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Total Dissipated Energy Prediction for Flexure- Dominated Reinforced Concrete Columns via Extreme Gradient Boosting

Year 2025, Volume: 25 Issue: 3, 604 - 612, 10.06.2025

Ziya Müderrisoğlu

https://doi.org/10.35414/akufemubid.1541763

Abstract

This study aims to provide an efficient framework for predicting the total dissipated energy level of flexure-dominated reinforced concrete columns via a commonly used machine learning method, extreme gradient boosting. A database including 177 reinforced concrete columns is compiled using open-access databases available in the literature. The proposed framework predicts the target total dissipated energy level depending on seven fundamental features: concrete compressive strength, longitudinal rebar yield strength, shear span-to-depth ratio, longitudinal rebar ratio, transverse rebar volumetric ratio, peak drift ratio, and equivalent damping ratio. Here, a correlation-based quantitative analysis is performed to reveal the effects of selected features on the total dissipated energy capacity. It is observed that the peak drift ratio, yield strength of longitudinal rebars, and concrete compressive strength are the most effective parameters among the other features. K-Fold cross-validation is implemented for the classification process. Validation results show that the three fundamental performance indicators such as the means of correlation of determination, the normalized root mean square error, and the mean absolute percentage error are evaluated as 0.75, 0.38, and 0.33, respectively. The sensitivity of predicted targets to algorithm-based hyperparameters is also investigated. The results of this study are expected to contribute to the energy-based design applications in the scope of predicting the dissipated energy capacity of flexure-dominated reinforced concrete column members.

