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Assessing Column Stability: A Comparative Study of Machine Learning Regression Models for Shear Strength Prediction Skip to content Yıl 2024, Cilt: 8 Sayı: 1, 279 – 289, 18.07.2024
Aybike Özyüksel Çiftçioğlu
https://doi.org/10.56554/jtom.1401261
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Aci Structural Journal, 111. https://doi.org/10.14359/51686734 Karbasi Arani, K., Marefat, M., Amrollahi-Biucky, A., & Khanmohammadi, M. (2013). Experimental seismic evaluation of old concrete columns reinforced by plain bars. The Structural Design of Tall and Special Buildings, 22. https://doi.org/10.1002/tal.686 Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Proceedings of the 31st International Conference on Neural Information Processing Systems, 3149–3157, https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. Khalilpourazari, S., & Doulabi, H. (2021). Using reinforcement learning to forecast the spread of COVID-19 in France. 1–8. https://doi.org/10.1109/ICAS49788.2021.9551174 Khalilpourazari, S., & Hashemi Doulabi, H. (2022). A flexible robust model for blood supply chain network design problem. Annals of Operations Research. https://doi.org/10.1007/s10479-022-04673-9 Khalilpourazari, S., Mirzazadeh, A., Weber, G.-W., & Pasandideh, S. H. R. (2020). A robust fuzzy approach for constrained multi-product economic production quantity with imperfect items and rework process. Optimization, 69(1), 63–90. https://doi.org/10.1080/02331934.2019.1630625 Khalilpourazari, S., & Pasandideh, S. (2016). Bi-objective optimization of multi-product EPQ model with backorders, rework process and random defective rate. https://10.1109/INDUSENG.2016.7519346. Lam, S., Wu, B., Wong, Y., Wang, Z. Y., Liu, Z., & Li, C. (2003). Drift Capacity of Rectangular Reinforced Concrete Columns with Low Lateral Confinement and High-Axial Load. Journal of Structural Engineering-Asce – J STRUCT ENG-ASCE, 129. https://doi.org/10.1061/(ASCE)0733-9445(2003)129:6(733) Li, Y.-A., Huang, Y.-T., & Hwang, S.-J. (2014). Seismic Response of Reinforced Concrete Short Columns Failed in Shear. ACI Structural Journal, 111. https://doi.org/10.14359/51686780 Li, Y., Cao, S., & Jing, D.-H. (2018). Concrete Columns Reinforced with High-Strength Steel Subjected to Reversed Cycle Loading. ACI Structural Journal, 115. https://doi.org/10.14359/51701296 Liu, T., Wang, Z., Zeng, J., & Wang, J. (2021). Machine-learning-based models to predict shear transfer strength of concrete joints. Engineering Structures, 249, 113253. https://doi.org/https://doi.org/10.1016/j.engstruct.2021.113253 Ma, L., Zhou, C., Lee, D., & Zhang, J. (2022). Prediction of axial compressive capacity of CFRP-confined concrete-filled steel tubular short columns based on XGBoost algorithm. Engineering Structures, 260, 114239. https://doi.org/https://doi.org/10.1016/j.engstruct.2022.114239 Marefat, M. S., Khanmohammadi, M., Bahrani, M. K., & Goli, A. (2006). Experimental Assessment of Reinforced Concrete Columns with Deficient Seismic Details under Cyclic Load. Advances in Structural Engineering, 9(3), 337–347. https://doi.org/10.1260/136943306777641959 Melo, J., Varum, H., & Rossetto, T. (2015). Experimental cyclic behaviour of RC columns with plain bars and proposal for Eurocode 8 formula improvement. Engineering Structures, 88, 22–36. https://doi.org/10.1016/j.engstruct.2015.01.033 Mohammadi, M., & Khalilpourazari, S. (2017). Minimizing Makespan in a single machine scheduling problem with deteriorating jobs and learning effects. https://doi.org/10.1145/3056662.3056715 Moslemi, S., Mirzazadeh, A., Weber, G.