پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 127 |
| تعداد شمارهها | 7,237 |
| تعداد مقالات | 77,624 |
| تعداد مشاهده مقاله | 161,089,150 |
| تعداد دریافت فایل اصل مقاله | 120,788,057 |
Hybrid Neuro-Fuzzy ML and MC Simulation-Based Reliability Analysis of Simply Supported Beam | ||
| Civil Engineering Infrastructures Journal | ||
| دوره 59، شماره 1، شهریور 2026، صفحه 45-63 اصل مقاله (1.29 M) | ||
| نوع مقاله: Research Papers | ||
| شناسه دیجیتال (DOI): 10.22059/ceij.2024.374883.2045 | ||
| نویسندگان | ||
| Abhishek Kumar1؛ Baboo Rai* 2؛ Pijush Samui3 | ||
| 1Ph.D. Candidate, Department of Civil Engineering, National Institute of Technology, Patna, India. | ||
| 2Associate Professor, Department of Civil Engineering, National Institute of Technology, Patna, India. | ||
| 3Professor, Department of Civil Engineering, National Institute of Technology, Patna, India. | ||
| چکیده | ||
| This paper introduces an innovative approach utilizing hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) models for the reliability-based design of structural beams. While structural reliability analysis with hybrid ANFIS models remains largely unexplored, existing studies primarily rely on rudimentary simulation models. To address this gap, this study employs Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) to enhance the performance of the ANFIS model. The Machine Learning (ML) models are validated on three Monte-Carlo datasets of size 1000, 2500, and 5000. The findings demonstrate satisfactory performance across all ML models, with the hybrid ANFIS models exhibiting superior predictive capabilities compared to traditional methods. Among the hybrid ANFIS models, ANFIS-PSO emerges as the most robust. The reliability indices and Probability of Failure (POF) values are calculated for the predicted values and compared with actual values. It is concluded that the ANFIS-PSO-based methodology is the most robust model and outperforms the other models. It is noteworthy that while the ANFIS-PSO model demonstrates exceptional performance, all models presented in this study serve as valuable tools for reliability-based structural design, offering robust alternatives to conventional methodologies. | ||
| کلیدواژهها | ||
| Reliability Analysis؛ Machine Learning؛ Hybrid ANFIS؛ Simply Supported Beam؛ Monte-Carlo Simulation | ||
| مراجع | ||
|
Achouri, F., Khatir, A., Smahi, Z., Capozucca, R. and Ouled Brahim, A. (2023). “Structural health monitoring of beam model based on swarm intelligence-based algorithms and neural networks employing FRF”, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 45, Article 621, https://doi.org/10.1007/s40430-023-04525-y.
Acito, M., Stochino, F. and Tattoni, S. (2011). “Structural response and reliability analysis of RC beam subjected to explosive loading”, Applied Mechanics and Materials, 82, 434-439, https://doi.org/10.4028/www.scientific.net/amm.82.434.
Atsalakis, G.S., Atsalaki, I.G. and Zopounidis, C. (2018). “Forecasting the success of a new tourism service by a neuro-fuzzy technique”, European Journal of Operational Research, 268(2), 716-727, https://doi.org/10.1016/J.EJOR.2018.01.044.
Ayyub, B.M. and Haldar, A. (1984). “Practical structural reliability techniques”, Journal of Structural Engineering, 110(8), 1707-1724, https://doi.org/10.1061/(ASCE)0733-9445(1984)110:8(1707).
Bureau of Indian Standards (1984). IS 800:1984 – Code of practice for general construction in steel, New Delhi, India: Bureau of Indian Standards.
Burges, C.J.C. (1998). “A tutorial on support vector machines for pattern recognition”, Data Mining and Knowledge Discovery, 2(2), 121-167, https://doi.org/10.1023/A:1009715923555.
Cardoso, J.B., de Almeida, J.R., Dias, J.M. and Coelho, P.G. (2008). “Structural reliability analysis using Monte Carlo simulation and neural networks”, Advances in Engineering Software, 39(6), 505-513, https://doi.org/10.1016/j.advengsoft.2007.03.015.
Ching, J. and Phoon, K.K. (2011). “A quantile-based approach for calibrating reliability-based partial factors”, Structural Safety, 33(4-5), 275-285, https://doi.org/10.1016/j.strusafe.2011.04.002.
Godil, S.S., Shamim, M.S., Enam, S.A. and Qidwai, U. (2011). “Fuzzy logic: A “simple” solution for complexities in neurosciences”, Surgical Neurology International, 2, 24, https://doi.org/10.4103/2152-7806.77177.
Haldar, A. and Mahadevan, S. (2000). Probability, reliability, and statistical methods in engineering design, John Wiley, https://doi.org/10.1002/bate.200002930.
Hao, P., Feng, S., Liu, H., Wang, Y., Wang, B. and Wang, B. (2021). “A novel nested stochastic kriging model for response noise quantification and reliability analysis”, Computer Methods in Applied Mechanics and Engineering, 384, 113941, https://doi.org/10.1016/j.cma.2021.113941.