Keywords

Energy-based design Total dissipated energy XGBoost Reinforced concrete columns

References

  • Abdalla, J.A. and Hawileh, R.A., 2021. Assessment of effect of strain amplitude and strain ratio on energy dissipation using machine learning. In Proceedings of the 18th International Conference on Computing in Civil and Building Engineering: ICCCBE 2020, Springer International Publishing, 98-108. https://doi.org/10.1007/978-3-030-51295-8_9
  • Acun, B. and Sucuoğlu, H., 2012. Energy dissipation capacity of reinforced concrete columns under cyclic displacements. ACI Structural Journal, 109(4). https://doi.org/10.14359/51683872
  • Anguita, D., Ghelardoni, L., Ghio, A., Oneto, L. and Ridella, S., 2012. The 'K' in K-fold Cross Validation. ESANN, 102, 441-446.
  • Chen, T. and Guestrin, C., 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining.
  • Chicco, D., Warrens, M. J. and Jurman, G. 2021. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. Peerj computer science, 7. https://doi.org/10.7717/peerj-cs.623
  • Deger, Z., Taşkın Kaya, G. and Sütcü, F., 2023. Investigation of the energy dissipation capacity of RC shear walls using meta-modeling methods. Journal of the Faculty of Engineering and Architecture of Gazi University, 38(4), 2311-2324. https://doi.org/10.17341/gazimmfd.1117820
  • Friedman, J. H., 2001. Greedy function approximation: a gradient boosting machine. Annals of Statistics, 1189-1232. https://doi.org/10.1214/aos/1013203451
  • Ghannoum, W., Sivaramakrishnan, B., Pujol, S., Catlin, A.C., Wang, Y., Yoosuf, N. and Fernando, S. 2015a. NEES: ACI 369 Circular column database. https://doi.org/10.4231/D39Z90B9T
  • Ghannoum, W., Sivaramakrishnan, B., Pujol, S., Catlin, A.C., Wang, Y., Yoosuf, N. and Fernando, S. 2015b. NEES: ACI 369 Rectangular column database. https://doi.org/ 10.4231/D36688J50
  • Hamidia, M., Kaboodkhani, M. and Bayesteh, H., 2024. Vision-oriented machine learning-assisted seismic energy dissipation estimation for damaged RC beam-column connections. Engineering Structures, 305. https://doi.org/10.1016/j.engstruct.2023.117345
  • Kwan, W.P. and Billington, S.L. 2003. Influence of hysteretic behavior on equivalent period and damping of structural systems. Journal of Structural Engineering, 129(5), 576-585. https://doi.org/10.1061/(ASCE)0733-9445(2003)129:5(576)
  • Liu, Z., Wang, Y., Cao, Z., Chen, Y. and Hu, Y., 2018. Seismic energy dissipation under variable amplitude loading for rectangular RC members in flexure. Earthquake Engineering and Structural Dynamics, 47(4), 831-853. https://doi.org/10.1002/eqe.2993
  • Moore, D.S., Notz, W.I. and Flinger, M.A., 2013. The basic practice of statistics (6th edition). New York, NY: W. H. Freeman and Company.
  • Muderrisoglu, Z., Dindar, A.A., Bozer, A., Özkaynak, H., Güllü, A., Güngör, B., Çalım, F. and Hasanoğlu, S., 2023. A quantitative investigation on the effects of flexure-dominated reinforced concrete column characteristics on the dissipated energy. Turkish Journal of Civil Engineering, 35(2), 87-102. https://doi.org/10.18400/tjce.1272125
  • Park, H. and Eom, T., 2006. A simplified method for estimating the amount of energy dissipated by flexure-dominated reinforced concrete members for moderate cyclic deformations. Earthquake Spectra, 22(2), 459-490. https://doi.org/10.1193/1.2197547
  • Pearson, K., 1920. Notes on the history of correlation. Biometrika, 13(1), 25-45. https://doi.org/10.2307/2331722
  • Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... and Duchesnay, É., 2011. Scikit-learn: Machine learning in Python. Journal of machine Learning research, 12, 2825-2830. https://doi.org/10.48550/arXiv.1201.0490
  • Poljanšek, K., Peruš, I. and Fajfar, P., 2009. Hysteretic energy dissipation capacity and the cyclic to monotonic drift ratio for rectangular RC columns in flexure. Earthquake Engineering and Structural Dynamics, 38(7), 907-928. https://doi.org/10.1002/eqe.875
  • Tanaka, H., 1990. Effect of lateral confining reinforcement on the ductile behaviour of reinforced concrete columns. Thesis, Doctor of Philosophy, University of Canterbury, Civil Engineering. https://doi.org/10.26021/3137
  • Tapeh, A.T.G. and Naser, M.Z., 2023. Artificial intelligence, machine learning, and deep learning in structural engineering: a scientometrics review of trends and best practices. Archives of Computational Methods in Engineering, 30(1), 115-159. https://doi.org/10.1007/s11831-022-09793-w
  • Thai, H.T., 2022. Machine learning for structural engineering: A state-of-the-art review. Structures, 38, 448-491. https://doi.org/10.1016/j.istruc.2022.02.003
  • Topaloglu, B., Kaya, G.T., Sutcu, F. and Deger, Z.T., 2022. Machine learning-based estimation of energy dissipation capacity of RC shear walls. Structures, 45, 343-352. https://doi.org/10.1016/j.istruc.2022.08.114
  • Van Rossum, G. and Drake, F.L., 1995. Python reference manual. Amsterdam: Centrum voor Wiskunde en Informatica, 111, 1-52.
  • Vu, N.S., Li, B. and Tran, C.T.N., 2022. Seismic behavior of reinforced concrete short columns subjected to varying axial load. ACI Structural Journal, 119(6), 99-112. https://doi.org/10.14359/51736108
  • Yaghoubi, S.T., Deger, Z.T., Taskin, G. and Sutcu, F., 2023. Machine learning-based predictive models for equivalent damping ratio of RC shear walls. Bulletin of Earthquake Engineering, 21(1). https://doi.org/10.1007/s10518-022-01533-6
  • Yalçın, C., Dindar, A.A., Yüksel, E., Özkaynak, H. and Büyüköztürk, O., 2021. Seismic design of RC frame structures based on energy-balance method. Engineering Structures, 237, 112220. https://doi.org/10.1016/j.engstruct.2021.112220
  • Yang, S. Y., Song, X. B., Jia, H. X., Chen, X. and Liu, X. L., 2016. Experimental research on hysteretic behaviors of corroded reinforced concrete columns with different maximum amounts of corrosion of rebar. Construction and Building Materials, 121, 319-327. https://doi.org/10.1016/j.conbuildmat.2016.06.002
  • Yıldızel, S.A., Özkılıç, Y.O., Bahrami, A., Aksoylu, C., Başaran, B., Hakamy, A. and Arslan, M.H., 2023. Experimental investigation and analytical prediction of flexural behaviour of reinforced concrete beams with steel fibres extracted from waste tyres. Case Studies in Construction Materials, 19, e02227. https://doi.org/10.1016/j.cscm.2023.e02227

