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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:

Interpretable machine-learning models for maximum displacements of RC beams under impact loading predictions
Dade Lai, Cristoforo Demartino, Yan Xiao
Engineering Structures (2023) Vol. 281, pp. 115723-115723
Closed Access | Times Cited: 40

Showing 26-50 of 40 citing articles:

Metaheuristic Optimization of Extreme Gradient Boosting Machine for Enhanced Prediction of Lateral Strength of Reinforced Concrete Columns under Cyclic Loadings
Phu Anh Huy Pham, Nhat‐Duc Hoang
Results in Engineering (2024), pp. 103125-103125
Open Access | Times Cited: 1
Mechanics-informed transformer-GCN for structural dynamic response prediction
Qi Liao, Yuequan Bao, Haiyang Hu, et al.
Engineering Structures (2024) Vol. 325, pp. 119470-119470
Closed Access | Times Cited: 1
Data‐driven model for seismic assessment, design, and retrofit of structures using explainable artificial intelligence
Khurram Shabbir, Mohamed Noureldin, Sung‐Han Sim
Computer-Aided Civil and Infrastructure Engineering (2024)
Open Access | Times Cited: 1
Multi-level physics informed deep learning for solving partial differential equations in computational structural mechanics
Weiwei He, Jinzhao Li, Xuan Kong, et al.
Communications Engineering (2024) Vol. 3, Iss. 1
Open Access | Times Cited: 1
Prediction of Failure Modes of Steel Tube-Reinforced Concrete Shear Walls Using Blending Fusion Model Based on Generative Adversarial Networks Data Augmentation
Guangchao Yang, Jigang Zhang, Zhehao Ma, et al.
Applied Sciences (2023) Vol. 13, Iss. 22, pp. 12433-12433
Open Access | Times Cited: 2
Prediction and Interpretation of Residual Bearing Capacity of Cfst Columns under Impact Loads Based Interpretable Stacking Fusion Modeling
Guangchao Yang, Ran Yang, Jian Zhang
Buildings (2023) Vol. 13, Iss. 11, pp. 2783-2783
Open Access | Times Cited: 2
Interpretable physics-aware alkali-silica reaction expansion prediction
Mohammad Amin Hariri‐Ardebili
Construction and Building Materials (2024) Vol. 449, pp. 138165-138165
Closed Access
A novel machine learning framework for impact force prediction of foam-filled multi-layer lattice composite structures
Jiye Chen, Yufeng Zhao, Hai Fang, et al.
Thin-Walled Structures (2024), pp. 112607-112607
Closed Access
Machine learning-based probabilistic predictions for Concrete Filled Steel Tube (CFST) column axial capacity
Dade Lai, Jingyu Wei, Alessandro Contento, et al.
Structures (2024) Vol. 70, pp. 107543-107543
Open Access
Exploring the impact resistance performance of RC beams based on an enhanced interpretable automated machine learning approach
D.L. Zou, Jianfu Teng, L. Xu
Structures (2024) Vol. 70, pp. 107893-107893
Closed Access
Shear bearing capacity prediction of STRC shear walls using data‐augmented fusion model
Guangchao Yang, Ji‐gang Zhang, Zhehao Ma, et al.
Structural Concrete (2024) Vol. 25, Iss. 4, pp. 2609-2623
Closed Access
Machine Learning Approach to Predicating Bending Capacity of Ecc-Concrete Composite Beams Hybrid Reinforced with Steel and Frp Bars
Wenjie Ge, Feng Zhang, Yi Wang, et al.
(2024)
Closed Access
Experimental, analytical and numerical study of reinforced concrete beams impacted by deformable bodies at low velocities
Lucas Márquez, Hervé Le Sourne, Philippe Rigo
International Journal of Impact Engineering (2024) Vol. 192, pp. 105027-105027
Closed Access
Machine Learning Techniques for Predicting the Peak Response of Reinforced Concrete Beam Subjected to Impact Loading
Ali Husnain, Munir Iqbal, Hafiz Ahmed Waqas, et al.
(2024)
Closed Access
Seismic Response Prediction of Rigid Rocking Structures Using Explainable LightGBM Models
Ioannis Karampinis, Kosmas E. Bantilas, Ioannis E. Kavvadias, et al.
Mathematics (2024) Vol. 12, Iss. 14, pp. 2280-2280
Open Access
A mathematical framework for efficient predictions of dynamic processes of RC piers under barge collisions
Wang We, Xu Wang, Rongxin Zhou, et al.
Case Studies in Construction Materials (2023) Vol. 19, pp. e02460-e02460
Open Access
Advancing Infrastructure Resilience: Machine Learning-Based Prediction of Bridges’ Rating Factors under Autonomous Truck Platoons
mohamed elshazli, Dina Hussein, Ganapati Bhat, et al.
Research Square (Research Square) (2023)
Open Access
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