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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:

Hybrid regression and machine learning model for predicting ultimate condition of FRP-confined concrete
Behrooz Keshtegar, Aliakbar Gholampour, Duc‐Kien Thai, et al.
Composite Structures (2021) Vol. 262, pp. 113644-113644
Closed Access | Times Cited: 38

Showing 1-25 of 38 citing articles:

State-of-the-art AI-based computational analysis in civil engineering
Chen Wang, Ling-han Song, Yuan Zhou, et al.
Journal of Industrial Information Integration (2023) Vol. 33, pp. 100470-100470
Closed Access | Times Cited: 39
Data-driven PSO-CatBoost machine learning model to predict the compressive strength of CFRP- confined circular concrete specimens
Nima Khodadadi, Hossein Roghani, Francisco De Caso, et al.
Thin-Walled Structures (2024) Vol. 198, pp. 111763-111763
Closed Access | Times Cited: 16
Predicting Confinement Effect of Carbon Fiber Reinforced Polymers on Strength of Concrete using Metaheuristics-based Artificial Neural Networks
Sarmed Wahab, Mohamed Suleiman, Faisal Shabbir, et al.
Journal of Civil Engineering Frontiers (2024) Vol. 4, Iss. 02, pp. 45-59
Open Access | Times Cited: 14
Predicting load capacity of shear walls using SVR–RSM model
Behrooz Keshtegar, Moncef L. Nehdi, T. Nguyen‐Thoi, et al.
Applied Soft Computing (2021) Vol. 112, pp. 107739-107739
Closed Access | Times Cited: 58
Prediction of ultimate condition of FRP-confined recycled aggregate concrete using a hybrid boosting model enriched with tabular generative adversarial networks
Xinyu Zhao, Jinxin Chen, G.M. Chen, et al.
Thin-Walled Structures (2022) Vol. 182, pp. 110318-110318
Closed Access | Times Cited: 49
Metaheuristic‐based machine learning modeling of the compressive strength of concrete containing waste glass
Mohamed El Amine Ben Seghier, Emadaldin Mohammadi Golafshani, Jafar Jafari‐Asl, et al.
Structural Concrete (2023) Vol. 24, Iss. 4, pp. 5417-5440
Open Access | Times Cited: 26
Coupled extreme gradient boosting algorithm with artificial intelligence models for predicting compressive strength of fiber reinforced polymer- confined concrete
Tao Hai, Zainab Hasan Ali, Faisal M. Mukhtar, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 134, pp. 108674-108674
Closed Access | Times Cited: 8
Prediction of the axial compressive strength of circular concrete-filled steel tube columns using sine cosine algorithm-support vector regression
Fei Lyu, Xinyu Fan, Faxing Ding, et al.
Composite Structures (2021) Vol. 273, pp. 114282-114282
Closed Access | Times Cited: 53
Development of a Reliable Machine Learning Model to Predict Compressive Strength of FRP-Confined Concrete Cylinders
Prashant Kumar, Harish Chandra Arora, Alireza Bahrami, et al.
Buildings (2023) Vol. 13, Iss. 4, pp. 931-931
Open Access | Times Cited: 17
Prediction of compressive strength of FRP-confined concrete using machine learning: A novel synthetic data driven framework
Sixiong Zeng, Xin Wang, Luqing Hua, et al.
Journal of Building Engineering (2024) Vol. 94, pp. 109918-109918
Closed Access | Times Cited: 6
Designing a reliable machine learning system for accurately estimating the ultimate condition of FRP-confined concrete
Meysam Alizamir, Aliakbar Gholampour, Sungwon Kim, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 5
Data-driven machine-learning models for predicting non-uniform confinement effects of FRP-confined concrete
Jian Xie, Chenhang Jia, Zhe Wang
Structures (2025) Vol. 74, pp. 108555-108555
Closed Access