Eğilme Etkisi Altındaki Betonarme Kolonlar için Toplam Tüketilen Enerji Seviyesinin Aşırı Gradyan Artırma Yaklaşımı ile Tahmini

Year 2025, Volume: 25 Issue: 3, 604 - 612, 10.06.2025

Ziya Müderrisoğlu

https://doi.org/10.35414/akufemubid.1541763

Abstract

Bu çalışma, betonarme kolonlarda tüketilen toplam enerji seviyesinin uygulamalarda yaygın olarak kullanılan aşırı gradian artırma yaklaşımı ile tahminine yönelik etkin bir algoritma önerilmesini amaçlamaktadır. Bu kapsamda, literatürde bulunan açık erişimli veri tabanları kullanılarak 177 adet betonarme kolona ait gerekli özellikleri içeren bir veri tabanı derlenmiştir. Öne sürülen çerçeve, hedef toplam tüketilen enerji seviyesini 7 temel özelliğe bağlı olarak tahmine olanak sağlamaktadır: beton basınç dayanımı, boyuna donatı akma dayanımı, kesme açıklığı-derinlik oranı, boyuna donatı oranı, enine donatı hacimsel oranı, maksimum ötelenme oranı ve eşdeğer sönüm oranı. Burada, seçilen özelliklerin toplam tüketilen enerji seviyesi üzerindeki etkilerini ortaya koyabilmek amacıyla korelasyon esaslı sayısal analizler gerçekleştirilmiştir. Analizler sonucunda, seçilen özellikler arasında toplam tüketilen enerji seviyesi üzerinde en etkin olan parametrelerin maksimum ötelenme oranı, boyuna donatı akma dayanımı ve beton dayanımı olduğu belirlenmiştir. Verilerin sınıflandırılması sürecinde K-katlı çapraz geçerlilik yaklaşımı uygulanmıştır. Geçerlilik sonuçları, üç temel performans göstergesine (belirleme katsayısı, normalize edilmiş kök ortalama kare hatası ve ortalama mutlak yüzde hatası) ait ortalama değerlerin sırasıyla 0.75, 0.38 ve 0.33 olarak belirlendiğini göstermiştir. Çalışma kapsamında, tahmin edilen enerji seviyelerinin algoritma bazlı parametrelere bağlı hassasiyet seviyeleri de araştırılmıştır. Çalışma sonuçlarının, özellikle son yıllarda çalışmaların yoğunlaştığı enerji esaslı tasarım uygulamalarına, eğilme etkisi altındaki betonarme kolon elemanlarda tüketilen toplam enerji seviyesinin tahmini kapsamında katkı sağlayacağı düşünülmektedir.

Keywords

Enerji-esaslı tasarım Toplam tüketilen enerji seviyesi Aşırı gradyan artırma yaklaşımı Betonarme kolonlar