-W., & Sobhanallahi, M. A. (2021). Integration of neural network and AP-NDEA model for performance evaluation of sustainable pharmaceutical supply chain. OPSEARCH. https://doi.org/10.1007/s12597-021-00561-1 Naser, M. Z. (2021). Mechanistically Informed Machine Learning and Artificial Intelligence in Fire Engineering and Sciences. Fire Technology, 1–44. https://doi.org/10.1007/s10694-020-01069-8 Naser, M. Z., & Ciftcioglu, A. O. (2022). Causal Discovery and Causal Learning for Fire Resistance Evaluation: Incorporating Domain Knowledge. https://doi.org/10.48550/arxiv.2204.05311 Naser, M. Z., & Çiftçioğlu, A. Ö. (2023). Revisiting Forgotten Fire Tests: Causal Inference and Counterfactuals for Learning Idealized Fire-Induced Response of RC Columns. Fire Technology, 59(4), 1761–1788. https://doi.org/10.1007/s10694-023-01405-8 9 Nguyen-Sy, T., Wakim, J., To, Q. D., Vu, M. N., Nguyen, T. D., & Nguyen, T. T. (2020). Predicting the compressive strength of concrete from its compositions and age using the extreme gradient boosting method. Construction and Building Materials, 260, 119757. https://doi.org/10.1016/j.conbuildmat.2020.119757 Nguyen, H., Vu, T., Vo, T. P., & Thai, H. T. (2021). Efficient machine learning models for prediction of concrete strengths. Construction and Building Materials, 266, 120950. https://doi.org/10.1016/j.conbuildmat.2020.120950 Özyüksel Çiftçioğlu, A. (2023). RAGN-L: A Stacked Ensemble Learning Technique for Classification of Fire- Resistant Columns. Expert Systems with Applications, 240(November 2023), 122491. https://doi.org/10.1016/j.eswa.2023.122491 Özyüksel Çiftçioğlu, A., & Naser, M. Z. (2022). Hiding in plain sight: What can interpretable unsupervised machine learning and clustering analysis tell us about the fire behavior of reinforced concrete columns? Structures, 40(April), 920–935. https://doi.org/10.1016/j.istruc.2022.04.076 Park, H.-G., Yu, E.-J., & Choi, K.-K. (2012). Shear-strength degradation model for RC columns subjected to cyclic loading. Engineering Structures, 34, 187–197. https://doi.org/https://doi.org/10.1016/j.engstruct.2011.08.041 Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106. https://doi.org/10.1007/BF00116251 Rajakarunakaran, S. A., Lourdu, A. R., Muthusamy, S., Panchal, H., Jawad Alrubaie, A., Musa Jaber, M., Ali, M. H., Tlili, I., Maseleno, A., Majdi, A., & Ali, S. H. M. (2022). Prediction of strength and analysis in self-compacting concrete using machine learning based regression techniques. Advances in Engineering Software, 173, 103267. https://doi.org/https://doi.org/10.1016/j.advengsoft.2022.103267 Shi, Q., Ma, L., Wang, Q., Wang, B., & Yang, K. (2021). Seismic performance of square concrete columns reinforced with grade 600 MPa longitudinal and transverse reinforcement steel under high axial load. Structures, 32, 1955–1970. https://doi.org/10.1016/j.istruc.2021.03.110 Van Rossum, G., & Drake Jr, F. L. (1995). Python reference manual. Centrum voor Wiskunde en Informatica Amsterdam, https://ir.cwi.nl/pub/5008. Wong, H. F., & Kuang, J. S. (2014). Predicting shear strength of RC interior beam–column joints by modified rotating-angle softened-truss model. Computers & Structures, 133, 12–17. https://doi.org/https://doi.org/10.1016/j.compstruc.2013.11.008 