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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:

Modeling Hot-Mix asphalt dynamic modulus using deep residual neural Networks: Parametric and sensitivity analysis study
Ghada Moussa, Mahmoud Owais
Construction and Building Materials (2021) Vol. 294, pp. 123589-123589
Closed Access | Times Cited: 37

Showing 1-25 of 37 citing articles:

Deep Learning for Integrated Origin–Destination Estimation and Traffic Sensor Location Problems
Mahmoud Owais
IEEE Transactions on Intelligent Transportation Systems (2024) Vol. 25, Iss. 7, pp. 6501-6513
Closed Access | Times Cited: 29
Prioritizing rear-end crash explanatory factors for injury severity level using deep learning and global sensitivity analysis
Mahmoud Owais, Abdullah Mohammed Alshehri, Jayadev Gyani, et al.
Expert Systems with Applications (2024) Vol. 245, pp. 123114-123114
Closed Access | Times Cited: 24
Traffic sensor location problem: Three decades of research
Mahmoud Owais
Expert Systems with Applications (2022) Vol. 208, pp. 118134-118134
Closed Access | Times Cited: 52
Global sensitivity analysis for studying hot-mix asphalt dynamic modulus parameters
Mahmoud Owais, Ghada Moussa
Construction and Building Materials (2024) Vol. 413, pp. 134775-134775
Closed Access | Times Cited: 11
Modeling green recycled aggregate concrete using machine learning and variance-based sensitivity analysis
Mahmoud Owais, Lamiaa K. Idriss
Construction and Building Materials (2024) Vol. 440, pp. 137393-137393
Closed Access | Times Cited: 11
INN-ASDNet: Embracing Involutional Neural Networks and Random Forest for Prediction of Autism Spectrum Disorder
Bhagya Lakshmi Polavarapu, Mahesh Kumar Morampudi, T N Tarun, et al.
Arabian Journal for Science and Engineering (2025)
Closed Access | Times Cited: 1
Global sensitivity analysis for seismic performance of shear wall with high-strength steel bars and recycled aggregate concrete
Lamiaa K. Idriss, Mahmoud Owais
Construction and Building Materials (2023) Vol. 411, pp. 134498-134498
Closed Access | Times Cited: 16
Red light crossing violations modelling using deep learning and variance-based sensitivity analysis
Mahmoud Owais, Mohamed Sayed
Expert Systems with Applications (2024), pp. 126258-126258
Closed Access | Times Cited: 6
Variance-based global sensitivity analysis for rear-end crash investigation using deep learning
Ghada Moussa, Mahmoud Owais, Essam Dabbour
Accident Analysis & Prevention (2021) Vol. 165, pp. 106514-106514
Closed Access | Times Cited: 34
New Approach for Estimating Intersection Control Delay From Passive Traffic Sensors at Network Level
Ahmed Almutairi, Ping Yi, Mahmoud Owais
IEEE Access (2024) Vol. 12, pp. 2882-2900
Open Access | Times Cited: 5
Preprocessing and postprocessing analysis for hot-mix asphalt dynamic modulus experimental data
Mahmoud Owais
Construction and Building Materials (2024) Vol. 450, pp. 138693-138693
Closed Access | Times Cited: 4
Enhance the accuracy, robustness and interpretability of asphalt mixture dynamic modulus prediction
Ke Zhang, Zhaohui Min, Xiatong Hao, et al.
International Journal of Pavement Engineering (2025) Vol. 26, Iss. 1
Closed Access
Deep learning based methodological approach for prediction of dynamic modulus and phase angle of asphalt concrete
Nishigandha Rajeshwar Jukte, Aravind Krishna Swamy
Engineering Applications of Artificial Intelligence (2025) Vol. 145, pp. 110269-110269
Closed Access
Innovative Machine Learning Approaches for Predicting the Asphalt Content During Marshall Design of Asphalt Mixtures
Mutahar Abdul Salam Al-ammari, Ruikun Dong, Mohammed Ali Nasser, et al.
Materials (2025) Vol. 18, Iss. 7, pp. 1474-1474
Open Access
Reliable forecasting non-linear triaxial mechanical response of recycled aggregate concrete by knowledge-enhanced, modified, explainable and replicable machine learning algorithms
Haoyu Zhu, Ming-Zhi Guo, Yan Zhang
Expert Systems with Applications (2025), pp. 127326-127326
Closed Access
Improving asphalt mix design by predicting alligator cracking and longitudinal cracking based on machine learning and dimensionality reduction techniques
Jian Liu, Fangyu Liu, Hongren Gong, et al.
Construction and Building Materials (2022) Vol. 354, pp. 129162-129162
Closed Access | Times Cited: 19
Involving prediction of dynamic modulus in asphalt mix design with machine learning and mechanical-empirical analysis
Jian Liu, Fangyu Liu, Zhen Wang, et al.
Construction and Building Materials (2023) Vol. 407, pp. 133610-133610
Closed Access | Times Cited: 12
Analysing Witczak 1-37A, Witczak 1-40D and Modified Hirsch Models for asphalt dynamic modulus prediction using global sensitivity analysis
Mahmoud Owais
International Journal of Pavement Engineering (2023) Vol. 24, Iss. 1
Closed Access | Times Cited: 11
Investigating the effects of reducing the number of temperatures and frequencies on the development of master curves for viscoelastic properties of bituminous composite
Masood Gholami, Mojtaba Khodadadi, Pouria Hajikarimi, et al.
Measurement (2024) Vol. 230, pp. 114503-114503
Closed Access | Times Cited: 3
In-Situ Dynamic Modulus Prediction for Asphalt Pavement Combining Machine Learning Algorithm and Sensing Technology
Cheng Zhang, Shihui Shen, Hai Huang, et al.
IEEE Transactions on Intelligent Transportation Systems (2024) Vol. 25, Iss. 8, pp. 8695-8704
Closed Access | Times Cited: 2
An attention-based deep learning model for citywide traffic flow forecasting
Tao Zhou, Bo Huang, Rongrong Li, et al.
International Journal of Digital Earth (2022) Vol. 15, Iss. 1, pp. 323-344
Open Access | Times Cited: 12
Experimental analysis and predictive modelling of linear viscoelastic response of asphalt mixture under dynamic shear loading
Jiqing Zhu, Abubeker Ahmed, Safwat Said, et al.
Construction and Building Materials (2022) Vol. 328, pp. 127095-127095
Open Access | Times Cited: 11
Fast Identification of Soybean Seed Varieties Using Laser-Induced Breakdown Spectroscopy Combined With Convolutional Neural Network
Xiaolong Li, Zhenni He, Fei Liu, et al.
Frontiers in Plant Science (2021) Vol. 12
Open Access | Times Cited: 13
Boosting Hot Mix Asphalt Dynamic Modulus Prediction Using Statistical and Machine Learning Regression Modeling Techniques
Ahmed M. Awed, Ahmed N. Awaad, Mosbeh R. Kaloop, et al.
Sustainability (2023) Vol. 15, Iss. 19, pp. 14464-14464
Open Access | Times Cited: 5
Asphalt concrete dynamic modulus prediction: Bayesian neural network approach
Babak Asadi, Ramez Hajj, Imad L. Al‐Qadi
International Journal of Pavement Engineering (2023) Vol. 24, Iss. 2
Closed Access | Times Cited: 5
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