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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:

Optimized Support Vector Machines Combined with Evolutionary Random Forest for Prediction of Back-Break Caused by Blasting Operation
Qun Yu, Masoud Monjezi, Ahmed Salih Mohammed, et al.
Sustainability (2021) Vol. 13, Iss. 22, pp. 12797-12797
Open Access | Times Cited: 35

Showing 1-25 of 35 citing articles:

Assessment of tunnel blasting-induced overbreak: A novel metaheuristic-based random forest approach
Biao He, Danial Jahed Armaghani, Sai Hin Lai
Tunnelling and Underground Space Technology (2023) Vol. 133, pp. 104979-104979
Closed Access | Times Cited: 76
State-of-the-art review of machine learning and optimization algorithms applications in environmental effects of blasting
Jian Zhou, Yulin Zhang, Yingui Qiu
Artificial Intelligence Review (2024) Vol. 57, Iss. 1
Open Access | Times Cited: 23
Machine learning based computational approach for crack width detection of self-healing concrete
Fadi Althoey, Muhammad Nasir Amin, Kaffayatullah Khan, et al.
Case Studies in Construction Materials (2022) Vol. 17, pp. e01610-e01610
Closed Access | Times Cited: 56
Six Novel Hybrid Extreme Learning Machine–Swarm Intelligence Optimization (ELM–SIO) Models for Predicting Backbreak in Open-Pit Blasting
Chuanqi Li, Jian Zhou, Manoj Khandelwal, et al.
Natural Resources Research (2022) Vol. 31, Iss. 5, pp. 3017-3039
Open Access | Times Cited: 49
Microstructure and chemical characterizations with soft computing models to evaluate the influence of calcium oxide and silicon dioxide in the fly ash and cement kiln dust on the compressive strength of cement mortar
Aso A. Abdalla, Ahmed Salih
Resources Conservation & Recycling Advances (2022) Vol. 15, pp. 200090-200090
Open Access | Times Cited: 43
Theoretical models to evaluate the effect of SiO2 and CaO contents on the long-term compressive strength of cement mortar modified with cement kiln dust (CKD)
Aso A. Abdalla, Ahmed Salih Mohammed
Archives of Civil and Mechanical Engineering (2022) Vol. 22, Iss. 3
Closed Access | Times Cited: 41
Compressive strength and sensitivity analysis of fly ash composite foam concrete: Efficient machine learning approach
Chen Zhang, Zhiduo Zhu, Liang Shi, et al.
Advances in Engineering Software (2024) Vol. 192, pp. 103634-103634
Closed Access | Times Cited: 10
Machine Learning Prediction Model Integrating Experimental Study for Compressive Strength of Carbon-Nanotubes Composites
Aneel Manan, Pu Zhang, Shoaib Ahmad, et al.
Journal of Engineering Research (2024)
Open Access | Times Cited: 9
Estimation of powder factor in mine blasting: feasibility of tree-based predictive models
Danial Jahed Armaghani, Mohammad Hayati, Ehsan Momeni, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2025) Vol. 8, Iss. 2
Open Access | Times Cited: 1
Microstructure, chemical compositions, and soft computing models to evaluate the influence of silicon dioxide and calcium oxide on the compressive strength of cement mortar modified with cement kiln dust
Aso A. Abdalla, Ahmed Salih Mohammed, Serwan Rafiq, et al.
Construction and Building Materials (2022) Vol. 341, pp. 127668-127668
Closed Access | Times Cited: 30
Extreme fine-tuning and explainable AI model for non-destructive prediction of concrete compressive strength, the case of ConcreteXAI dataset
José A. Guzmán-Torres, Francisco J. Domínguez-Mota, G. Tinoco-Guerrero, et al.
Advances in Engineering Software (2024) Vol. 192, pp. 103630-103630
Closed Access | Times Cited: 7
Predicting the compressive strength of UHPC with coarse aggregates in the context of machine learning
Yan Yuan, Ming Yang, Xiangwen Shang, et al.
Case Studies in Construction Materials (2023) Vol. 19, pp. e02627-e02627
Open Access | Times Cited: 16
A hybrid model based on convolution neural network and long short-term memory for qualitative assessment of permeable and porous concrete
Manish Kumar, Manish Kumar, Shatakshi Singh, et al.
Case Studies in Construction Materials (2023) Vol. 19, pp. e02254-e02254
Open Access | Times Cited: 14
Predicting pavement condition index based on the utilization of machine learning techniques: A case study
Abdualmtalab Abdualaziz Ali, Abdalrhman Milad, Amgad Hussein, et al.
Journal of Road Engineering (2023) Vol. 3, Iss. 3, pp. 266-278
Open Access | Times Cited: 14
Prediction of Ground Vibration Induced by Rock Blasting Based on Optimized Support Vector Regression Models
Yifan Huang, Zikang Zhou, Mingyu Li, et al.
Computer Modeling in Engineering & Sciences (2024) Vol. 139, Iss. 3, pp. 3147-3165
Open Access | Times Cited: 6
Prediction of creep rupture life of ODS steels based on machine learning
Tian-Xing Yang, Peng Dou
Materials Today Communications (2024) Vol. 38, pp. 108117-108117
Closed Access | Times Cited: 5
Compressive strength prediction of hydrothermally solidified clay with different machine learning techniques
Minguo Lin, Ruobin Su, Geng Chen, et al.
Journal of Cleaner Production (2023) Vol. 413, pp. 137541-137541
Closed Access | Times Cited: 11
Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method
Jian Zhou, Yuxin Chen, Hui Chen, et al.
Frontiers in Public Health (2023) Vol. 11
Open Access | Times Cited: 10
Open-Pit Bench Blasting Fragmentation Prediction Based on Stacking Integrated Strategy
Yulei Sui, Zhiyong Zhou, Rui Zhao, et al.
Applied Sciences (2025) Vol. 15, Iss. 3, pp. 1254-1254
Open Access
Tensile behavior evaluation of two-stage concrete using an innovative model optimization approach
Muhammad Nasir Amin, Faizullah Jan, Kaffayatullah Khan, et al.
REVIEWS ON ADVANCED MATERIALS SCIENCE (2025) Vol. 64, Iss. 1
Open Access
A systematic survey of hybrid ML techniques for predicting peak particle velocity (PPV) in open-cast mine blasting operations
Gundaveni Shylaja, Prashanth Ragam
Artificial Intelligence Review (2025) Vol. 58, Iss. 7
Open Access
Utilizing heuristic strategies for predicting the backbreak occurrences in open-pit mines, Gol Gohar Mine, Iran
Parviz Sorabi, Mohammad Ataei, Mohammad Reza Alimoradi Jazi, et al.
Soft Computing (2024)
Closed Access | Times Cited: 3
Estimation of Blast-Induced Peak Particle Velocity through the Improved Weighted Random Forest Technique
Biao He, Sai Hin Lai, Ahmed Salih Mohammed, et al.
Applied Sciences (2022) Vol. 12, Iss. 10, pp. 5019-5019
Open Access | Times Cited: 14
Optimized Data-Driven Models for Prediction of Flyrock due to Blasting in Surface Mines
Xiaohua Ding, Mehdi Jamei, Mahdi Hasanipanah, et al.
Sustainability (2023) Vol. 15, Iss. 10, pp. 8424-8424
Open Access | Times Cited: 7
Revolutionizing the construction industry by cutting edge artificial intelligence approaches: a review
E. Gill, Daniela Cardone, Alessia Amelio
Frontiers in Artificial Intelligence (2024) Vol. 7
Open Access | Times Cited: 2
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