journal article·2026-06

A stacked ensemble model for rapid seismic damage assessment of masonry buildings using data from the February 2023, Kahramanmaraş, Türkiye earthquakes

Sara Mostofi, Hasan Basri Başağa, Fatih Yesevi okur, Zafer Yilmaz, Ertuğrul Taciroğlu, Tuğrul Sezdirmez, Ahmet Can Altunişik

Publisher page

Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

Source: Not provided · Quality: missing

Research context

OpenAlex topics: Masonry and Concrete Structural Analysis · Seismic Performance and Analysis · Structural Health Monitoring Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (62)

Show source-provided references
1. Field Investigation of the Performance of Masonry buildings during the October 23 and November 9, 2011, Van Earthquakes in Turkey10.1061/(ASCE)CF.1943-5509.0000383 ↗
2. M. Günaydin, B. Atmaca, S. Demir, A.C. Altunişik, M. Hüsem, S. Adanur, Ş. Ateş, Z. Angin, Seismic damage assessment of masonry buildings in Elazığ and Malatya following the 2020 Elazığ-Sivrice earthquake, Turkey, Bulletin of Earthquake Engineering 19 (2021) 2421–2456. Doi: 10.1007/s10518-021-01073-5.10.1007/s10518-021-01073-5 ↗
3. Ö. Mercimek, Seismic failure modes of masonry structures exposed to Kahramanmaraş earthquakes (Mw 7.7 and 7.6) on February 6, 2023, Eng. Fail. Anal. 151 (2023) 107422. Doi: 10.1016/j.engfailanal.2023.107422.10.1016/j.engfailanal.2023.107422 ↗
4. Evaluation of earthquake-related damages on masonry structures due to the 6, Kahramanmaraş-Türkiye earthquakes: a case study for Hatay Governorship Building
5. Assessment of Structural damage following the October 30, 2020 Aegean Sea Earthquake and Tsunami10.1142/S1793431121500299 ↗
6. Performance of masonry structures during the, Northridge (Los Angeles) earthquake
7. Damage to masonry buildings caused by the, Hyogo-ken Nanbu (Kobe, Japan) earthquake
8. Field investigation on the performance of building structures during the 12, Wenchuan earthquake in China
9. Post-Earthquake Reconnaissance of Unreinforced and Retrofitted Masonry Parapets10.1193/121715EQS184M ↗
10. Post-earthquake damage simulation of two Colonial Unreinforced Clay Brick Masonry buildings using the Equivalent Frame Approach10.1016/j.istruc.2019.01.010 ↗
11. Post-earthquake damage assessment of traditional masonry buildings: a case study of Bhaktapur municipality following 2015 Gorkha (Nepal) earthquake10.1016/j.engfailanal.2021.105277 ↗
12. Performance of Posttensioned Seismic Retrofit of two Stone Masonry buildings during the Canterbury Earthquakes10.1061/(ASCE)CF.1943-5509.0000603 ↗
13. Post-earthquake diagnostic investigation of a historic masonry tower10.1016/j.culher.2014.09.002 ↗
14. Post-Earthquake Assessment of a Historical Masonry Building after the Zagreb Earthquake—Case Study10.3390/buildings12030323 ↗
15. Construction safety predictions with multi-head attention graph and sparse accident networks10.1016/j.autcon.2023.105102 ↗
16. Deep learning-based classification of earthquake-impacted buildings using textual damage descriptions10.1016/j.ijdrr.2019.101111 ↗
17. Classifying earthquake damage to buildings using machine learning10.1177/8755293019878137 ↗
18. Rapid earthquake loss assessment based on machine learning and representative sampling10.1177/87552930211042393 ↗
19. The use of machine-learning methods for post-earthquake building usability assessment: a predictive model for seismic-risk impact analyses10.1016/j.ijdrr.2023.104033 ↗
20. Earthquake damage and rehabilitation intervention prediction using machine learning
21. A real‐time seismic damage prediction framework based on machine learning for earthquake early warning10.1002/eqe.4029 ↗
22. Machine learning-based estimation of the out-of-plane displacement of brick infill exposed to earthquake shaking10.1016/j.engappai.2024.109007 ↗
23. A machine learning damage prediction model for the, Puebla-Morelos, Mexico, earthquake
24. Testing machine learning models for seismic damage prediction at a regional scale using building-damage dataset compiled after the, Gorkha Nepal earthquake
25. Machine Learning-based Rapid Post-Earthquake damage Detection of RC Resisting-Moment Frame buildings10.3390/s23104694 ↗
26. Seismic damage Assessment of RC Structures after the 2015 Gorkha
27. Evaluation of Machine Learning and Web-based Process for damage score Estimation of existing buildings10.3390/buildings12050578 ↗
28. ML-EHSAPP: a prototype for machine learning-based earthquake hazard safety assessment of structures by using a smartphone app10.1080/19648189.2021.1892829 ↗
