Development of Hybrid Machine Learning Models for Predicting the Critical Buckling Load of I-Shaped Cellular Beams
Source abstract
Abstract
The principal purpose of this work is to develop three hybrid machine learning (ML) algorithms, namely ANFIS-RCSA, ANFIS-CA, and ANFIS-SFLA which are a combination of adaptive neuro-fuzzy inference system (ANFIS) with metaheuristic optimization techniques such as real-coded simulated annealing (RCSA), cultural algorithm (CA) and shuffled frog leaping algorithm (SFLA), respectively, to predict the critical buckling load of I-shaped cellular steel beams with circular openings. For this purpose, the existing database of buckling tests on I-shaped steel beams were extracted from the available literature and used to generate the datasets for modeling. Eight inputs, considered as independent variables, including the beam length, beam end-opening distance, opening diameter, inter-opening distance, section height, web thickness, flange width, and flange thickness, as well as one output of the critical buckling load of cellular steel beams considered as a dependent variable, were used in the datasets. Three quality assessment criteria, namely correlation coefficient (R), root mean squared error (RMSE) and mean absolute error (MAE) were employed for assessment of three developed hybrid ML models. The obtained results indicate that all three hybrid ML models have a strong ability to predict the buckling load of steel beams with circular openings, but ANFIS-SFLA (R = 0.960, RMSE = 0.040 and MAE = 0.017) exhibits the best effectiveness as compared with other hybrid models. In addition, sensitivity analysis was investigated and compared with linear statistical correlation between inputs and output to validate the importance of input variables in the models. The sensitivity results show that the most influenced variable affecting beam buckling capacity is the beam length, following by the flange width, the flange thickness, and the web thickness, respectively. This study shows that the hybrid ML techniques could help in establishing a robust numerical tool for beam buckling analysis. The proposed methodology is also promising to predict other types of failure, as well as other types of perforated beams.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Structural Health Monitoring Techniques · Geotechnical Engineering and Underground Structures · Structural Load-Bearing Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
University of Transport Technology, Hanoi 100000, Vietnam
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Faculty of Engineering, Vietnam National University of Agriculture, Gia Lam, Hanoi 100000, Vietnam
Thuyloi University, Hanoi 100000, Vietnam
References (90)
Show source-provided references
1. Design procedure for the web-post buckling of steel cellular beams10.1016/j.jcsr.2018.06.020 ↗
2. Serviceability performance of composite cellular beams with partial shear connection10.1016/j.jcsr.2018.04.032 ↗
3. Structural behaviour of arched steel beams with cellular openings10.1016/j.jcsr.2018.06.029 ↗
4. Elastic lateral stability of I-shaped cellular steel beams10.1016/j.jcsr.2010.08.009 ↗
5. Nonlinear analysis of cellular steel beams under combined buckling modes10.1016/j.tws.2011.12.009 ↗
6. Performance of unprotected and protected cellular beams in fire conditions10.1016/j.conbuildmat.2015.12.150 ↗
7. Ly, H.-B., Desceliers, C., Le, L.M., Le, T.-T., Pham, B.T., Nguyen-Ngoc, L., Doan, V.T., and Le, M. (2019). Quantification of Uncertainties on the Critical Buckling Load of Columns under Axial Compression with Uncertain Random Materials. Materials, 12.10.3390/ma12111828 ↗
8. Le, L., Ly, H.-B., Pham, B., Le, V., Phạm, T., Nguyen, D.-H., Tran, X.-T., and Le, T.-T. (2019). Hybrid Artificial Intelligence Approaches for Predicting Buckling Damage of Steel Columns Under Axial Compression. Materials, 12.10.3390/ma12101670 ↗
9. Ly, H.-B., Le, L.M., Duong, H.T., Nguyen, T.C., Pham, T.A., Le, T.-T., Le, V.M., Nguyen-Ngoc, L., and Pham, B.T. (2019). Hybrid Artificial Intelligence Approaches for Predicting Critical Buckling Load of Structural Members under Compression Considering the Influence of Initial Geometric Imperfections. Appl. Sci., 9.10.3390/app9112258 ↗
10. Lateral–torsional buckling resistance of cellular beams10.1016/j.jcsr.2014.11.003 ↗
11. Web buckling study of the behaviour and strength of perforated steel beams with different novel web opening shapes10.1016/j.jcsr.2011.04.004 ↗