Experimental investigation on axial compressive behavior of novel FRP-ECC-HSC composite short column
Shuai Li, Tak‐Ming Chan, Ben Young
Composite Structures (2022) Vol. 303, pp. 116285-116285
Open Access | Times Cited: 24
Calibrating equations to predict the compressive strength of FRP-Confined columns using optimized neural network model
Hamid Khodadadi Koodiani, Negar Erfanian, Arsalan Majlesi, et al.
Structures (2023) Vol. 56, pp. 105060-105060
Closed Access | Times Cited: 13
Machine learning-based prediction of shear strength of steel reinforced concrete columns subjected to axial compressive load and seismic lateral load
Siyuan Wang, Jinjun Xu, Yanlin Wang, et al.
Structures (2023) Vol. 56, pp. 104968-104968
Closed Access | Times Cited: 12
Estimating the compressive strength of rectangular fiber reinforced polymer–confined columns using multilayer perceptron, radial basis function, and support vector regression methods
Yaser Moodi, Mohammad Ghasemi, Seyed Roohollah Mousavi
Journal of Reinforced Plastics and Composites (2021) Vol. 41, Iss. 3-4, pp. 130-146
Closed Access | Times Cited: 29
Data‐driven investigation of compressive strength of FRP‐confined concrete columns using a unified model based on RSM considering interactions between parameters
Ali Vafaei, Abdullah Davoudi‐Kia, Saman Soleimani Kutanaei, et al.
Structural Concrete (2024) Vol. 25, Iss. 3, pp. 2183-2205
Closed Access | Times Cited: 3
Prediction of Stress–Strain Behavior of PET FRP-Confined Concrete Using Machine Learning Models
Arslan Qayyum Khan, Muhammad Naveed, Muhammad Dawood Rasheed, et al.
Arabian Journal for Science and Engineering (2024)
Closed Access | Times Cited: 3
Direct Shear Strength Prediction for Precast Concrete Joints Using the Machine Learning Method
Tongxu Liu, Zhen Wang, Zilin Long, et al.
Journal of Bridge Engineering (2022) Vol. 27, Iss. 5
Closed Access | Times Cited: 18
Machine Learning Alternatives to Response Surface Models
Badih Ghattas, Diane Manzon
Mathematics (2023) Vol. 11, Iss. 15, pp. 3406-3406
Open Access | Times Cited: 9
Prediction of compressive strength of fiber-reinforced polymers-confined cylindrical concrete using artificial intelligence methods
Faride Jamali, Seyed Roohollah Mousavi, Abdolhamid Bahr Peyma, et al.
Journal of Reinforced Plastics and Composites (2022) Vol. 41, Iss. 17-18, pp. 679-704
Closed Access | Times Cited: 15
A new formulation for predicting the ultimate capacities of FRP-confined concrete using advanced machine learning framework: Developed structural reliability analysis
Mahmoud Alfouneh, Mohamed El Amine Ben Seghier, Behrooz Keshtegar, et al.
Structures (2024) Vol. 68, pp. 107158-107158
Closed Access | Times Cited: 2
Predicting ultimate condition and transition point on axial stress–strain curve of FRP-confined concrete using a meta-heuristic algorithm
Ali Fallah Pour, Roohollah Shirani Faradonbeh, Aliakbar Gholampour, et al.
Composite Structures (2022) Vol. 304, pp. 116387-116387
Closed Access | Times Cited: 11
Fiber-Reinforced Polymer Confined Concrete: Data-Driven Predictions of Compressive Strength Utilizing Machine Learning Techniques
Filippos Sofos, Christos G. Papakonstantinou, Maria Valasaki, et al.
Applied Sciences (2022) Vol. 13, Iss. 1, pp. 567-567
Open Access | Times Cited: 11
Probabilistic fatigue estimation framework for aeroengine bladed discs with multiple fuzziness modeling
Yaowei Wang, Lu-Kai Song, Xueqin Li, et al.
Journal of Materials Research and Technology (2023) Vol. 24, pp. 2812-2827
Open Access | Times Cited: 6
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