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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:

Machine learning-aided scenario-based seismic drift measurement for RC moment frames using visual features of surface damage
Mohammadjavad Hamidia, Sina Mansourdehghan, Amir Hossein Asjodi, et al.
Measurement (2022) Vol. 205, pp. 112195-112195
Closed Access | Times Cited: 24

Showing 24 citing articles:

Prediction of pull-out behavior of timber glued-in glass fiber reinforced polymer and steel rods under various environmental conditions based on ANN and GEP models
Mostafa Mohammadzadeh Taleshi, Nima Tajik, Alireza Mahmoudian, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e02842-e02842
Open Access | Times Cited: 20
AI-driven computer vision-based automated repair activity identification for seismically damaged RC columns
Samira Azhari, Sara Jamshidian, Mohammadjavad Hamidia
Automation in Construction (2025) Vol. 170, pp. 105959-105959
Closed Access | Times Cited: 3
Data-driven strength-based seismic damage index measurement for RC columns using crack image-derived parameters
Mobinasadat Afzali, Mohammadjavad Hamidia, Mohammad Safi
Measurement (2023) Vol. 218, pp. 113155-113155
Closed Access | Times Cited: 27
Post-earthquake damage assessment for RC columns using crack image complexity measures
Sara Jamshidian, Mohammadjavad Hamidia
Bulletin of Earthquake Engineering (2023) Vol. 21, Iss. 13, pp. 6029-6063
Closed Access | Times Cited: 25
Vision-oriented machine learning-assisted seismic energy dissipation estimation for damaged RC beam-column connections
Mohammadjavad Hamidia, Mostafa Kaboodkhani, Hamid Bayesteh
Engineering Structures (2023) Vol. 301, pp. 117345-117345
Closed Access | Times Cited: 25
Crack image-based FEMA P-58-compliant fragility models for automated earthquake-induced loss estimation in non-ductile RC moment frames
Parnia Zamani, Samira Azhari, Mohammadjavad Hamidia, et al.
Structures (2024) Vol. 60, pp. 105873-105873
Closed Access | Times Cited: 13
Computer vision-based quantification of updated stiffness for damaged RC columns after earthquake
Mohammadjavad Hamidia, Majid Sheikhi, Amir Hossein Asjodi, et al.
Advances in Engineering Software (2024) Vol. 190, pp. 103597-103597
Closed Access | Times Cited: 8
Data-driven crack image-based seismic failure mode identification for damaged RC columns
Samira Azhari, Mohammadjavad Hamidia
Engineering Failure Analysis (2024) Vol. 160, pp. 108160-108160
Closed Access | Times Cited: 8
Vision‐based probabilistic post‐earthquake loss estimation for reinforced concrete shear walls
Samira Azhari, Mohammadjavad Hamidia, Fatemeh Rouhani
Structural Concrete (2024) Vol. 25, Iss. 3, pp. 2020-2052
Closed Access | Times Cited: 8
Post‐earthquake stiffness loss estimation for reinforced concrete columns using fractal analysis of crack patterns
Mohammadjavad Hamidia, Mobinasadat Afzali, Sara Jamshidian, et al.
Structural Concrete (2023) Vol. 24, Iss. 3, pp. 3933-3951
Closed Access | Times Cited: 20
Energy-based damage assessment of RC frames with non-seismic beam-column joint detailing using crack image processing techniques
Mostafa Kaboodkhani, Hamid Bayesteh, Mohammadjavad Hamidia
Engineering Failure Analysis (2023) Vol. 155, pp. 107723-107723
Closed Access | Times Cited: 20
Nonmodel rapid seismic assessment of eccentrically braced frames incorporating masonry infills using machine learning techniques
Romina Chalabi, Omid Yazdanpanah, Kiarash M. Dolatshahi
Journal of Building Engineering (2023) Vol. 79, pp. 107784-107784
Closed Access | Times Cited: 18
Extended fragility surfaces for unreinforced masonry walls using vision-derived damage parameters
Amir Hossein Asjodi, Kiarash M. Dolatshahi
Engineering Structures (2023) Vol. 278, pp. 115467-115467
Closed Access | Times Cited: 16
Machine vision‐based automated earthquake‐induced drift ratio quantification for reinforced concrete columns
Mohammadjavad Hamidia, Sara Jamshidian, Mobinasadat Afzali, et al.
The Structural Design of Tall and Special Buildings (2023) Vol. 32, Iss. 18
Closed Access | Times Cited: 13
Tree-based machine learning models for predicting the bond strength in reinforced recycled aggregate concrete
Alireza Mahmoudian, Maryam Bypour, Denise‐Penelope N. Kontoni
Asian Journal of Civil Engineering (2024)
Closed Access | Times Cited: 5
Reconstructing the complete force-deformation response envelope for unreinforced masonry walls using self-supervised learning
Sepehr Saeidi, Amir Hossein Asjodi, Kiarash M. Dolatshahi, et al.
Earthquake Spectra (2025)
Closed Access
Three‐dimensional fragility surface for reinforced concrete shear walls using image‐based damage features
Amir Hossein Asjodi, Kiarash M. Dolatshahi, Henry Burton
Earthquake Engineering & Structural Dynamics (2023) Vol. 52, Iss. 8, pp. 2533-2553
Closed Access | Times Cited: 11
Multivariable fragility curves for unreinforced masonry walls
Samane Rezaei, Kiarash M. Dolatshahi, Amir Hossein Asjodi
Bulletin of Earthquake Engineering (2023) Vol. 21, Iss. 7, pp. 3357-3398
Closed Access | Times Cited: 11
Multi-feature driven seismic damage state identification for reinforced concrete shear walls using computer vision and machine learning
Samira Azhari, Ali Mahmoodi, Amirhossein Samavi, et al.
Advances in Engineering Software (2024) Vol. 199, pp. 103796-103796
Closed Access | Times Cited: 4
Integrating crack pattern entropy measures with synthesized learners for accumulated seismic damage evaluation in reinforced concrete frames
Mostafa Kaboodkhani, Mohammadjavad Hamidia, Hamid Bayesteh
Advanced Engineering Informatics (2025) Vol. 65, pp. 103271-103271
Closed Access
Explainable Tuned Machine Learning Models for Assessing the Impact of Corrosion on Bond Strength in Concrete
Maryam Bypour, Alireza Mahmoudian, Mohammad Yekrangnia, et al.
Cleaner Engineering and Technology (2024) Vol. 23, pp. 100834-100834
Open Access | Times Cited: 3
Prediction and design of mechanical properties of origami-inspired braces based on machine learning
Jianguo Cai, Huafei Xu, Jiacheng Chen, et al.
AI in Civil Engineering (2024) Vol. 3, Iss. 1
Open Access
Data-driven nonmodel seismic assessment of eccentrically braced frames with soil-structure interaction
Mahshad Jamdar, Kiarash M. Dolatshahi, Omid Yazdanpanah
Engineering Applications of Artificial Intelligence (2024) Vol. 139, pp. 109549-109549
Closed Access
Two-stage prediction of drift ratio limits of corroded RC columns based on interpretable machine learning methods
Yan Zhou, Yihong Qiu, Liuzhuo Chen
Developments in the Built Environment (2024), pp. 100588-100588
Open Access
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