12. Experimental study on long spanning composite cellular beam under flexure and shear10.1016/j.jcsr.2015.08.047 ↗
13. A meshfree unit-cell method for effective planar analysis of cellular beams10.1016/j.compstruc.2016.09.002 ↗
14. Cellular beam design for resistance to inelastic lateral–torsional buckling10.1016/j.tws.2015.08.026 ↗
15. Cemented paste backfill for mineral tailings management: Review and future perspectives10.1016/j.mineng.2019.106025 ↗
16. Self-compacting concrete strength prediction using surrogate models10.1007/s00521-017-3007-7 ↗
17. Cavaleri, L., Asteris, P.G., Psyllaki, P.P., Douvika, M.G., Skentou, A.D., and Vaxevanidis, N.M. (2019). Prediction of Surface Treatment Effects on the Tribological Performance of Tool Steels Using Artificial Neural Networks. Appl. Sci., 9.10.3390/app9142788 ↗
18. Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures10.1007/s00521-018-03965-1 ↗
19. Chen, H., Asteris, P.G., Jahed Armaghani, D., Gordan, B., and Pham, B.T. (2019). Assessing Dynamic Conditions of the Retaining Wall: Developing Two Hybrid Intelligent Models. Appl. Sci., 9.10.3390/app9061042 ↗
20. Predicting the strength properties of slurry infiltrated fibrous concrete using artificial neural network10.1007/s11709-017-0445-3 ↗
21. Neural networks in mechanics of structures and materials—New results and prospects of applications10.1016/S0045-7949(01)00083-9 ↗
22. Computational homogenization of nonlinear elastic materials using neural networks10.1002/nme.4953 ↗
23. Prediction of shear strength of soft soil using machine learning methods10.1016/j.catena.2018.04.004 ↗
24. Kopal, I., Labaj, I., Harnicarova, M., Valicek, J., and Hruby, D. (2018). Prediction of the Tensile Response of Carbon Black Filled Rubber Blends by Artificial Neural Network. Polymers, 10.10.3390/polym10060644 ↗
25. Artificial neural network as an incremental non-linear constitutive model for a finite element code10.1016/S0045-7825(03)00350-5 ↗
26. A new neural network-based model for hysteretic behavior of materials10.1002/nme.2082 ↗
27. A strength prediction model using artificial intelligence for recycling waste tailings as cemented paste backfill10.1016/j.jclepro.2018.02.154 ↗
28. Deriving temperature-dependent material models for structural steel through artificial intelligence10.1016/j.conbuildmat.2018.09.186 ↗
29. Buckling optimization of variable stiffness cylindrical shells through artificial intelligence techniques10.1016/j.compstruct.2019.111513 ↗
30. Optimal design of hierarchical grid-stiffened cylindrical shell structures based on linear buckling and nonlinear collapse analyses10.1016/j.tws.2017.06.019 ↗
31. Abambres, M., Rajana, K., Tsavdaridis, K.D., and Ribeiro, T.P. (2019). Neural Network-Based Formula for the Buckling Load Prediction of I-Section Cellular Steel Beams. Computers, 8.10.31224/osf.io/wg7hd ↗
32. Buckling load prediction of laminated composite stiffened panels subjected to in-plane shear using artificial neural networks10.1016/j.tws.2016.01.025 ↗
33. Artificial neural network model of the strength of thin rectangular plates with weld induced initial imperfections10.1016/j.ress.2011.02.010 ↗
34. Smith, M. (2009). ABAQUS/Standard User’s Manual, Dassault Systemes Simulia Corp. Version 6.9.
35. Moment gradient factor of cellular steel beams under inelastic flexure10.1016/j.jcsr.2014.02.007 ↗
36. Abramovich, H. (2017). Stability and Vibrations of Thin-Walled Composite Structures, Woodhead Publishing. [1st ed.].
37. Ly, H.-B., Pham, B.T., Dao, D.V., Le, V.M., Le, L.M., and Le, T.-T. (2019). Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete. Appl. Sci., 9.10.3390/app9183841 ↗
38. Nguyen, V.V., Pham, B.T., Vu, B.T., Prakash, I., Jha, S., Shahabi, H., Shirzadi, A., Ba, D.N., Kumar, R., and Chatterjee, J.M. (2019). Hybrid Machine Learning Approaches for Landslide Susceptibility Modeling. Forests, 10.10.3390/f10020157 ↗
39. Termeh, S.V.R., Khosravi, K., Sartaj, M., Keesstra, S.D., Tsai, F.T.-C., Dijksma, R., and Pham, B.T. (2019). Optimization of an adaptive neuro-fuzzy inference system for groundwater potential mapping. Hydrogeol. J.10.1007/s10040-019-02017-9 ↗
40. Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility10.1016/j.catena.2018.12.033 ↗
41. Tien Bui, D., Ngoc Do, A., Bui, H.-B., and Hoang, N.-D. (2018). Spatial Prediction of Rainfall Induced Shallow Landslides Using Adaptive-Network-Based Fuzzy Inference System and Particle Swarm Optimization: A Case Study at the Uttarakhand Area, India. Advances and Applications in Geospatial Technology and Earth Resources, Springer International Publishing.