Woods, J., Kiousis, P., Ehsani, M., Saadatmanesh, H., & Fritz, W. (2007). Bending ductility of rectangular high strength concrete columns. Engineering Structures – ENG STRUCT, 29, 1783–1790. https://doi.org/10.1016/j.engstruct.2006.09.024 Zavvar Sabegh, M. H., Mirzazadeh, A., Salehian, S., & Wilhelm Weber, G. (2014). A Literature Review on the Fuzzy Control Chart; Classifications & Analysis. International Journal of Supply and Operations Management, 1(2), 167–189. https://doi.org/10.22034/2014.2.03 Zhang, J., Cai, R., Li, C., & Liu, X. (2020). Seismic behavior of high-strength concrete columns reinforced with high-strength steel bars. Engineering Structures, 218, 110861. https://doi.org/10.1016/j.engstruct.2020.110861 Zhang, X., Akber, M. Z., & Zheng, W. (2021). Prediction of seven-day compressive strength of field concrete. Construction and Building Materials, 305, 124604. https://doi.org/https://doi.org/10.1016/j.conbuildmat.2021.124604 Zhang, Y., Zheng, S., Rong, X., Dong, L., & Zheng, H. (2019). Seismic Performance of Reinforced Concrete Short Columns Subjected to Freeze–Thaw Cycles. Applied Sciences, 9(13). https://doi.org/10.3390/app9132708 Zhou, X., & Liu, J. (2010). Seismic behavior and shear strength of tubed RC short columns. Journal of Constructional Steel Research, 66(3), 385–397. https://doi.org/https://doi.org/10.1016/j.jcsr.2009.10.011
Assessing Column Stability: A Comparative Study of Machine Learning Regression Models for Shear Strength Prediction Yıl 2024, Cilt: 8 Sayı: 1, 279 – 289, 18.07.2024
Aybike Özyüksel Çiftçioğlu
https://doi.org/10.56554/jtom.1401261
Öz This research presents a comprehensive investigation into the accurate estimation of shear strength in rectangular reinforced concrete columns through advanced machine learning (ML) models. The study addresses the intricate challenge posed by shear strength complexity, which is crucial for evaluating column stability and ensuring structural integrity. Building upon a substantial dataset comprising 545 experimental observations sourced from diverse literature, this research establishes a robust foundation for predictive modeling. Four distinct ML regression models, Random Forest, Decision Tree, XGBoost, and LightGBM, are meticulously evaluated for their performance. The evaluation employs established metrics, including R2, RMSE, MAE, and MAPE to quantify their predictive capabilities. The outcomes highlight the models’ robustness in capturing nuanced variations in shear strength, with impressive R2 values ranging from 93.6% to 93.9%, showcasing their exceptional ability to elucidate intricate shear behaviors. Furthermore, comparative analysis indicates the slightly superior performance of the Random Forest over the Decision Tree, highlighting the efficacy of ensemble methods in this context. Extending the exploration to include XGBoost and LightGBM, the study showcases their potential as accurate shear strength predictors. The performance of the models is validated through scatter plots and error distribution plots, confirming accurate shear strength predictions across various scenarios. This research contributes significantly to the advancement of structural engineering methodologies by highlighting the potential of ML to improve the accuracy of shear strength estimation. The findings not only underscore the exceptional performance of ML models but also provide valuable insights into their comparative effectiveness, paving the way for enhanced structural assessments in columns.
Anahtar Kelimeler Machine Learning, Random Forest, Reinforced concrete, Regression, Shear Strength
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Annals of Operations Research, 328(1), 643–664. https://doi.org/10.1007/s10479-022-04686-4 Ho, J. C. M. (2012). Experimental Tests on High-Strength Concrete Columns Subjected to Combined Medium Axial Load and Flexure. Advances in Structural Engineering, 15(8), 1359–1374. https://doi.org/10.1260/1369- 4332.15.8.1359 Hugo, R., André, F., & António, A. (2016). Behavior of Rectangular Reinforced-Concrete Columns under Biaxial Cyclic Loading and Variable Axial Loads. Journal of Structural Engineering, 142(1), 4015085. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001345 Hwang, S.-K., & Yun, H.-D. (2004). Effects of transverse reinforcement on flexural behaviour of high-strength concrete columns. Engineering Structures, 26, 1–12. https://doi.org/10.1016/j.engstruct.2003.08.004 Karbasi Arani, K., Di Ludovico, M., Marefat, M., Prota, A., & Manfredi, G. (2014). Lateral Response Evaluation of Old Type Reinforced Concrete Columns with Smooth Bars. Aci Structural Journal, 111. https://doi.org/10.14359/51686734 Karbasi Arani, K., Marefat, M., Amrollahi-Biucky, A., & Khanmohammadi, M. (2013). Experimental seismic evaluation of old concrete columns reinforced by plain bars. The Structural Design of Tall and Special Buildings, 22. https://doi.org/10.1002/tal.686 Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Proceedings of the 31st International Conference on Neural Information Processing Systems, 3149–3157, https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. Khalilpourazari, S., & Doulabi, H. (2021). Using reinforcement learning to forecast the spread of COVID-19 in France. 1–8. https://doi.org/10.1109/ICAS49788.2021.9551174 Khalilpourazari, S., & Hashemi Doulabi, H. (2022). A flexible robust model for blood supply chain network design problem. Annals of Operations Research. https://doi.org/10.1007/s10479-022-04673-9 Khalilpourazari, S., Mirzazadeh, A., Weber, G.-W., & Pasandideh, S. H. R. (2020). A robust fuzzy approach for constrained multi-product economic production quantity with imperfect items and rework process. Optimization, 69(1), 63–90. https://doi.org/10.1080/02331934.2019.1630625 Khalilpourazari, S., & Pasandideh, S. (2016). Bi-objective optimization of multi-product EPQ model with backorders, rework process and random defective rate. https://10.1109/INDUSENG.2016.7519346. Lam, S., Wu, B., Wong, Y., Wang, Z. Y., Liu, Z., & Li, C. (2003). Drift Capacity of Rectangular Reinforced Concrete Columns with Low Lateral Confinement and High-Axial Load. Journal of Structural Engineering-Asce – J STRUCT ENG-ASCE, 129. https://doi.org/10.1061/(ASCE)0733-9445(2003)129:6(733) Li, Y.-A., Huang, Y.-T., & Hwang, S.-J. (2014). Seismic Response of Reinforced Concrete Short Columns Failed in Shear. ACI Structural Journal, 111. https://doi.org/10.14359/51686780 Li, Y., Cao, S., & Jing, D.-H. (2018). Concrete Columns Reinforced with High-Strength Steel Subjected to Reversed Cycle Loading. ACI Structural Journal, 115. https://doi.org/10.14359/51701296 Liu, T., Wang, Z., Zeng, J., & Wang, J. (2021). Machine-learning-based models to predict shear transfer strength of concrete joints. Engineering Structures, 249, 113253. https://doi.org/https://doi.org/10.1016/j.engstruct.2021.113253 Ma, L., Zhou, C., Lee, D., & Zhang, J. (2022). Prediction of axial compressive capacity of CFRP-confined concrete-filled steel tubular short columns based on XGBoost algorithm. Engineering Structures, 260, 114239. https://doi.org/https://doi.org/10.1016/j.engstruct.2022.114239 Marefat, M. S., Khanmohammadi, M., Bahrani, M. K., & Goli, A. (2006). Experimental Assessment of Reinforced Concrete Columns with Deficient Seismic Details under Cyclic Load. Advances in Structural Engineering, 9(3), 337–347. https://doi.org/10.1260/136943306777641959 Melo, J., Varum, H., & Rossetto, T. (2015). Experimental cyclic behaviour of RC columns with plain bars and proposal for Eurocode 8 formula improvement. Engineering Structures, 88, 22–36. https://doi.org/10.1016/j.engstruct.2015.01.033 Mohammadi, M., & Khalilpourazari, S. (2017). Minimizing Makespan in a single machine scheduling problem with deteriorating jobs and learning effects. https://doi.org/10.1145/3056662.3056715 Moslemi, S., Mirzazadeh, A., Weber, G.