Comparison of Axial Capacity and Nonlinear Buckling Behavior of Welded Built-Up and Hot-Rolled H-Beam Steel Columns

Authors

  • Febri Novianto Universitas 17 Agustus 1945 Surabaya, Indonesia
  • Ony Frengky Rumihin Universitas 17 Agustus 1945 Surabaya, Indonesia
  • Buntara Sthenly Gan Nihon University, Japan

DOI:

https://doi.org/10.67412/jceegs.v1i2.76

Keywords:

Finite Element Analysis, Global Flexural Buckling, Hot-Rolled H-Sections, Initial Geometric Imperfections, Steel Columns, Welded Built-Up H-Sections

Abstract

The buckling resistance of steel columns is governed by cross-sectional properties, initial geometric imperfections, and residual stresses associated with fabrication. Differences between hot-rolled and welded built-up H-sections may therefore affect their nonlinear stability response. This study compares the buckling behavior of steel columns with these two section types using finite element analysis in SAP2000. The numerical models account for geometric nonlinearity, initial geometric imperfections, and fabrication-related residual stress effects represented through a material-zoning approach. Structural behavior is assessed in terms of buckling factors, ultimate axial capacity, load–displacement relationships, and deformation patterns. The results show that all models exhibit global flexural buckling, consistent with the governing mode identified by eigenvalue analysis. The hot-rolled section achieves a slightly higher ultimate axial capacity than the welded built-up section, whereas the orientation of initial geometric imperfections substantially affects lateral deformation and the nonlinear response. Although the capacity difference between the section types is relatively small, the findings indicate that manufacturing-related characteristics and imperfection orientation influence column stability. This comparison highlights the importance of incorporating initial imperfections and fabrication-related effects into nonlinear numerical assessments of steel compression members. The results provide a comparative basis for evaluating the stability behavior of hot-rolled and welded built-up H-section columns.

References

Abbas, Y. M., Babiker, A., Elwakeel, A., & Khan, M. I. (2025). Cost–Performance Multi-Objective Optimization of Quaternary-Blended Cement Concrete. Buildings, 15(22), 4074. https://doi.org/10.3390/buildings15224074

Abellán-García, J., Daza, N., Molinares, M., Abbas, Y. M., & Khan, M. I. (2023). Multi-Criteria Optimization of Cost-Effective and Environmentally Friendly Reactive Powder Concrete Incorporating Waste Glass and Micro Calcium Carbonate. Materials, 16(19), 6434. https://doi.org/10.3390/ma16196434

Ahmadi, M., Abdollahzadeh, E., Kashfi, M., Khataei, B., & Razavi, M. (2025). Life Cycle Assessment and Performance Evaluation of Self-Compacting Concrete Incorporating Waste Marble Powder and Aggregates. Materials, 18(13), 2982. https://doi.org/10.3390/ma18132982

Almuflih, A. S., Abas, M., Khan, I., & Noor, S. (2024). Parametric Optimization of FDM Process for PA12-CF Parts Using Integrated Response Surface Methodology, Grey Relational Analysis, and Grey Wolf Optimization. Polymers, 16(11), 1508. https://doi.org/10.3390/polym16111508

Azam, R., Riaz, M. R., Shakeel, M., Farooq, M. U., & Arshad, M. A. (2024). Economical design strategies for reinforced concrete one-way slabs: a parametric approach considering material strength and rebar size. Discover Civil Engineering, 1(1), 102. https://doi.org/10.1007/s44290-024-00106-7

Bhattacharyya, J. J., Inman, S. B., Wischhusen, M. A., Qi, J., Poon, J., Scully, J. R., & Agnew, S. R. (2023). Lightweight, low cost compositionally complex multiphase alloys with optimized strength, ductility and corrosion resistance: Discovery, design and mechanistic understandings. Materials & Design, 228, 111831. https://doi.org/10.1016/j.matdes.2023.111831

Chen, D.-C., Zheng, Q.-D., & Chen, C.-H. (2024). Optimization of Tensile Strength and Cost-Effectiveness of Polyethylene Terephthalate Glycol in Fused Deposition Modeling Using the Taguchi Method and Analysis of Variance. Polymers, 16(22), 3133. https://doi.org/10.3390/polym16223133

Diab, M., Refaey, M., & Eltobgy, H. (2025). Finite Element Modeling of Newly Developed Cold-Formed Hollow Flanged Z-beams. Journal of Engineering Research, 9. https://doi.org/10.70259/engJER.2025.921932

Elhishi, S., Elashry, A. M., & El-Metwally, S. (2023). Unboxing machine learning models for concrete strength prediction using XAI. Scientific Reports, 13(1), 19892. https://doi.org/10.1038/s41598-023-47169-7

Ganendra, B., Muttaqie, T., Prabowo, A. R., Tuswan, T., Samad, R., Djordjevic, B., Budiana, E. P., Adie, P. W., Sholikhah, M., & Sholehuddin, M. I. (2026). Assessing the resilience of steel cylindrical shells under bending loads: a parametric study of imperfections and corrosion. Journal of the Mechanical Behavior of Materials, 35(1). https://doi.org/10.1515/jmbm-2025-0110