42. Dao, D., Trinh, S., Ly, H.-B., and Pham, B. (2019). Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches. Appl. Sci., 9.10.3390/app9061113 ↗
43. Nguyen, H.-L., Le, T.-H., Pham, C.-T., Le, T.-T., Ho, L., Le, V., Pham, B., and Ly, H.-B. (2019). Development of Hybrid Artificial Intelligence Approaches and a Support Vector Machine Algorithm for Predicting the Marshall Parameters of Stone Matrix Asphalt. Appl. Sci., 2019.10.3390/app9153172 ↗
44. Oonsivilai, A., and El-Hawary, M.E. (1999, January 9–12). Power system dynamic load modeling using adaptive-network-based fuzzy inference system. Proceedings of the Engineering Solutions for the Next Millennium. 1999 IEEE Canadian Conference on Electrical and Computer Engineering (Cat. No.99TH8411), Edmonton, AB, Canada.
45. Neuro-fuzzy controller of low head hydropower plants using adaptive-network based fuzzy inference system10.1109/60.638941 ↗
46. Nguyen, H.-L., Pham, B.T., Son, L.H., Thang, N.T., Ly, H.-B., Le, T.-T., Ho, L.S., Le, T.-H., and Tien Bui, D. (2019). Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction. Appl. Sci., 9.10.3390/app9214715 ↗
47. Neuro-fuzzy technique to predict air-overpressure induced by blasting10.1007/s12517-015-1984-3 ↗
48. Ly, H.-B., Le, L.M., Phi, L.V., Phan, V.-H., Tran, V.Q., Pham, B.T., Le, T.-T., and Derrible, S. (2019). Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data. Sensors, 19.10.3390/s19224941 ↗
49. Flocculation-dewatering prediction of fine mineral tailings using a hybrid machine learning approach10.1016/j.chemosphere.2019.125450 ↗
50. Equation of state calculations by fast computing machines10.1063/1.1699114 ↗
51. Optimization by Simulated Annealing10.1126/science.220.4598.671 ↗
52. Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm10.1007/BF00940812 ↗
53. Burke, E.K., and Kendall, G. (2005). Simulated Annealing. Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, Springer US.
54. Optimization with a simulated annealing algorithm of a hybrid system for renewable energy including battery and hydrogen storage10.1016/j.energy.2018.08.112 ↗
55. Pham, D., and Karaboga, D. (2000). Genetic algorithms, tabu search, simulated annealing and neural networks. Intelligent Optimisation Techniques, Springer.10.1007/978-1-4471-0721-7_3 ↗
56. Dréo, J., Siarry, P., Pétrowski, A., and Taillard, E. (2006). Metaheuristics for Hard Optimization, Springer.
57. Sampling design for air quality measurement surveys: An optimization approach10.1016/j.atmosenv.2011.03.063 ↗
58. A simulated annealing heuristic for the hybrid vehicle routing problem10.1016/j.asoc.2016.12.027 ↗
59. Chung, C.-J. (1997). Knowledge-based Approaches to Self-adaptation in Cultural Algorithms. [Ph.D. Thesis, Wayne State University].
60. Reynolds, R. (1994). An Introduction to Cultural Algorithms, World Scientific Press.
61. Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability10.1016/j.agrformet.2018.12.015 ↗
62. A modified cultural algorithm with a balanced performance for the differential evolution frameworks10.1016/j.knosys.2016.08.005 ↗
63. An Efficient Function Optimization Algorithm based on Culture Evolution
64. Power loss minimization by optimal capacitor placement in radial distribution system using modified cultural algorithm10.1002/etep.1820 ↗
65. Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm10.1061/(ASCE)0733-9496(2003)129:3(210) ↗
66. Kennedy, J., and Eberhart, R. (December, January 27). Particle swarm optimization. Proceedings of the ICNN’95—International Conference on Neural Networks, Perth, Australia.