-W., & Sobhanallahi, M. A. (2021). Integration of neural network and AP-NDEA model for performance evaluation of sustainable pharmaceutical supply chain. OPSEARCH. https://doi.org/10.1007/s12597-021-00561-1 Naser, M. Z. (2021). Mechanistically Informed Machine Learning and Artificial Intelligence in Fire Engineering and Sciences. Fire Technology, 1–44. https://doi.org/10.1007/s10694-020-01069-8 Naser, M. Z., & Ciftcioglu, A. O. (2022). Causal Discovery and Causal Learning for Fire Resistance Evaluation: Incorporating Domain Knowledge. https://doi.org/10.48550/arxiv.2204.05311 Naser, M. Z., & Çiftçioğlu, A. Ö. (2023). Revisiting Forgotten Fire Tests: Causal Inference and Counterfactuals for Learning Idealized Fire-Induced Response of RC Columns. Fire Technology, 59(4), 1761–1788. https://doi.org/10.1007/s10694-023-01405-8 9 Nguyen-Sy, T., Wakim, J., To, Q. D., Vu, M. N., Nguyen, T. D., & Nguyen, T. T. (2020). Predicting the compressive strength of concrete from its compositions and age using the extreme gradient boosting method. Construction and Building Materials, 260, 119757. https://doi.org/10.1016/j.conbuildmat.2020.119757 Nguyen, H., Vu, T., Vo, T. P., & Thai, H. T. (2021). Efficient machine learning models for prediction of concrete strengths. Construction and Building Materials, 266, 120950. https://doi.org/10.1016/j.conbuildmat.2020.120950 Özyüksel Çiftçioğlu, A. (2023). RAGN-L: A Stacked Ensemble Learning Technique for Classification of Fire- Resistant Columns. Expert Systems with Applications, 240(November 2023), 122491. https://doi.org/10.1016/j.eswa.2023.122491 Özyüksel Çiftçioğlu, A., & Naser, M. Z. (2022). Hiding in plain sight: What can interpretable unsupervised machine learning and clustering analysis tell us about the fire behavior of reinforced concrete columns? Structures, 40(April), 920–935. https://doi.org/10.1016/j.istruc.2022.04.076 Park, H.-G., Yu, E.-J., & Choi, K.-K. (2012). Shear-strength degradation model for RC columns subjected to cyclic loading. Engineering Structures, 34, 187–197. https://doi.org/https://doi.org/10.1016/j.engstruct.2011.08.041 Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106. https://doi.org/10.1007/BF00116251 Rajakarunakaran, S. A., Lourdu, A. R., Muthusamy, S., Panchal, H., Jawad Alrubaie, A., Musa Jaber, M., Ali, M. H., Tlili, I., Maseleno, A., Majdi, A., & Ali, S. H. M. (2022). Prediction of strength and analysis in self-compacting concrete using machine learning based regression techniques. Advances in Engineering Software, 173, 103267. https://doi.org/https://doi.org/10.1016/j.advengsoft.2022.103267 Shi, Q., Ma, L., Wang, Q., Wang, B., & Yang, K. (2021). Seismic performance of square concrete columns reinforced with grade 600 MPa longitudinal and transverse reinforcement steel under high axial load. Structures, 32, 1955–1970. https://doi.org/10.1016/j.istruc.2021.03.110 Van Rossum, G., & Drake Jr, F. L. (1995). Python reference manual. Centrum voor Wiskunde en Informatica Amsterdam, https://ir.cwi.nl/pub/5008. Wong, H. F., & Kuang, J. S. (2014). Predicting shear strength of RC interior beam–column joints by modified rotating-angle softened-truss model. Computers & Structures, 133, 12–17. https://doi.org/https://doi.org/10.1016/j.compstruc.2013.11.008 Woods, J., Kiousis, P., Ehsani, M., Saadatmanesh, H., & Fritz, W. (2007). Bending ductility of rectangular high strength concrete columns. Engineering Structures – ENG STRUCT, 29, 1783–1790. https://doi.org/10.1016/j.engstruct.2006.09.024 Zavvar Sabegh, M. H., Mirzazadeh, A., Salehian, S., & Wilhelm Weber, G. (2014). A Literature Review on the Fuzzy Control Chart; Classifications & Analysis. International Journal of Supply and Operations Management, 1(2), 167–189. https://doi.org/10.22034/2014.2.03 Zhang, J., Cai, R., Li, C., & Liu, X. (2020). Seismic behavior of high-strength concrete columns reinforced with high-strength steel bars. Engineering Structures, 218, 110861. https://doi.org/10.1016/j.engstruct.2020.110861 Zhang, X., Akber, M. Z., & Zheng, W. (2021). Prediction of seven-day compressive strength of field concrete. Construction and Building Materials, 305, 124604. https://doi.org/https://doi.org/10.1016/j.conbuildmat.2021.124604 Zhang, Y., Zheng, S., Rong, X., Dong, L., & Zheng, H. (2019). Seismic Performance of Reinforced Concrete Short Columns Subjected to Freeze–Thaw Cycles. Applied Sciences, 9(13). https://doi.org/10.3390/app9132708 Zhou, X., & Liu, J. (2010). Seismic behavior and shear strength of tubed RC short columns. Journal of Constructional Steel Research, 66(3), 385–397. https://doi.org/https://doi.org/10.1016/j.jcsr.2009.10.011 Toplam 46 adet kaynakça vardır.