Hao, P., Wang, Y., Ma, R., Liu, H., Wang, B. and Li, G. (2019). “A new reliability-based design optimization framework using isogeometric analysis”, Computer Methods in Applied Mechanics and Engineering, 345, 476-501, https://doi.org/10.1016/j.cma.2018.11.008.
Harr, M.E. (1987). Reliability-based design in civil engineering, Dover Publications, https://search.worldcat.org/title/14242672.
Jang, J.S.R. (1993). “ANFIS: adaptive-network-based fuzzy inference system”, IEEE Transactions on Systems, Man and Cybernetics, 23(3), 665-685, https://doi.org/10.1109/21.256541.
Kar, S.S. and Roy, L.B. (2022). “Probabilistic based reliability slope stability analysis using FOSM, FORM, and MCS”, Engineering, Technology and Applied Science Research, 12(2), 8236-8240, https://doi.org/10.48084/etasr.4689.
Karimi Sharafshadeh, B., Ketabdari, M.J., Azarsina, F., Amiri, M. and Nehdi, M.L. (2023). “New fuzzy-heuristic methodology for analyzing compression load capacity of composite columns”, Buildings, 13(1), 125, https://doi.org/10.3390/buildings13010125.
Kennedy, J. and Eberhart, R. (1995). “Particle swarm optimization”, Proceedings of IEEE International Conference on Neural Networks, Perth, WA, Australia, https://doi.org/10.1109/ICNN.1995.488968.
Kumar, D.R., Wipulanusat, W., Kumar, M., Keawsawasvong, S. and Samui, P. (2024a). ”Optimized neural network-based state-of-the-art soft computing models for the bearing capacity of strip footings subjected to inclined loading”, Intelligent Systems with Applications, 21, 200314, https://doi.org/10.1016/j.iswa.2023.200314.
Kumar, D.R., Wipulanusat, W., Sunkpho, J., Keawsawasvong, S., Jitchaijaroen, W. and Samui, P. (2024b). “Machine learning approaches for the prediction of the seismic stability of unsupported rectangular excavation”, Engineered Science, 28, 1083, https://doi.org/10.30919/es1083.
Kumar, M., Fathima, N.Z. and Kumar, D.R. (2024c). “A novel XGBoost and RF-based metaheuristic models for concrete compression strength”, Lecture Notes in Civil Engineering, 388, 495-503, https://doi.org/10.1007/978-981-99-6233-4_45.
Kumar, M., Kumar, D.R., Khatti, J., Samui, P. and Grover, K.S. (2024d). “Prediction of bearing capacity of pile foundation using deep learning approaches”, Frontiers of Structural and Civil Engineering, 18, 870-886, https://doi.org/10.1007/s11709-024-1085-z.
Kumar, M., Samui, P., Kumar, D.R. and Asteris, P.G. (2024e). “State-of-the-art XGBoost, RF and DNN based soft-computing models for PGPN piles”, Geomechanics and Geoengineering, 19(6), 975-990, https://doi.org/10.1080/17486025.2024.2337702.
Kumar, M. and TN, D.S. (2023). “Genetic programming based compressive strength prediction model for green concrete”, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2023.03.024.
Kumar, N. and Kumari, S. (2024). “State-of-the-art advanced hybrid ANN’s paradigm for assessment and prediction of slope stability”, Multiscale and Multidisciplinary Modelling, Experiments and Design, 7, 3483-3509, https://doi.org/10.1007/S41939-024-00415-5.
Kumar, R., Kumar, A. and Ranjan Kumar, D. (2024f). “Buckling response of CNT based hybrid FG plates using finite element method and machine learning method“, Composite Structures, 319, 117204, https://doi.org/10.1016/j.compstruct.2023.117204.
Kumar, V., Burman, A. and Kumar, M. (2024g). “Generic form of stability charts using slide software for rock slopes based on the Hoek-Brown failure criterion”, Multiscale and Multidisciplinary Modelling, Experiments and Design, 7, 975-989, https://doi.org/10.1007/s41939-023-00265-7.
Lee, T.H. and Mosalam, K.M. (2005). “Seismic demand sensitivity of reinforced concrete shear‐wall building using FOSM method”, Earthquake Engineering and Structural Dynamics, 34(14), 1719-1736, https://doi.org/10.1002/eqe.506.
Li, S., Zhao, H.B. and Ru, Z. (2013). “Slope reliability analysis by updated support vector machine and Monte Carlo simulation”, Natural Hazards, 65(1), 707-722, https://doi.org/10.1007/s11069-012-0396-x.
Liu, X., Li, T., Zhou, Z. and Hu, L. (2022). “An efficient multi-objective reliability-based design optimization method for structure based on probability and interval hybrid model“, Computer Methods in Applied Mechanics and Engineering, 392, 114682, https://doi.org/10.1016/j.cma.2022.114682.
Ly, H.B., Pham, B.T., Le, L.M., Le, T.T., Le, V.M. and Asteris, P.G. (2021). “Estimation of axial load-carrying capacity of concrete-filled steel tubes using surrogate models”, Neural Computing and Applications, 33(8), 3437-3458, https://doi.org/10.1007/s00521-020-05214-w.
