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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:

Damage Detection in Composites By Artificial Neural Networks Trained By Using in Situ Distributed Strains
America Califano, Neha Chandarana, Luigi Grassia, et al.
Applied Composite Materials (2020) Vol. 27, Iss. 5, pp. 657-671
Open Access | Times Cited: 42

Showing 1-25 of 42 citing articles:

Prediction of maximum tensile stress in plain-weave composite laminates with interacting holes via stacked machine learning algorithms: A comparative study
Faramarz Bagherzadeh, Torkan Shafighfard, Raja Muhammad Awais Khan, et al.
Mechanical Systems and Signal Processing (2023) Vol. 195, pp. 110315-110315
Closed Access | Times Cited: 75
Autonomous damage recognition in visual inspection of laminated composite structures using deep learning
Sakineh Fotouhi, Farzad Pashmforoush, Mahdi Bodaghi, et al.
Composite Structures (2021) Vol. 268, pp. 113960-113960
Open Access | Times Cited: 83
Machine learning assisted characterisation and simulation of compressive damage in composite laminates
Johannes Reiner, Reza Vaziri, Navid Zobeiry
Composite Structures (2021) Vol. 273, pp. 114290-114290
Closed Access | Times Cited: 62
Machine Learning Based Quantitative Damage Monitoring of Composite Structure
Xinlin Qing, Yunlai Liao, Yihan Wang, et al.
International Journal of Smart and Nano Materials (2022) Vol. 13, Iss. 2, pp. 167-202
Open Access | Times Cited: 61
Intelligent structural health monitoring of composite structures using machine learning, deep learning, and transfer learning: a review
Muhammad Muzammil Azad, Sungjun Kim, Yu Bin Cheon, et al.
Advanced Composite Materials (2023) Vol. 33, Iss. 2, pp. 162-188
Closed Access | Times Cited: 39
Compression after multiple impact strength of composite laminates prediction method based on machine learning approach
Jingyu Zhao, Ben Wang, Qihui Lyu, et al.
Aerospace Science and Technology (2023) Vol. 136, pp. 108243-108243
Closed Access | Times Cited: 28
On ultrasound propagation in composite laminates: advances in numerical simulation
Leandro Maio, Paul Fromme
Progress in Aerospace Sciences (2021) Vol. 129, pp. 100791-100791
Open Access | Times Cited: 49
A machine learning framework for real-time inverse modeling and multi-objective process optimization of composites for active manufacturing control
Keith D. Humfeld, Da‐Wei Gu, Geoffrey A. Butler, et al.
Composites Part B Engineering (2021) Vol. 223, pp. 109150-109150
Open Access | Times Cited: 43
Full scale promoted convolution neural network for intelligent terahertz 3D characterization of GFRP delamination
Yafei Xu, Hongkuan Zhou, Yuqing Cui, et al.
Composites Part B Engineering (2022) Vol. 242, pp. 110022-110022
Closed Access | Times Cited: 31
Implementation of a probabilistic machine learning strategy for failure predictions of adhesively bonded joints using cohesive zone modeling
Yuval Freed, Marco Salviato, Navid Zobeiry
International Journal of Adhesion and Adhesives (2022) Vol. 118, pp. 103226-103226
Closed Access | Times Cited: 30
Damage Detection in Flat Panels by Guided Waves Based Artificial Neural Network Trained through Finite Element Method
Donato Perfetto, Alessandro De Luca, Marco Perfetto, et al.
Materials (2021) Vol. 14, Iss. 24, pp. 7602-7602
Open Access | Times Cited: 34
Experiments and modelling of ultrasonic waves in composite plates under varying temperature
Donato Perfetto, Zahra Sharif Khodaei, Alessandro De Luca, et al.
Ultrasonics (2022) Vol. 126, pp. 106820-106820
Closed Access | Times Cited: 26
Development of aviation industry-oriented methodology for failure predictions of brittle bonded joints using probabilistic machine learning
Yuval Freed, Navid Zobeiry, Marco Salviato
Composite Structures (2022) Vol. 297, pp. 115979-115979
Closed Access | Times Cited: 23
On the Use of Machine Learning for Damage Assessment in Composite Structures: A Review
Ronny Francis Ribeiro, Guilherme Ferreira Gomes
Applied Composite Materials (2023) Vol. 31, Iss. 1, pp. 1-37
Closed Access | Times Cited: 16
Deep Transfer Learning Approach for Localization of Damage Area in Composite Laminates Using Acoustic Emission Signal
Jingyu Zhao, Weihua Xie, Dong Yu, et al.
Polymers (2023) Vol. 15, Iss. 6, pp. 1520-1520
Open Access | Times Cited: 13
Impact damage prediction method of glass/carbon fiber hybrid composites based on particle swarm optimization–backpropagation neural network
Long Chen, Licheng Lu, Xianfeng Gu, et al.
Polymer Composites (2025)
Closed Access
Development of Robust Machine Learning Models for Predicting Flexural Strengths of Fiber-Reinforced Polymeric Composites
Abdulhammed K. Hamzat, Umar Salman, Md Shafinur Murad, et al.
Hybrid Advances (2025), pp. 100385-100385
Open Access
Damage detection in composites using non-destructive testing aided by ANN technique: A review
Neetika Saha, Parikshit Roy, Pijush Topdar
Journal of Thermoplastic Composite Materials (2023) Vol. 36, Iss. 12, pp. 4997-5033
Closed Access | Times Cited: 10
Damage monitoring of pultruded GFRP composites using wavelet transform of vibration signals
Vamsi Inturi, M.P. Hemanth, Pavan Kumar Penumakala, et al.
Measurement (2022) Vol. 195, pp. 111177-111177
Closed Access | Times Cited: 14
Optimization techniques for damage detection of composite structure: A review
Nikhil P. Raut, A.B. Kolekar, Shrinivas L. Gombi
Materials Today Proceedings (2021) Vol. 45, pp. 4830-4834
Closed Access | Times Cited: 18
Vibration-based delamination evaluation in GFRP composite beams using ANN
T. G. Sreekanth, M. Senthilkumar, S. Manikanta Reddy
Polymers and Polymer Composites (2021) Vol. 29, Iss. 9_suppl, pp. S317-S324
Closed Access | Times Cited: 18
Study on Prediction of Compression Performance of Composite Laminates After Impact Based on Convolutional Neural Networks
Fengyang Jiang, Zhidong Guan, Xiaodong Wang, et al.
Applied Composite Materials (2021) Vol. 28, Iss. 4, pp. 1153-1173
Closed Access | Times Cited: 14
Numerical Simulation and Multi-objective Optimization for Curing Process of Thermosetting Prepreg
Jiatong Hou, Bo You, Jiazhong Xu, et al.
Applied Composite Materials (2022) Vol. 29, Iss. 4, pp. 1409-1429
Closed Access | Times Cited: 8
Machine learning-enabled thickness estimation of thin coatings on carbon fibre composites using microwaves
Zhen Li, Zhaozong Meng, Jian Liu, et al.
Measurement Science and Technology (2023) Vol. 34, Iss. 12, pp. 124003-124003
Closed Access | Times Cited: 4
Optimization method for low-velocity impact identification in nanocomposite using genetic algorithm
Zhiyuan Wang, Long Chen, Chenyang Zhu, et al.
Nanotechnology Reviews (2024) Vol. 13, Iss. 1
Open Access | Times Cited: 1
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