Gantes, C. J., Vernardos, S. M., Koulatsou, K. G., & Gül, S. (2024). Nonlinear Finite Element Analysis of Tubular Steel Wind Turbine Towers near Man Door and Ventilation Openings to Optimize Design against Buckling. Vibration, 7(1), 212–228. https://doi.org/10.3390/vibration7010012

Gardner, L., Li, J., Meng, X., Huang, C., & Kyvelou, P. (2023). Buckling tests on I‐section steel columns strengthened by additive manufacturing. Ce/Papers, 6(3–4), 720–725. https://doi.org/10.1002/cepa.2462

Han, X., Wang, S., Zhou, Z., Chen, G., Wei, H., Chu, Y., & Liu, X. (2026). Model Prediction and Multi-Objective Optimization of Unfired Bricks Incorporated with Drinking Water Treatment Sludge Using Machine Learning. Buildings, 16(12), 2336. https://doi.org/10.3390/buildings16122336

Hariri, B., & Christopoulos, C. (2024). Numerical hybrid simulation assessment of E‐BRBF and E‐FBF systems for mitigating P‐delta effects and enhancing post‐earthquake performance. Earthquake Engineering & Structural Dynamics, 53(12), 3754–3774. https://doi.org/10.1002/eqe.4195

Horn, A., Gomez, A., Nyvang Legarth, B., & Berggreen, C. (2026). Temperature Dependent Performance of Thermoplastic Polyphenylene Sulfide (PPS) and Polyetherimide (PEI) Honeycomb Sandwiches Using Four-Point Bend Testing. Applied Composite Materials, 33(3), 89. https://doi.org/10.1007/s10443-026-10469-y

Hu, Q., Su, C., Zhang, J., & Zhao, X. (2026). Optimization of UHPC Mix Design Using Polyacrylonitrile Fibers and Coarse Aggregates for Cost Reduction. Buildings, 16(6), 1200. https://doi.org/10.3390/buildings16061200

Khamwachirapithak, P., Sae-Tang, K., Mhuantong, W., Tanapongpipat, S., Zhao, X.-Q., Liu, C.-G., Wei, D.-Q., Champreda, V., & Runguphan, W. (2023). Optimizing Ethanol Production in Saccharomyces cerevisiae at Ambient and Elevated Temperatures through Machine Learning-Guided Combinatorial Promoter Modifications. ACS Synthetic Biology, 12(10), 2897–2908. https://doi.org/10.1021/acssynbio.3c00199

Khan, M. S., Peng, T., Khan, M. A., Khan, A., Ahmad, M., Aziz, K., Sabri Sabri, M. M., & Abd EL-Gawaad, N. S. (2025). Explainable AutoML models for predicting the strength of high-performance concrete using Optuna, SHAP and ensemble learning. Frontiers in Materials, 12. https://doi.org/10.3389/fmats.2025.1542655

Kim, M., Kim, O., Song, Y., Ko, C., Kim, J., Habibnejad-korayem, M., & Kim, J. (2024). Cost-Effective Laser Powder Bed Fusion of Ti-6Al-4V Grade 5: The Effect of Expanding Powder Size Distribution on Mechanical Performance. Materials, 18(1), 6. https://doi.org/10.3390/ma18010006

Lindh, P., & Lemenkova, P. (2023). Effects of Water—Binder Ratio on Strength and Seismic Behavior of Stabilized Soil from Kongshavn, Port of Oslo. Sustainability, 15(15), 12016. https://doi.org/10.3390/su151512016

Mandal, S., Shiuly, A., Sau, D., Mondal, A. K., & Sarkar, K. (2024). Study on the use of different machine learning techniques for prediction of concrete properties from their mixture proportions with their deterministic and robust optimisation. AI in Civil Engineering, 3(1), 7. https://doi.org/10.1007/s43503-024-00024-8

Myroshnichenko, I. (2025). Comprehensive analysis of stability and strength of thin-walled reinforced concrete elements based on energy approaches. Academic Journal Industrial Machine Building Civil Engineering, 2(65), 109–117. https://doi.org/10.26906/znp.2025.65.4197

Ngodji, C. D., Gauchey, M., Wain, G., Morelli, F., Natali, A., Lippi, F., & D’Antimo, M. (2025). Enhancing High-Bay Warehouse Sustainability: High-Strength and Low-Carbon Steel for Weight, Cost, and CO2 Optimization. Sustainability, 17(19), 8775. https://doi.org/10.3390/su17198775

Oviedo, A. I., Londoño, J. M., Vargas, J. F., Zuluaga, C., & Gómez, A. (2024). Modeling and Optimization of Concrete Mixtures Using Machine Learning Estimators and Genetic Algorithms. Modelling, 5(3), 642–658. https://doi.org/10.3390/modelling5030034