29. Rapid seismic damage-state assessment of steel moment frames using machine learning10.1016/j.engstruct.2021.113737 ↗
30. Building-damage detection method based on machine learning utilizing aerial photographs of the Kumamoto earthquake10.1177/8755293019901309 ↗
31. Earthquake-Induced Building-damage Mapping using Explainable AI (XAI)10.3390/s21134489 ↗
32. Transferability of Convolutional Neural Network Models for Identifying Damaged buildings due to Earthquake, Remote Sens
33. A novel attention-based deep learning method for post-disaster building damage classification10.1016/j.eswa.2022.117268 ↗
34. Stacking: a novel data-driven ensemble machine learning strategy for prediction and mapping of Pb-Zn prospectivity in Varcheh district, west Iran10.1016/j.eswa.2023.121668 ↗
35. Distraction-level recognition based on stacking ensemble learning for IVIS secondary tasks10.1016/j.eswa.2023.122849 ↗
36. Standard fetal ultrasound plane classification based on stacked ensemble of deep learning models10.1016/j.eswa.2023.122153 ↗
37. A stacking-based ensemble learning method for earthquake casualty prediction10.1016/j.asoc.2020.107038 ↗
38. Application of a new machine learning model to improve earthquake ground motion predictions10.1007/s11069-023-06230-4 ↗
39. A stacking learning-based method for identifying the structural damage in structures10.1016/j.istruc.2024.107864 ↗
40. A hybrid stacked ensemble model for rapid seismic damage assessment with imbalanced training data: a case study on the 2023 Kahramanmaraş earthquakes10.1016/j.engstruct.2025.120754 ↗
41. Automatic Detection of Collapsed buildings after the 6 February 2023 Türkiye Earthquakes using Post-disaster Satellite Images with Deep Learning-based Semantic Segmentation Models10.3390/buildings14030582 ↗
42. M.E. Yildirim, C. Yesilyurt, U. Gozun, B. Ozturk, C. Donmez, Structural Performance of R/C Buildings in 2023 Kahramanmaraş Earthquakes Under the Lens of Hassan Index, in: 2024: pp. 99–114. Doi: 10.1007/978-3-031-57659-1_7.10.1007/978-3-031-57659-1_7 ↗
43. Kahramanmaraş (Mw 7.7 Pazarcık and Mw 7.6 Elbistan) Earthquakes in Türkiye
44. Disaster waste management process: the case of February 6 earthquake10.31462/jcemi.2024.01034055 ↗
45. Field reconnaissance on seismic performance and functionality of Turkish industrial facilities affected by the Kahramanmaras earthquake sequence
46. Monitored seismic performance assessment of cable–stayed bridges during the 2023 Kahramanmaraş earthquakes (M7.7 and M7.6)10.1007/s13349-024-00807-8 ↗
47. Earthquake doublet in Turkey and Syria10.1038/s43247-023-00747-z ↗
48. Performance and damages of reinforced concrete buildings during the October 23 and November 9, 2011 Van, Turkey, earthquakes10.1016/j.soildyn.2013.06.004 ↗
49. Determination of mechanical properties of concrete and steel materials taken from buildings in Antakya after the Kahramanmaraş earthquakes
50. Seismic damage assessment of under-construction industrial buildings: Insights from the february 2023 Türkiye-Syria earthquakes
51. Regional Building damage Survey Data on the 2023 Kahramanmaraş, Türkiye, Earthquakes
52. Relationships between Peak Ground Acceleration, Peak Ground Velocity, and Modified Mercalli Intensity in California10.1193/1.1586058 ↗
53. A framework for feature selection through boosting10.1016/j.eswa.2021.115895 ↗
54. Stacked generalization10.1016/S0893-6080(05)80023-1 ↗
55. A comparative study of several classification metrics and their performances on data10.30574/wjaets.2023.8.1.0054 ↗
56. Comparative Analysis using Various Performance Metrics in Imbalanced Data for Multi-class Text Classification
57. The Matthews correlation coefficient(MCC) should replace the ROCAUC as the standard metric for assessing binary classification10.1186/s13040-023-00322-4 ↗
58. Generating synthetic data with variational autoencoder to address class imbalance of graph attention network prediction model for construction management10.1016/j.aei.2024.102606 ↗
59. Machine learning for the prediction of problems in steel tube bending process10.1016/j.engappai.2024.108584 ↗
60. A big Data-Enabled Decision support Model for Post-Earthquake damage Classification of RC buildings: a Case Study on February 6, Kahramanmaraş Doublet Earthquakes10.1080/13632469.2025.2505974 ↗
61. Abrahams · NIST-FEMA Post-Earthquake Functional Recovery Workshop Report · 202110.6028/NIST.SP.1269 ↗
62. Beyzaei · NIST Transportation Systems and Functional Recovery Workshop Report · 202310.6028/NIST.SP.1295 ↗