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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:

Testing machine learning models for seismic damage prediction at a regional scale using building-damage dataset compiled after the 2015 Gorkha Nepal earthquake
Subash Ghimire, Philippe Guéguen, Sophie Giffard‐Roisin, et al.
Earthquake Spectra (2022) Vol. 38, Iss. 4, pp. 2970-2993
Open Access | Times Cited: 41

Showing 1-25 of 41 citing articles:

Empirical seismic vulnerability assessment model of typical urban buildings
Si-Qi Li, Yongsheng Chen, Hongbo Liu, et al.
Bulletin of Earthquake Engineering (2023) Vol. 21, Iss. 4, pp. 2217-2257
Closed Access | Times Cited: 53
Investigation of seismic damage to existing buildings by using remotely observed images
Roberto Nascimbene
Engineering Failure Analysis (2024) Vol. 161, pp. 108282-108282
Closed Access | Times Cited: 21
Deep artificial intelligence applications for natural disaster management systems: A methodological review
Akhyar Akhyar, Mohd Asyraf Zulkifley, Jaesung Lee, et al.
Ecological Indicators (2024) Vol. 163, pp. 112067-112067
Open Access | Times Cited: 19
Machine-Learning Applications in Structural Response Prediction: A Review
A. Afshar, Gholamreza Nouri, Shahin Ghazvineh, et al.
Practice Periodical on Structural Design and Construction (2024) Vol. 29, Iss. 3
Closed Access | Times Cited: 6
Quantum‐enhanced machine learning technique for rapid post‐earthquake assessment of building safety
Sanjeev Bhatta, Ji Dang
Computer-Aided Civil and Infrastructure Engineering (2024)
Open Access | Times Cited: 6
Machine learning prediction models for ground motion parameters and seismic damage assessment of buildings at a regional scale
Sanjeev Bhatta, Xiandong Kang, Ji Dang
Resilient Cities and Structures (2024) Vol. 3, Iss. 1, pp. 84-102
Open Access | Times Cited: 4
Ensemble technique to predict post-earthquake damage of buildings integrating tree-based models and tabular neural networks
Zhonghao Li, Hao Lei, Enlin Ma, et al.
Computers & Structures (2023) Vol. 287, pp. 107114-107114
Closed Access | Times Cited: 11
Machine Learning-Based Classification for Rapid Seismic Damage Assessment of Buildings at a Regional Scale
Sanjeev Bhatta, Ji Dang
Journal of Earthquake Engineering (2023) Vol. 28, Iss. 7, pp. 1861-1891
Closed Access | Times Cited: 10
Machine learning predictions of code-based seismic vulnerability for reinforced concrete and masonry buildings: Insights from a 300-building database
Angelo Aloisio, Yuri De Santis, Francesco Irti, et al.
Engineering Structures (2023) Vol. 301, pp. 117295-117295
Open Access | Times Cited: 10
Screening for Anomalous Safety Condition Among Existing Buildings Using Explainable Machine Learning
Jie Liu, Guiwen Liu, Neng Wang, et al.
Structural Control and Health Monitoring (2025) Vol. 2025, Iss. 1
Open Access
Earthquake-Induced Damage Grade Prediction in Buildings Using Machine Learning
J. Blessy Karunya, S. Varshini, R. Jasmitha, et al.
Lecture notes in electrical engineering (2025), pp. 521-530
Closed Access
Explainable seismic damage prediction model based on CELS-WOA-Stacking
Yi Gu, Shichao Yang, Xu Zhou, et al.
Advanced Engineering Informatics (2025) Vol. 66, pp. 103430-103430
Closed Access
Estimation of economic loss and recover process after earthquake base on nighttime light data and time series model
Jinpeng Zhao, Xiaojun Li, Su Chen
Reliability Engineering & System Safety (2025), pp. 111244-111244
Closed Access
Seismic Hazard Loss Assessment of Reservoir Dams Based on Random Forest Algorithm
X. Chen, Yonggang Guo
Natural Hazards Review (2025) Vol. 26, Iss. 3
Closed Access
Enhancing seismic assessment and risk management of buildings: A neural network-based rapid visual screening method development
Nurullah Bektaş, Orsolya Kegyes-Brassai
Engineering Structures (2024) Vol. 304, pp. 117606-117606
Open Access | Times Cited: 3
Over-sampling for data augmentation in data-driven models for the shear strength prediction of RC membranes
Luis Alberto Bedriñana, Jostin Gabriel Landeo, Julio Sucasaca, et al.
Structures (2024) Vol. 60, pp. 105870-105870
Closed Access | Times Cited: 2
Machine learning model for building seismic peak roof drift ratio assessment
Federico Mori, Daniele Spina, Flavio Bocchi, et al.
Geomatics Natural Hazards and Risk (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 4
A machine learning approach to formation of earthquake categories using hierarchies of magnitude and consequence to guide emergency management
Donald Douglas Atsa’am, Terlumun Gbaden, Ruth Wario
Data Science and Management (2023) Vol. 6, Iss. 4, pp. 208-213
Open Access | Times Cited: 4
Host-to-target region testing of machine learning models for seismic damage prediction in buildings
Subash Ghimire, Philippe Guéguen
Natural Hazards (2024) Vol. 120, Iss. 5, pp. 4563-4579
Open Access | Times Cited: 1
Applying Machine Learning to Earthquake Engineering: A Scientometric Analysis of World Research
Yi Hu, Wentao Wang, Lei Li, et al.
Buildings (2024) Vol. 14, Iss. 5, pp. 1393-1393
Open Access | Times Cited: 1
A multi-level damage assessment model based on change detection technology in remote sensing images
Dongzhe Han, Guang Yang, Wangze Lu, et al.
Natural Hazards (2024)
Closed Access | Times Cited: 1
Testing machine learning models for heuristic building damage assessment applied to the Italian Database of Observed Damage (DaDO)
Subash Ghimire, Philippe Guéguen, Adrien Pothon, et al.
(2023)
Open Access | Times Cited: 3
Machine Learning Assessment of Damage Grade for Post-Earthquake Buildings: A Three-Stage Approach Directly Handling Categorical Features
Yutao Li, Chuanguo Jia, Hong Chen, et al.
Sustainability (2023) Vol. 15, Iss. 18, pp. 13847-13847
Open Access | Times Cited: 3
Random forest-based multi-hazard loss estimation using hypothetical data at seismic and tsunami monitoring networks
Yao Li, Katsuichiro Goda
Geomatics Natural Hazards and Risk (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 3
Development of a seismic loss prediction model for residential buildings using machine learning – Ōtautahi / Christchurch, New Zealand
Samuel Roeslin, Quincy Ma, Pavan Chigullapally, et al.
Natural hazards and earth system sciences (2023) Vol. 23, Iss. 3, pp. 1207-1226
Open Access | Times Cited: 1
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