References

  • Abdalla, J.A. and Hawileh, R.A., 2021. Assessment of effect of strain amplitude and strain ratio on energy dissipation using machine learning. In Proceedings of the 18th International Conference on Computing in Civil and Building Engineering: ICCCBE 2020, Springer International Publishing, 98-108. https://doi.org/10.1007/978-3-030-51295-8_9
  • Acun, B. and Sucuoğlu, H., 2012. Energy dissipation capacity of reinforced concrete columns under cyclic displacements. ACI Structural Journal, 109(4). https://doi.org/10.14359/51683872
  • Anguita, D., Ghelardoni, L., Ghio, A., Oneto, L. and Ridella, S., 2012. The 'K' in K-fold Cross Validation. ESANN, 102, 441-446.
  • Chen, T. and Guestrin, C., 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining.
  • Chicco, D., Warrens, M. J. and Jurman, G. 2021. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. Peerj computer science, 7. https://doi.org/10.7717/peerj-cs.623
  • Deger, Z., Taşkın Kaya, G. and Sütcü, F., 2023. Investigation of the energy dissipation capacity of RC shear walls using meta-modeling methods. Journal of the Faculty of Engineering and Architecture of Gazi University, 38(4), 2311-2324. https://doi.org/10.17341/gazimmfd.1117820
  • Friedman, J. H., 2001. Greedy function approximation: a gradient boosting machine. Annals of Statistics, 1189-1232. https://doi.org/10.1214/aos/1013203451
  • Ghannoum, W., Sivaramakrishnan, B., Pujol, S., Catlin, A.C., Wang, Y., Yoosuf, N. and Fernando, S. 2015a. NEES: ACI 369 Circular column database. https://doi.org/10.4231/D39Z90B9T
  • Ghannoum, W., Sivaramakrishnan, B., Pujol, S., Catlin, A.C., Wang, Y., Yoosuf, N. and Fernando, S. 2015b. NEES: ACI 369 Rectangular column database. https://doi.org/ 10.4231/D36688J50
  • Hamidia, M., Kaboodkhani, M. and Bayesteh, H., 2024. Vision-oriented machine learning-assisted seismic energy dissipation estimation for damaged RC beam-column connections. Engineering Structures, 305. https://doi.org/10.1016/j.engstruct.2023.117345
  • Kwan, W.P. and Billington, S.L. 2003. Influence of hysteretic behavior on equivalent period and damping of structural systems. Journal of Structural Engineering, 129(5), 576-585. https://doi.org/10.1061/(ASCE)0733-9445(2003)129:5(576)
  • Liu, Z., Wang, Y., Cao, Z., Chen, Y. and Hu, Y., 2018. Seismic energy dissipation under variable amplitude loading for rectangular RC members in flexure. Earthquake Engineering and Structural Dynamics, 47(4), 831-853. https://doi.org/10.1002/eqe.2993
  • Moore, D.S., Notz, W.I. and Flinger, M.A., 2013. The basic practice of statistics (6th edition). New York, NY: W. H. Freeman and Company.
  • Muderrisoglu, Z., Dindar, A.A., Bozer, A., Özkaynak, H., Güllü, A., Güngör, B., Çalım, F. and Hasanoğlu, S., 2023. A quantitative investigation on the effects of flexure-dominated reinforced concrete column characteristics on the dissipated energy. Turkish Journal of Civil Engineering, 35(2), 87-102. https://doi.org/10.18400/tjce.1272125
  • Park, H. and Eom, T., 2006. A simplified method for estimating the amount of energy dissipated by flexure-dominated reinforced concrete members for moderate cyclic deformations. Earthquake Spectra, 22(2), 459-490. https://doi.org/10.1193/1.2197547
  • Pearson, K., 1920. Notes on the history of correlation. Biometrika, 13(1), 25-45. https://doi.org/10.2307/2331722
  • Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... and Duchesnay, É., 2011. Scikit-learn: Machine learning in Python. Journal of machine Learning research, 12, 2825-2830. https://doi.org/10.48550/arXiv.1201.0490
  • Poljanšek, K., Peruš, I. and Fajfar, P., 2009. Hysteretic energy dissipation capacity and the cyclic to monotonic drift ratio for rectangular RC columns in flexure. Earthquake Engineering and Structural Dynamics, 38(7), 907-928. https://doi.org/10.1002/eqe.875
  • Tanaka, H., 1990. Effect of lateral confining reinforcement on the ductile behaviour of reinforced concrete columns. Thesis, Doctor of Philosophy, University of Canterbury, Civil Engineering. https://doi.org/10.26021/3137
  • Tapeh, A.T.G. and Naser, M.Z., 2023. Artificial intelligence, machine learning, and deep learning in structural engineering: a scientometrics review of trends and best practices. Archives of Computational Methods in Engineering, 30(1), 115-159. https://doi.org/10.1007/s11831-022-09793-w
  • Thai, H.T., 2022. Machine learning for structural engineering: A state-of-the-art review. Structures, 38, 448-491. https://doi.org/10.1016/j.istruc.2022.02.003
  • Topaloglu, B., Kaya, G.T., Sutcu, F. and Deger, Z.T., 2022. Machine learning-based estimation of energy dissipation capacity of RC shear walls. Structures, 45, 343-352. https://doi.org/10.1016/j.istruc.2022.08.114
  • Van Rossum, G. and Drake, F.L., 1995. Python reference manual. Amsterdam: Centrum voor Wiskunde en Informatica, 111, 1-52.
  • Vu, N.S., Li, B. and Tran, C.T.N., 2022. Seismic behavior of reinforced concrete short columns subjected to varying axial load. ACI Structural Journal, 119(6), 99-112. https://doi.org/10.14359/51736108
  • Yaghoubi, S.T., Deger, Z.T., Taskin, G. and Sutcu, F., 2023. Machine learning-based predictive models for equivalent damping ratio of RC shear walls. Bulletin of Earthquake Engineering, 21(1). https://doi.org/10.1007/s10518-022-01533-6
  • Yalçın, C., Dindar, A.A., Yüksel, E., Özkaynak, H. and Büyüköztürk, O., 2021. Seismic design of RC frame structures based on energy-balance method. Engineering Structures, 237, 112220. https://doi.org/10.1016/j.engstruct.2021.112220
  • Yang, S. Y., Song, X. B., Jia, H. X., Chen, X. and Liu, X. L., 2016. Experimental research on hysteretic behaviors of corroded reinforced concrete columns with different maximum amounts of corrosion of rebar. Construction and Building Materials, 121, 319-327. https://doi.org/10.1016/j.conbuildmat.2016.06.002
  • Yıldızel, S.A., Özkılıç, Y.O., Bahrami, A., Aksoylu, C., Başaran, B., Hakamy, A. and Arslan, M.H., 2023. Experimental investigation and analytical prediction of flexural behaviour of reinforced concrete beams with steel fibres extracted from waste tyres. Case Studies in Construction Materials, 19, e02227. https://doi.org/10.1016/j.cscm.2023.e02227
There are 28 citations in total.

