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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:

Utilizing advanced machine learning approaches to assess the seismic fragility of non-engineered masonry structures
Ehsan Harirchian, Seyed Ehsan Aghakouchaki Hosseini, Viviana Novelli, et al.
Results in Engineering (2024) Vol. 21, pp. 101750-101750
Open Access | Times Cited: 12

Showing 12 citing articles:

A systematic review of trustworthy artificial intelligence applications in natural disasters
A. S. Albahri, Yahya Layth Khaleel, Mustafa Abdulfattah Habeeb, et al.
Computers & Electrical Engineering (2024) Vol. 118, pp. 109409-109409
Open Access | Times Cited: 33
Compressive strength prediction models for concrete containing nano materials and exposed to elevated temperatures
Hany A. Dahish, Ahmed D. Almutairi
Results in Engineering (2025), pp. 103975-103975
Open Access | Times Cited: 2
RAGN-R: A multi-subject ensemble machine-learning method for estimating mechanical properties of advanced structural materials
Farzin Kazemi, Aybike Özyüksel Çiftçioğlu, Torkan Shafighfard, et al.
Computers & Structures (2025) Vol. 308, pp. 107657-107657
Closed Access | Times Cited: 2
Data-Driven Machine-Learning-Based Seismic Response Prediction and Damage Classification for an Unreinforced Masonry Building
Nagavinothini Ravichandran, Butsawan Bidorn, Oya Mercan, et al.
Applied Sciences (2025) Vol. 15, Iss. 4, pp. 1686-1686
Open Access
Fragility assessment of steel jacket offshore platforms using time series prediction with deep learning methods
Rashid Ali, Hamed Rahman Shokrghozar
Ships and Offshore Structures (2025), pp. 1-16
Closed Access
A state-of-the-art analysis of base isolation systems and future directions for developing a novel multi-directional smart-hybrid isolation system integrated with earthquake early warning system for building structures
TARIQ H. R. BERMANY, Siti Aminah Osman, Mohd Yazmil Md. Yatim
Results in Engineering (2025) Vol. 25, pp. 104501-104501
Open Access
Exploring the distribution of seismic loads in tunnel-form concrete buildings using the probable area method
Vahid Mohsenian, Nima Gharaei-Moghaddam, Abdolreza S. Moghadam
Results in Engineering (2024) Vol. 21, pp. 101851-101851
Open Access | Times Cited: 2
Innovative approach to estimate structural damage using linear regression and K-nearest neighbors machine learning algorithms
Vasile Calofir, Ruben-Iacob Munteanu, Mircea Ştefan Simoiu, et al.
Results in Engineering (2024) Vol. 22, pp. 102250-102250
Open Access | Times Cited: 1
Resistance of eccentric braced steel frames against progressive collapse and overload factor
Fayez Rakhsha, S. Hatami, Mojtaba Gorji Azandariani, et al.
Structures (2024) Vol. 70, pp. 107933-107933
Closed Access | Times Cited: 1
Experimental study on the pullout behavior of steel rebars in masonry shotcreted layer
Hadi Sabouri, Mohammad Yekrangnia
Results in Engineering (2024) Vol. 23, pp. 102670-102670
Open Access
Efficiency and explainability of design‐oriented machine learning models to estimate seismic response, fragility, and loss of a steel building inventory
Mohsen Zaker Esteghamati, Shivalinga Baddipalli
Earthquake Engineering & Structural Dynamics (2024)
Closed Access
Data-Driven Modeling of Lateral and Cracking Loads in Confined Masonry Walls Using Machine Learning
Hamza Mahamad Bile, Kadir Güler
Buildings (2024) Vol. 14, Iss. 12, pp. 4016-4016
Open Access
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