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Journals / International Journal of Pure and Applied Sciences / 2026 / Volume: 12 Issue: 1

Seismic performance prediction of reinforced concrete buildings using the RYTEIE method: a comparison of machine learning models

Rabia Nur Sağlam(FIRAT UNIVERSITY)0000-0003-3015-0766
Muhammed Veysi Güler(FIRAT UNIVERSITY)0009-0002-8350-9808
Mahmut Kaya(FIRAT UNIVERSITY)0000-0002-7846-1769
Mustafa Ulaş(FIRAT UNIVERSITY)0000-0002-0096-9693
Kürşat Esat Alyamaç(FIRAT UNIVERSITY)0000-0002-3226-4073
Pages
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Publication Date
2026-06-22
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results indicate that the Random Forest model predicts seismic performance scores with high accuracy.

📌 3 Key Takeaways

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Objective & Hypothesis:This study aims to predict seismic performance scores defined under the “Principles for the Identification of Risky Structures” using a final dataset of 3,543 buildings, obtained after preprocessing raw data from 4,200 reinforced concrete buildings in central Elazığ.
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Key Findings & Data:Input parameters included building identification data, geometric characteristics, and structural irregularities, while the seismic performance score was the output.
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Conclusion & Impact:Results indicate that the Random Forest model predicts seismic performance scores with high accuracy.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Machine Learning · Seismic Performance Prediction · RYTEIE

Abstract

This study aims to predict seismic performance scores defined under the “Principles for the Identification of Risky Structures” using a final dataset of 3,543 buildings, obtained after preprocessing raw data from 4,200 reinforced concrete buildings in central Elazığ. Various supervised machine learning models were compared. Input parameters included building identification data, geometric characteristics, and structural irregularities, while the seismic performance score was the output. Models such as KNN and Random Forest were trained and evaluated using regression metrics including R-squared (R²), mean absolute error (MAE), and mean squared error (MSE). Results indicate that the Random Forest model predicts seismic performance scores with high accuracy. These findings suggest that such approaches can serve as fast, effective, and reliable decision-support tools for post-earthquake building risk prioritization. Additionally, they help reduce time and labor costs in field data collection, contributing to more efficient disaster management and improved urban resilience. The study is expected to support urban transformation and disaster management strategies in earthquake-prone regions like Elazığ.

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Cite This Article

Sağlam, R. N., Güler, M. V., Kaya, M., Ulaş, M., Alyamaç, K. E. (2026). Seismic performance prediction of reinforced concrete buildings using the RYTEIE method: a comparison of machine learning models. International Journal of Pure and Applied Sciences, Volume: 12 Issue: 1. https://doi.org/10.29132/ijpas.1928087

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References (18)

  1. M. Marangoz and Ç. İzci, “Evaluation of the economic, social and environmental impacts of natural disasters in the context of the February 6, 2023 Kahramanmaraş earthquakes from the perspective of entrepreneurs [in Turkish],” SOBBİAD, vol. 24, no. 52, Art. no. 52, Jun. 2023.
  2. J. Rainer, D. Allen, and A. Jablonski, “Manual for Screening of Buildings for Seismic Investigation,” Jan. 1992.
  3. “Principles for the Identification of Risky Buildings [in Turkish].” Accessed: Apr. 11, 2026. [Online]. Available: https://webdosya.csb.gov.tr/db/altyapi/icerikler/r-skl--yapilarin-tesp-t-ed-lmes-ne-il-sk-n-esaslar-20190218134628.pdf
  4. H. Salehi and R. Burgueño, “Emerging artificial intelligence methods in structural engineering,” Engineering Structures, vol. 171, pp. 170–189, Sep. 2018, doi: 10.1016/j.engstruct.2018.05.084.
  5. A. Utku, M. Kaya, and Y. Canbay, “A New Hybrid ConvViT Model for Dangerous Farm Insect Detection,” Applied Sciences, vol. 15, no. 5, p. 2518, Jan. 2025, doi: 10.3390/app15052518.
  6. E. Harirchian et al., “A review on application of soft computing techniques for the rapid visual safety evaluation and damage classification of existing buildings,” Journal of Building Engineering, vol. 43, p. 102536, Nov. 2021, doi: 10.1016/j.jobe.2021.102536.
  7. A. Çevik, A. E. Kurtoğlu, M. Bilgehan, M. E. Gülşan, and H. M. Albegmprli, “Support vector machines in structural engineering: a review,” Journal of Civil Engineering and Management, vol. 21, no. 3, pp. 261–281, Feb. 2015, doi: 10.3846/13923730.2015.1005021.
  8. Ö. F. Nemutlu, A. Sari, and B. Balun, “A Novel Approach to Seismic Vulnerability Assessment of Existing Residential Reinforced Concrete Buildings Stock: A Case Study for Bingöl, Turkey,” Iran J Sci Technol Trans Civ Eng, vol. 47, no. 6, pp. 3609–3625, Dec. 2023, doi: 10.1007/s40996-023-01206-7.
  9. Ö. F. Nemutlu, “Multiple Regression-Based Prediction Method to Assess the Impact of PGA and Distance on Post-Earthquake Structural Damage Levels,” TJNS, vol. 14, no. 2, pp. 37–51, Jun. 2025, doi: 10.46810/tdfd.1605168.
  10. A. Silahtar, M. Senkaya, and H. Karaaslan, “Multi-criteria decision-making (MCDM) model for building damages in the Kahramanmaraş 2023 earthquakes sequence based on soil, ground motion and earthquake source-path parameters,” Bull Earthquake Eng, vol. 23, no. 12, pp. 5137–5159, Sep. 2025, doi: 10.1007/s10518-025-02139-4.
  11. E. Harirchian, V. Kumari, K. Jadhav, R. Raj Das, S. Rasulzade, and T. Lahmer, “A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings,” Applied Sciences, vol. 10, no. 20, p. 7153, Jan. 2020, doi: 10.3390/app10207153.
  12. Ö. F. Nemutlu, “Investigation of earthquake performance, structural risks and losses in buildings in the city center of Bingöl [in Turkish],” Jun. 2023, Accessed: Apr. 11, 2026. [Online]. Available: http://hdl.handle.net/11527/24214
  13. A. Géron, “Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow,” 2009.
  14. R. Çekik and M. Kaya, “A New Feature Selection Metric Based on Rough Sets and Information Gain in Text Classification,” GU J Sci, Part A, vol. 10, no. 4, pp. 472–486, Dec. 2023, doi: 10.54287/gujsa.1379024.
  15. N. S. Altman, “An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression,” The American Statistician, vol. 46, no. 3, pp. 175–185, Aug. 1992, doi: 10.1080/00031305.1992.10475879.
  16. L. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, Oct. 2001, doi: 10.1023/A:1010933404324.
  17. T. Sefer and M. Kaya, “Detection of Dust on Solar Panels with Deep Learning,” KSU J. Eng. Sci., vol. 27, no. 4, pp. 1451–1464, Dec. 2024, doi: 10.17780/ksujes.1493906.
  18. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning. in Springer Series in Statistics. New York, NY: Springer, 2009. doi: 10.1007/978-0-387-84858-7.