Salah, H. A., Mutalib, A. A., Algaifi, H. A., Yahya, I. Bin, Yusof, M. A. I., Sakib, N., & Elsayed, M. (2023). Assessment of the Mechanical Properties of High Strength Mortar Incorporating Silica Fume and Graphene Nanoplatelets: Experimental and Mathematical Modeling. Sustainability, 15(10), 8054. https://doi.org/10.3390/su15108054

Salvador, C. A. F., Van Landeghem, H. P., & Antunes, R. A. (2023). Selection of Ti Alloys for Bio‐Implants: An Application of the Ashby Approach with Conflicting Objectives. Advanced Engineering Materials, 25(22). https://doi.org/10.1002/adem.202301169

Sun, C., Wang, K., Liu, Q., Wang, P., & Pan, F. (2023). Machine-Learning-Based Comprehensive Properties Prediction and Mixture Design Optimization of Ultra-High-Performance Concrete. Sustainability, 15(21), 15338. https://doi.org/10.3390/su152115338

Sun, H., Li, W., Zhang, Q., Wang, Z., & Guo, Z. (2026). Energy-based buckling stability analysis of free-standing stepped cross-section pipe piles considering self-weight. https://doi.org/10.2139/ssrn.7111985

Sun, J., Wang, L., & Gong, D. (2023). A Joint Optimization Algorithm Based on the Optimal Shape Parameter–Gaussian Radial Basis Function Surrogate Model and Its Application. Mathematics, 11(14), 3169. https://doi.org/10.3390/math11143169

Sun, W., Zhao, J., Li, Q., & Xiao, Z. (2024). Experimentally optimized anchored-FRP method: A high-strength, cost-effective and ease-to-install technique for concrete rehabilitation. Case Studies in Construction Materials, 21, e03927. https://doi.org/10.1016/j.cscm.2024.e03927

Swierczyna, S. (2026). Experimental and Numerical Investigation of Cold-Formed Steel Storage Platforms with Perforated Channel Beams. Materials, 19(15), 3316. https://doi.org/10.3390/ma19153316

Szewczyk, S., Semko, V., & Studziński, R. (2025). Numerical Design Calculation According to EN 1993-1-14 of Innovative Thin-Walled Columns with Sectional Transverse Strengthening. Materials, 18(16), 3878. https://doi.org/10.3390/ma18163878

Tanikella, N. G., McDonald, A. G., & Maughan, M. R. (2026). Optimizing Wood–Hemp–Sodium Silicate Composites for Strength, Extrudability, and Cost in Additive Manufacturing Applications. Materials, 19(2), 357. https://doi.org/10.3390/ma19020357

Thennarasan Latha, A., Murugesan, B., & Thomas, B. S. (2023). Compressed Stabilized Earth Block Incorporating Municipal Solid Waste Incinerator Bottom Ash as a Partial Replacement for Fine Aggregates. Buildings, 13(5), 1114. https://doi.org/10.3390/buildings13051114

Tipu, R. K., Rathi, P., Pandya, K. S., & Panchal, V. R. (2025). Optimizing sustainable blended concrete mixes using deep learning and multi-objective optimization. Scientific Reports, 15(1), 16356. https://doi.org/10.1038/s41598-025-00943-1

Tunçel, O., Kahya, Ç., & Tüfekci, K. (2024). Optimization of Flexural Performance of PETG Samples Produced by Fused Filament Fabrication with Response Surface Method. Polymers, 16(14), 2020. https://doi.org/10.3390/polym16142020

Wu, W. (2024). Mechanical Performance/Cost Ratio Analysis of Carbon/Glass Interlayer and Intralayer Hybrid Composites. Coatings, 14(7), 810. https://doi.org/10.3390/coatings14070810

Zhang, F., Wen, B., Niu, D., Li, A., & Guo, B. (2024). Optimized Design of Low-Carbon Mix Ratio for Non-Dominated Sorting Genetic Algorithm II Concrete Based on Genetic Algorithm-Improved Back Propagation. Materials, 17(16), 4077. https://doi.org/10.3390/ma17164077

Zhao, X., Yang, K., He, X., Wei, Z., Zhang, J., & Yu, X. (2024). Mix proportion and microscopic characterization of coal-based solid waste backfill material based on response surface methodology and multi-objective decision-making. Scientific Reports, 14(1), 5672. https://doi.org/10.1038/s41598-024-56028-y

Zhu, X., & Xu, W. (2026). Sustainable Mix Design of Recycled Aggregate Concrete: Machine Learning-Assisted Multi-Objective Optimization of Strength, Life-Cycle Cost, and Net Carbon Emissions. Buildings, 16(16), 3198. https://doi.org/10.3390/buildings16163198

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Published

2026-08-15

How to Cite

Novianto, F., Rumihin, O. F., & Gan, B. S. (2026). Comparison of Axial Capacity and Nonlinear Buckling Behavior of Welded Built-Up and Hot-Rolled H-Beam Steel Columns. Journal of Civil and Enviromental Engineering in the Global South, 1(2), 121–139. https://doi.org/10.67412/jceegs.v1i2.76

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