Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Application of metaheuristic optimization algorithms in predicting the compressive strength of 3D-printed fiber-reinforced concrete
Mana Alyami, Majid Khan, Muhammad Faisal Javed, et al.
Developments in the Built Environment (2023) Vol. 17, pp. 100307-100307
Open Access | Times Cited: 33

Showing 1-25 of 33 citing articles:

Evaluation of water quality indexes with novel machine learning and SHapley Additive ExPlanation (SHAP) approaches
Ali Aldrees, Majid Khan, Abubakr Taha Bakheit Taha, et al.
Journal of Water Process Engineering (2024) Vol. 58, pp. 104789-104789
Closed Access | Times Cited: 69
Forecasting the strength of graphene nanoparticles-reinforced cementitious composites using ensemble learning algorithms
Majid Khan, Roz‐Ud‐Din Nassar, Waqar Anwar, et al.
Results in Engineering (2024) Vol. 21, pp. 101837-101837
Open Access | Times Cited: 28
Metaheuristic optimization algorithms-based prediction modeling for titanium dioxide-Assisted photocatalytic degradation of air contaminants
Muhammad Faisal Javed, Bilal Siddiq, Kennedy C. Onyelowe, et al.
Results in Engineering (2024) Vol. 23, pp. 102637-102637
Open Access | Times Cited: 21
Predicting Penetration Depth in Ultra-High-Performance Concrete Targets under Ballistic Impact: An Interpretable Machine Learning Approach Augmented by Deep Generative Adversarial Network
Majid Khan, Muhammad Faisal Javed, Nashwan Adnan Othman, et al.
Results in Engineering (2025), pp. 103909-103909
Open Access | Times Cited: 3
Concrete 3D printing technology for sustainable construction: A review on raw material, concrete type and performance
Xiaonan Wang, Wengui Li, Yipu Guo, et al.
Developments in the Built Environment (2024) Vol. 17, pp. 100378-100378
Open Access | Times Cited: 16
Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants
Muhammad Faisal Javed, Muhammad Zubair Shahab, Usama Asif, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 13
Predicting the properties of concrete incorporating graphene nano platelets by experimental and machine learning approaches
Rayed Alyousef, Roz‐Ud‐Din Nassar, Muhammad Fawad, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e03018-e03018
Open Access | Times Cited: 12
A Review of Concrete Carbonation Depth Evaluation Models
Xinhao Wang, Qiuwei Yang, Xi Peng, et al.
Coatings (2024) Vol. 14, Iss. 4, pp. 386-386
Open Access | Times Cited: 11
Utilizing contemporary machine learning techniques for determining soilcrete properties
Waleed Bin Inqiad, Muhammad Saud Khan, Zeeshan Mehmood, et al.
Earth Science Informatics (2025) Vol. 18, Iss. 1
Open Access | Times Cited: 1
Advanced and hybrid machine learning techniques for predicting compressive strength in palm oil fuel ash-modified concrete with SHAP analysis
Tariq Ali, Kennedy C. Onyelowe, Muhammad Sarmad Mahmood, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 1
Development and optimization of geopolymer concrete with compressive strength prediction using particle swarm-optimized extreme gradient boosting
Shimol Philip, Nidhi Marakkath
Applied Soft Computing (2025), pp. 113149-113149
Closed Access | Times Cited: 1
Predicting compressive strength of hollow concrete prisms using machine learning techniques and explainable artificial intelligence (XAI)
Waleed Bin Inqiad, Elena Valentina Dumitrascu, Robert Alexandru Dobre, et al.
Heliyon (2024) Vol. 10, Iss. 17, pp. e36841-e36841
Open Access | Times Cited: 8
Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches
Muhammad Fawad, Hisham Alabduljabbar, Furqan Farooq, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 7
Predicting 28-day compressive strength of fibre-reinforced self-compacting concrete (FR-SCC) using MEP and GEP
Waleed Bin Inqiad, Muhammad Shahid Siddique, Mujahid Ali, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 5
Optimized prediction modeling of micropollutant removal efficiency in forward osmosis membrane systems using explainable machine learning algorithms
Ali Aldrees, Muhammad Faisal Javed, Majid Khan, et al.
Journal of Water Process Engineering (2024) Vol. 66, pp. 105937-105937
Closed Access | Times Cited: 5
Machine learning models for estimating the compressive strength of rubberized concrete subjected to elevated temperature: Optimization and hyper-tuning
Turki S. Alahmari, Irfan Ullah, Furqan Farooq
Sustainable Chemistry and Pharmacy (2024) Vol. 42, pp. 101763-101763
Closed Access | Times Cited: 5
Predicting residual strength of hybrid fibre-reinforced Self-compacting concrete (HFR-SCC) exposed to elevated temperatures using machine learning
Muhammad Saud Khan, Liqiang Ma, Waleed Bin Inqiad, et al.
Case Studies in Construction Materials (2024) Vol. 22, pp. e04112-e04112
Closed Access | Times Cited: 5
Performance Comparison of Artificial Neural Network and Random Forest Models for Predicting the Compressive Strength of Fibre-Reinforced GGBS-Based Geopolymer Concrete Composites
Shimol Philip, M. Nidhi
Materials Circular Economy (2024) Vol. 6, Iss. 1
Closed Access | Times Cited: 4
Predicting the shield effectiveness of carbon fiber reinforced mortars utilizing metaheuristic algorithms
Mana Alyami, Irfan Ullah, Furqan Ahmad, et al.
Case Studies in Construction Materials (2025), pp. e04357-e04357
Open Access
Predicting Mechanical Properties of Concrete Structures Using Metaheuristic-Optimization-Based Machine Learning Models
Ngoc‐Mai Nguyen
Applied Soft Computing (2025), pp. 112893-112893
Closed Access
A Comprehensive Review of the Tunicate Swarm Algorithm: Variations, Applications, and Results
Rong Zheng, Abdelazim G. Hussien, Anas Bouaouda, et al.
Archives of Computational Methods in Engineering (2025)
Closed Access
Predicting pile bearing capacity using gene expression programming with SHapley Additive exPlanation interpretation
Adil Khan, Majid Khan, Waseem Akhtar Khan, et al.
Deleted Journal (2025) Vol. 2, Iss. 1
Open Access
Soft-computing models for predicting plastic viscosity and interface yield stress of fresh concrete
Waleed Bin Inqiad, Muhammad Faisal Javed, Deema Mohammed Alsekait, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Machine learning-based prediction of shear strength in interior beam-column joints
Iman Kattoof Harith, Wissam Nadir, Mustafa S. Salah, et al.
Deleted Journal (2025) Vol. 7, Iss. 5
Open Access
Compressive Strength Prediction and Feature Analysis for GGBS-Based Geopolymer Concrete Using Optimized XGBoost and SHAP: A Comparative Study of Optimization Algorithms and Experimental Validation
Shimol Philip, M. Nidhi
Journal of Building Engineering (2025), pp. 112879-112879
Closed Access
Page 1 - Next Page

Scroll to top