67. Shuffled frog-leaping algorithm: A memetic meta-heuristic for discrete optimization10.1080/03052150500384759 ↗
68. A modified shuffled frog-leaping optimization algorithm: Applications to project management10.1080/15732470500254535 ↗
69. Huynh, T.-H. (2009, January 10–13). Duc-Hoang Nguyen Fuzzy controller design using a new shuffled frog leaping algorithm. Proceedings of the 2009 IEEE International Conference on Industrial Technology, Gippsland, Australia.10.1109/ICIT.2009.4939563 ↗
70. Reliability constrained generation expansion planning by a modified shuffled frog leaping algorithm10.1016/j.ijepes.2014.07.073 ↗
71. Estimation of induction motor parameters using shuffled frog-leaping algorithm10.1007/s00202-012-0261-7 ↗
72. Improved shuffled frog leaping algorithm-based BP neural network and its application in bearing early fault diagnosis10.1007/s00521-015-1850-y ↗
73. Silhavy, R., Senkerik, R., Oplatkova, Z.K., Prokopova, Z., and Silhavy, P. (2015). Binarization Methods for Shuffled Frog Leaping Algorithms That Solve Set Covering Problems. Software Engineering in Intelligent Systems, Springer International Publishing.10.1007/978-3-319-18473-9 ↗
74. Comparison of Two Evolutionary Algorithms for Optimization of Bridge Deck Repairs10.1111/j.1467-8667.2006.00458.x ↗
75. Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition10.1016/j.tust.2016.12.009 ↗
76. Application of artificial neural network for predicting shaft and tip resistances of concrete piles10.15446/esrj.v19n1.38712 ↗
77. Dao, D.V., Ly, H.-B., Trinh, S.H., Le, T.-T., and Pham, B.T. (2019). Artificial Intelligence Approaches for Prediction of Compressive Strength of Geopolymer Concrete. Materials, 12.10.3390/ma12060983 ↗
78. Rock strength estimation: A PSO-based BP approach10.1007/s00521-016-2728-3 ↗
79. A novel artificial intelligence approach based on Multi-layer Perceptron Neural Network and Biogeography-based Optimization for predicting coefficient of consolidation of soil10.1016/j.catena.2018.10.004 ↗
80. Zhou, J., Nekouie, A., Arslan, C.A., Pham, B.T., and Hasanipanah, M. (2019). Novel approach for forecasting the blast-induced AOp using a hybrid fuzzy system and firefly algorithm. Eng. Comput.10.1007/s00366-019-00725-0 ↗
81. Determination of compound channel apparent shear stress: Application of novel data mining models10.2166/hydro.2019.037 ↗
82. Development of an Artificial Intelligence Approach for Prediction of Consolidation Coefficient of Soft Soil: A Sensitivity Analysis10.2174/1874836801913010178 ↗
83. Meteorological data mining and hybrid data-intelligence models for reference evaporation simulation: A case study in Iraq10.1016/j.compag.2019.105041 ↗
84. Ha-Minh, C., Dao, D.V., Benboudjema, F., Derrible, S., Huynh, D.V.K., and Tang, A.M. (2020). A Possibility of AI Application on Mode-choice Prediction of Transport Users in Hanoi. CIGOS 2019, Innovation for Sustainable Infrastructure, Springer.10.1007/978-981-15-0802-8 ↗
85. Ly, H.-B., Monteiro, E., Le, T.-T., Le, V.M., Dal, M., Regnier, G., and Pham, B.T. (2019). Prediction and Sensitivity Analysis of Bubble Dissolution Time in 3D Selective Laser Sintering Using Ensemble Decision Trees. Materials, 12.10.3390/ma12091544 ↗
86. Prediction of blast-induced air overpressure: A hybrid AI-based predictive model10.1007/s10661-015-4895-6 ↗
87. Development of artificial intelligence models for the prediction of Compression Coefficient of soil: An application of Monte Carlo sensitivity analysis10.1016/j.scitotenv.2019.05.061 ↗
88. Ha-Minh, C., Dao, D.V., Benboudjema, F., Derrible, S., Huynh, D.V.K., and Tang, A.M. (2020). Development of 48-hour Precipitation Forecasting Model using Nonlinear Autoregressive Neural Network. CIGOS 2019, Innovation for Sustainable Infrastructure, Springer.10.1007/978-981-15-0802-8 ↗
89. Stochastic continuum modeling of random interphases from atomistic simulations. Application to a polymer nanocomposite10.1016/j.cma.2015.10.006 ↗
90. Ha-Minh, C., Dao, D.V., Benboudjema, F., Derrible, S., Huynh, D.V.K., and Tang, A.M. (2020). Development of Artificial Neural Networks for Prediction of Compression Coefficient of Soft Soil. CIGOS 2019, Innovation for Sustainable Infrastructure, Springer.10.1007/978-981-15-0802-8 ↗