Ayrıntılar Birincil Dil İngilizce Konular Üretim ve Endüstri Mühendisliği (Diğer) Bölüm Araştırma Makalesi Yazarlar Aybike Özyüksel Çiftçioğlu MANİSA CELÂL BAYAR ÜNİVERSİTESİ 0000-0003-4424-7622 Türkiye
Erken Görünüm Tarihi 18 Temmuz 2024 Yayımlanma Tarihi 18 Temmuz 2024 Gönderilme Tarihi 6 Aralık 2023 Kabul Tarihi 7 Nisan 2024 Yayımlandığı Sayı Yıl 2024 Cilt: 8 Sayı: 1
Kaynak Göster APA Özyüksel Çiftçioğlu, A. (2024). Assessing Column Stability: A Comparative Study of Machine Learning Regression Models for Shear Strength Prediction. Journal of Turkish Operations Management, 8(1), 279-289. https://doi.org/10.56554/jtom.1401261 AMA Özyüksel Çiftçioğlu A. Assessing Column Stability: A Comparative Study of Machine Learning Regression Models for Shear Strength Prediction. JTOM. Temmuz 2024;8(1):279-289. doi:10.56554/jtom.1401261 Chicago Özyüksel Çiftçioğlu, Aybike. “Assessing Column Stability: A Comparative Study of Machine Learning Regression Models for Shear Strength Prediction”. Journal of Turkish Operations Management 8, sy. 1 (Temmuz 2024): 279-89. https://doi.org/10.56554/jtom.1401261. EndNote Özyüksel Çiftçioğlu A (01 Temmuz 2024) Assessing Column Stability: A Comparative Study of Machine Learning Regression Models for Shear Strength Prediction. Journal of Turkish Operations Management 8 1 279–289. IEEE A. Özyüksel Çiftçioğlu, “Assessing Column Stability: A Comparative Study of Machine Learning Regression Models for Shear Strength Prediction”, JTOM , c. 8, sy. 1, ss. 279–289, 2024, doi: 10.56554/jtom.1401261. ISNAD Özyüksel Çiftçioğlu, Aybike. “Assessing Column Stability: A Comparative Study of Machine Learning Regression Models for Shear Strength Prediction”. Journal of Turkish Operations Management 8/1 (Temmuz 2024), 279-289. https://doi.org/10.56554/jtom.1401261. JAMA Özyüksel Çiftçioğlu A. Assessing Column Stability: A Comparative Study of Machine Learning Regression Models for Shear Strength Prediction. JTOM . 2024;8:279–289. MLA Özyüksel Çiftçioğlu, Aybike. “Assessing Column Stability: A Comparative Study of Machine Learning Regression Models for Shear Strength Prediction”. Journal of Turkish Operations Management, c. 8, sy. 1, 2024, ss. 279-8, doi:10.56554/jtom.1401261. Vancouver Özyüksel Çiftçioğlu A. Assessing Column Stability: A Comparative Study of Machine Learning Regression Models for Shear Strength Prediction. JTOM. 2024;8(1):279-8.
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