Mishra, B.B., Kumar, A., Zaburko, J., Sadowska-Buraczewska, B. and Barnat-Hunek, D. (2021). “Dynamic response of angle ply laminates with uncertainties using MARS, ANN-PSO, GPR and ANFIS”, Materials, 14(2), 395, https://doi.org/10.3390/ma14020395.
Mohammadizadeh, M.R., Esfandnia, F. and Khatibinia, M. (2023). “Prediction of shear strength of reinforced concrete deep beams using neuro-fuzzy inference system and meta-heuristic algorithms”, Civil Engineering Infrastructures Journal, 56(1), 137-157, https://doi.org/10.22059/ceij.2022.334953.1803.
Naess, A., Leira, B.J. and Batsevych, O. (2012). “Reliability analysis of large structural systems”, Probabilistic Engineering Mechanics, 28, 164-168, https://doi.org/10.1016/j.probengmech.2011.08.024.
Papadrakakis, M., Papadopoulos, V. and Lagaros, N.D. (1996). “Structural reliability analysis of elastic-plastic structures using neural networks and Monte Carlo simulation”, Computer Methods in Applied Mechanics and Engineering, 136(1-2), 145-163, https://doi.org/10.1016/0045-7825(96)01011-0.
Pezeshki, Z. and Mazinani, S.M. (2019). “Comparison of artificial neural networks, fuzzy logic and neuro fuzzy for predicting optimization of building thermal consumption: a survey“, Artificial Intelligence Review, 52(1), 495-525, https://doi.org/10.1007/s10462-018-9630-6/figures/10.
Pradeep, T., Bardhan, A., Burman, A. and Samui, P. (2021). “Rock strain prediction using deep neural network and hybrid models of ANFIS and meta-heuristic optimization algorithms”, Infrastructures, 6(9), 129, https://doi.org/10.3390/infrastructures6090129.
Samui, P., Kurup, P., Dhivya, S. and Jagan, J. (2016). “Reliability analysis of quick sand condition”, Geotechnical and Geological Engineering, 34(2), 579-584, https://doi.org/10.1007/s10706-015-9967-4.
Saraygord Afshari, S., Enayatollahi, F., Xu, X. and Liang, X. (2022a). “Machine learning-based methods in structural reliability analysis: A review”, Reliability Engineering and System Safety, 219, 108223, https://doi.org/10.1016/j.ress.2021.108223.
Saraygord Afshari, S., Enayatollahi, F., Xu, X. and Liang, X. (2022b). “Machine learning-based methods in structural reliability analysis: a review”, Reliability Engineering and System Safety, 219, 108223, https://doi.org/10.1016/j.ress.2021.108223.
Şener, R., Koç, M.A. and Ermiş, K. (2024). “Hybrid ANFIS-PSO algorithm for estimation of the characteristics of porous vacuum preloaded air bearings and comparison performance of the intelligent algorithm with the ANN”, Engineering Applications of Artificial Intelligence, 128, 107460, https://doi.org/10.1016/j.engappai.2023.107460.
Singh, P., Kumar, D. and Samui, P. (2020). “Reliability analysis of rock slope using soft computing techniques”, Jordan Journal of Civil Engineering, 14(1), 27-42, https://www.researchgate.net/publication/349393580.
Sivakumar Babu, G.L. and Srivastava, A. (2007). “Reliability analysis of allowable pressure on shallow foundation using response surface method”, Computers and Geotechnics, 34(3), 187-194, https://doi.org/10.1016/j.compgeo.2006.11.002.
Sivakumar Babu, G.L., Srivastava, A. and Murthy, D.S. (2006). “Reliability analysis of the bearing capacity of a shallow foundation resting on cohesive soil”, 43(2), 217-223, https://doi.org/10.1139/T05-099.
Strauss, A., Hoffmann, S., Wendner, R. and Bergmeister, K. (2009). “Structural assessment and reliability analysis for existing engineering structures, applications for real structures”, Structure and Infrastructure Engineering, 5(4), 277-286, https://doi.org/10.1080/15732470601185638.
Taylor, K.E. (2001). “Summarizing multiple aspects of model performance in a single diagram”, Journal of Geophysical Research: Atmospheres, 106(D7), 7183-7192, https://doi.org/10.1029/2000JD900719.
Toratti, T., Schnabl, S. and Turk, G. (2007). “Reliability analysis of a glulam beam”, Structural Safety, 29(4), 279-293, https://doi.org/10.1016/j.strusafe.2006.07.011.
Zargari, A.S., Khorshidi, A.N., Mirzahossein, H. and Shakoori, S. (2024). “Predicting the fluctuation of travel time reliability as a result of congestion variations by bagging-based regressors”, Civil Engineering Infrastructures Journal, 57(1), 85-101, https://doi.org/10.22059/CEIJ.2023.349853.1878. | ||
|
آمار تعداد مشاهده مقاله: 525 تعداد دریافت فایل اصل مقاله: 436 |
||
سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب