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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:

Development of Hybrid Machine Learning Models for Predicting the Critical Buckling Load of I-Shaped Cellular Beams
Haï-Bang Ly, Tien-Thinh Le, Lei Lü, et al.
Applied Sciences (2019) Vol. 9, Iss. 24, pp. 5458-5458
Open Access | Times Cited: 55

Showing 1-25 of 55 citing articles:

Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil
Quang Hung Nguyen, Haï-Bang Ly, Lanh Si Ho, et al.
Mathematical Problems in Engineering (2021) Vol. 2021, pp. 1-15
Open Access | Times Cited: 462
A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation
Dong Van Dao, Hojjat Adeli, Haï-Bang Ly, et al.
Sustainability (2020) Vol. 12, Iss. 3, pp. 830-830
Open Access | Times Cited: 179
Metaheuristic optimization of Levenberg–Marquardt-based artificial neural network using particle swarm optimization for prediction of foamed concrete compressive strength
Haï-Bang Ly, May Huu Nguyen, Binh Thai Pham
Neural Computing and Applications (2021) Vol. 33, Iss. 24, pp. 17331-17351
Closed Access | Times Cited: 104
New Prediction Model for the Ultimate Axial Capacity of Concrete-Filled Steel Tubes: An Evolutionary Approach
Muhammad Faisal Javed, Furqan Farooq, Shazim Ali Memon, et al.
Crystals (2020) Vol. 10, Iss. 9, pp. 741-741
Open Access | Times Cited: 113
Estimation of axial load-carrying capacity of concrete-filled steel tubes using surrogate models
Haï-Bang Ly, Binh Thai Pham, Lei Lü, et al.
Neural Computing and Applications (2020) Vol. 33, Iss. 8, pp. 3437-3458
Closed Access | Times Cited: 105
Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest
Tuan Anh Pham, Haï-Bang Ly, Van Quan Tran, et al.
Applied Sciences (2020) Vol. 10, Iss. 5, pp. 1871-1871
Open Access | Times Cited: 102
[Retracted] Taxonomy of Adaptive Neuro‐Fuzzy Inference System in Modern Engineering Sciences
Shivali Chopra, Gaurav Dhiman, Ashutosh Sharma, et al.
Computational Intelligence and Neuroscience (2021) Vol. 2021, Iss. 1
Open Access | Times Cited: 95
Optimization of Artificial Intelligence System by Evolutionary Algorithm for Prediction of Axial Capacity of Rectangular Concrete Filled Steel Tubes under Compression
Hung Quang Nguyen, Haï-Bang Ly, Van Quan Tran, et al.
Materials (2020) Vol. 13, Iss. 5, pp. 1205-1205
Open Access | Times Cited: 92
Investigation and Optimization of the C-ANN Structure in Predicting the Compressive Strength of Foamed Concrete
Dong Van Dao, Haï-Bang Ly, Huong-Lan Thi Vu, et al.
Materials (2020) Vol. 13, Iss. 5, pp. 1072-1072
Open Access | Times Cited: 88
Practical machine learning-based prediction model for axial capacity of square CFST columns
Tien-Thinh Le
Mechanics of Advanced Materials and Structures (2020) Vol. 29, Iss. 12, pp. 1782-1797
Closed Access | Times Cited: 86
Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams
Haï-Bang Ly, Tien-Thinh Le, Huong-Lan Thi Vu, et al.
Sustainability (2020) Vol. 12, Iss. 7, pp. 2709-2709
Open Access | Times Cited: 82
Strength through defects: A novel Bayesian approach for the optimization of architected materials
Zacharias Vangelatos, Haris Moazam Sheikh, Philip Marcus, et al.
Science Advances (2021) Vol. 7, Iss. 41
Open Access | Times Cited: 80
AI-powered GUI for prediction of axial compression capacity in concrete-filled steel tube columns
Panagiotis G. Asteris, Konstantinos Daniel Tsavdaridis, Minas E. Lemonis, et al.
Neural Computing and Applications (2024)
Closed Access | Times Cited: 14
Artificial intelligence in metal forming
Jian Cao, Markus� Bambach, Marion Merklein, et al.
CIRP Annals (2024) Vol. 73, Iss. 2, pp. 561-587
Closed Access | Times Cited: 11
Design deep neural network architecture using a genetic algorithm for estimation of pile bearing capacity
Tuan Anh Pham, Van Quan Tran, Huong-Lan Thi Vu, et al.
PLoS ONE (2020) Vol. 15, Iss. 12, pp. e0243030-e0243030
Open Access | Times Cited: 67
Extreme Learning Machine Based Prediction of Soil Shear Strength: A Sensitivity Analysis Using Monte Carlo Simulations and Feature Backward Elimination
Binh Thai Pham, T. Nguyen‐Thoi, Haï-Bang Ly, et al.
Sustainability (2020) Vol. 12, Iss. 6, pp. 2339-2339
Open Access | Times Cited: 53
Hybrid artificial intelligence models based on adaptive neuro fuzzy inference system and metaheuristic optimization algorithms for prediction of daily rainfall
Binh Thai Pham, Kien-Trinh Thi Bui, Indra Prakash, et al.
Physics and Chemistry of the Earth Parts A/B/C (2024) Vol. 134, pp. 103563-103563
Closed Access | Times Cited: 6
Shear Strength of Cellular Steel Beams Predicted by Hybrid ANFIS-ECBO Model
A. Kaveh, Neda Khavaninzadeh
Arabian Journal for Science and Engineering (2024)
Closed Access | Times Cited: 6
A Novel Hybrid Model Based on a Feedforward Neural Network and One Step Secant Algorithm for Prediction of Load-Bearing Capacity of Rectangular Concrete-Filled Steel Tube Columns
Quang Hung Nguyen, Haï-Bang Ly, Van Quan Tran, et al.
Molecules (2020) Vol. 25, Iss. 15, pp. 3486-3486
Open Access | Times Cited: 43
Investigation of ANN architecture for predicting shear strength of fiber reinforcement bars concrete beams
Quang Hung Nguyen, Haï-Bang Ly, Thuy‐Anh Nguyen, et al.
PLoS ONE (2021) Vol. 16, Iss. 4, pp. e0247391-e0247391
Open Access | Times Cited: 34
Forecasting the Capacity of Open-Ended Pipe Piles Using Machine Learning
Baturalp Öztürk, Antonio Kodsy, Magued Iskander
Infrastructures (2023) Vol. 8, Iss. 1, pp. 12-12
Open Access | Times Cited: 13
Probabilistic resistance predictions of laterally restrained cellular steel beams by natural gradient boosting
Vitaliy V. Degtyarev, Stephen Hicks, Felipe Piana Vendramell Ferreira, et al.
Thin-Walled Structures (2024) Vol. 205, pp. 112367-112367
Open Access | Times Cited: 5
Prediction of Later-Age Concrete Compressive Strength Using Feedforward Neural Network
Thuy‐Anh Nguyen, Haï-Bang Ly, Hai‐Van Thi, et al.
Advances in Materials Science and Engineering (2020) Vol. 2020, Iss. 1
Open Access | Times Cited: 35
Prediction of Ultimate Load of Rectangular CFST Columns Using Interpretable Machine Learning Method
Tien-Thinh Le, Hieu Chi Phan
Advances in Civil Engineering (2020) Vol. 2020, Iss. 1
Open Access | Times Cited: 34
Surrogate models for the compressive strength mapping of cement mortar materials
Panagiotis G. Asteris, Liborio Cavaleri, Haï-Bang Ly, et al.
Soft Computing (2021) Vol. 25, Iss. 8, pp. 6347-6372
Closed Access | Times Cited: 30
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