring using physics-informed machine learning and advanced data collection methods
Syed Haider Mehdi Rizvi, Muntazir Abbas
Engineering Research Express (2023) Vol. 5, Iss. 3, pp. 032003-032003
Open Access | Times Cited: 17
A comparative analysis of damage identification methods based on multi-channel data fusion and convolutional neural network for frame structures
Tianli Huang, Tao Liang, Long Chen
Structures (2024) Vol. 61, pp. 106076-106076
Closed Access | Times Cited: 5
Research on Concrete Beam Damage Detection Using Convolutional Neural Networks and Vibrations from ABAQUS Models and Computer Vision
Xin Bai, Zi Zhang
Buildings (2025) Vol. 15, Iss. 2, pp. 220-220
Open Access
Unsupervised transfer learning for monitoring CFRP responses using discrete strains
Yan Huai, Songhe Meng, Bo Gao, et al.
International Journal of Mechanical Sciences (2025), pp. 110142-110142
Closed Access
Bearing Fault Diagnosis Using Lightweight and Robust One-Dimensional Convolution Neural Network in the Frequency Domain
Mohammed Hakim, Abdoulhdi A. Borhana Omran, Jawaid I. Inayat-Hussain, et al.
Sensors (2022) Vol. 22, Iss. 15, pp. 5793-5793
Open Access | Times Cited: 26
Bridge Structural Damage Identification Based on Parallel Multi-head Self-attention Mechanism and Bidirectional Long and Short-term Memory Network
Qi Liu, Jiaxing Wang, Hualin Dai, et al.
Arabian Journal for Science and Engineering (2024)
Closed Access | Times Cited: 4
Damage identification method of arch bridges using MobileViT and transfer learning
Jingzhou Xin, Liming Jiang, Qin Tang, et al.
Journal of Civil Structural Health Monitoring (2025)
Closed Access
Steel structural damage identification based on multisensor same-class graph and GraphSAGE network
Qi Liu, Jiaxing Wang, Hualin Dai, et al.
Structures (2025) Vol. 74, pp. 108388-108388
Closed Access
Bayesian-Based Hyperparameter Optimization of 1D-CNN for Structural Anomaly Detection
Xiaofei Li, Hainan Guo, Langxing Xu, et al.
Sensors (2023) Vol. 23, Iss. 11, pp. 5058-5058
Open Access | Times Cited: 12
LSTM-Based Autoencoder with Maximal Overlap Discrete Wavelet Transforms Using Lamb Wave for Anomaly Detection in Composites
Syed Haider Mehdi Rizvi, Muntazir Abbas, Syed Sajjad Haider Zaidi, et al.
Applied Sciences (2024) Vol. 14, Iss. 7, pp. 2925-2925
Open Access | Times Cited: 3
DC-DC Buck circuit fault diagnosis with insufficient state data based on deep model and transfer strategy
Zhen Jia, Zhenbao Liu, Chi‐Man Vong, et al.
Expert Systems with Applications (2022) Vol. 213, pp. 118918-118918
Closed Access | Times Cited: 16
Parallel convolutional neural network toward high efficiency and robust structural damage identification
Xijun Ye, Yongjie Cao, Airong Liu, et al.
Structural Health Monitoring (2023) Vol. 22, Iss. 6, pp. 3805-3826
Closed Access | Times Cited: 10
Model-based damage identification with simulated transmittance deviations and deep learning classification
Panagiotis Seventekidis, Dimitrios Giagopoulos
Structural Health Monitoring (2022) Vol. 21, Iss. 5, pp. 2206-2230
Closed Access | Times Cited: 15
A Two-Stage Structural Damage Detection Method Based on 1D-CNN and SVM
Chenhui Jiang, Qifeng Zhou, Jiayan Lei, et al.
Applied Sciences (2022) Vol. 12, Iss. 20, pp. 10394-10394
Open Access | Times Cited: 13
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