Details

Primary Language English
Subjects Civil Engineering (Other)
Journal Section Articles
Authors

Ziya Müderrisoğlu 0000-0003-1220-8047

Early Pub Date May 22, 2025
Publication Date June 10, 2025
Submission Date September 1, 2024
Acceptance Date December 9, 2024
Published in Issue Year 2025 Volume: 25 Issue: 3

Cite

APA Müderrisoğlu, Z. (2025). Total Dissipated Energy Prediction for Flexure- Dominated Reinforced Concrete Columns via Extreme Gradient Boosting. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 25(3), 604-612. https://doi.org/10.35414/akufemubid.1541763
AMA Müderrisoğlu Z. Total Dissipated Energy Prediction for Flexure- Dominated Reinforced Concrete Columns via Extreme Gradient Boosting. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. June 2025;25(3):604-612. doi:10.35414/akufemubid.1541763
Chicago Müderrisoğlu, Ziya. “Total Dissipated Energy Prediction for Flexure- Dominated Reinforced Concrete Columns via Extreme Gradient Boosting”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 25, no. 3 (June 2025): 604-12. https://doi.org/10.35414/akufemubid.1541763.
EndNote Müderrisoğlu Z (June 1, 2025) Total Dissipated Energy Prediction for Flexure- Dominated Reinforced Concrete Columns via Extreme Gradient Boosting. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 25 3 604–612.
IEEE Z. Müderrisoğlu, “Total Dissipated Energy Prediction for Flexure- Dominated Reinforced Concrete Columns via Extreme Gradient Boosting”, Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, vol. 25, no. 3, pp. 604–612, 2025, doi: 10.35414/akufemubid.1541763.
ISNAD Müderrisoğlu, Ziya. “Total Dissipated Energy Prediction for Flexure- Dominated Reinforced Concrete Columns via Extreme Gradient Boosting”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 25/3 (June 2025), 604-612. https://doi.org/10.35414/akufemubid.1541763.
JAMA Müderrisoğlu Z. Total Dissipated Energy Prediction for Flexure- Dominated Reinforced Concrete Columns via Extreme Gradient Boosting. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2025;25:604–612.
MLA Müderrisoğlu, Ziya. “Total Dissipated Energy Prediction for Flexure- Dominated Reinforced Concrete Columns via Extreme Gradient Boosting”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, vol. 25, no. 3, 2025, pp. 604-12, doi:10.35414/akufemubid.1541763.
Vancouver Müderrisoğlu Z. Total Dissipated Energy Prediction for Flexure- Dominated Reinforced Concrete Columns via Extreme Gradient Boosting. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2025;25(3):604-12.
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