Structural Multimodal Analysis of Bridges Using Adaptive Neuro-Fuzzy Inference System and Artificial Intelligence Metamodels

International Journal of Civil Engineering

© 2025 by SSRG - IJCE Journal

Volume 12 Issue 6

Year of Publication : 2025

Authors : Mouna EL Mkhalet, Hicham Lamouri, Nouzha Lamdouar

10.14445/23488352/IJCE-V12I6P111

How to Cite?

Mouna EL Mkhalet, Hicham Lamouri, Nouzha Lamdouar, "Structural Multimodal Analysis of Bridges Using Adaptive Neuro-Fuzzy Inference System and Artificial Intelligence Metamodels," SSRG International Journal of Civil Engineering, vol. 12,  no. 6, pp. 122-135, 2025. Crossref, https://doi.org/10.14445/23488352/IJCE-V12I6P111

Abstract:

This study investigates using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and machine learning methods for predicting the natural vibration properties of a bridge modeled as a three-degree-of-freedom system. The ANFIS method, which integrates Fuzzy Logic and Neural Networks, was used to analyze a dataset generated from four input and three output variables representing the first three natural vibration periods. A Monte Carlo simulation created a probability density distribution for these variables. The study aims to enhance the precision of bridge response predictions using artificial intelligence, offering a more adaptable alternative to conventional analytical approaches for structural dynamic analysis. The results demonstrate the effectiveness of ANFIS in predicting natural mode periods, with a comparative analysis revealing that ANFIS models, particularly those using triangular membership functions, provide accurate predictions. The findings highlight the importance of dataset partitioning and membership function selection in optimizing ANFIS performance. A comparative evaluation with Artificial Neural Networks (ANN) and the Response Surface Method (RSM) shows that ANFIS and ANN closely match the reference values from algebraic modal analysis, while RSM exhibits some deviations. The study concludes that ANFIS is a viable method for real-world applications in structural engineering, offering a balance between computational efficiency and interpretability.

Keywords:

Reinforced Concrete Bridges, Adaptive Neuro-Fuzzy Inference System, Bridge engineering, Structural dynamics, Monte carlo simulation, Artificial Neural Network, Response Surface Methodology.

References:

[1] J.S.R. Jang, “ANFIS: Adaptive-Network-Based Fuzzy Inference System,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, no. 3, pp. 665-685, 1993.
[CrossRef] [Google Scholar] [Publisher Link]
[2] M. Masher, “Flexure and Shear Behavior of Ferrocement Members: Experimental and Theoretical Study,” PhD Thesis, Civil Engineering, Basrah University, Iraq, 2010.
[Google Scholar]
[3] S. Fallahian, and S.M. Seyedpoor, “A Two Stage Method for Structural Damage Identification Using an Adaptive Neuro-Fuzzy Inference System and Particle Swarm Optimization,” Asian Journal of Civil Engineering (Building and Housing), vol. 11, no. 6, pp. 795-808, 2010.
[Google Scholar]
[4] Abha Mittal, Shaifaly Sharma, and D.P. Kanungo, “A Comparison of ANFIS and ANN for the Prediction of Peak Ground Acceleration in Indian Himalayan Region,” Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011), vol. 2, pp. 485-495, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Shaokuan Chen et al., “Modelling and Analysis on Emergency Evacuation from Metro Stations,” Mathematical Problems in Engineering, vol. 2017, no. 1, pp. 1-11, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Bachar Kabalan et al., “Framework for Centralized and Dynamic Pedestrian Management in Railway Stations,” Transportation Research Procedia, vol. 27, pp. 712-719, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Hamad Alawad, and Sakdirat Kaewunruen, “Wireless Sensor Networks: Toward Smarter Railway Stations,” Infrastructures, vol. 3, no. 3, pp. 1-17, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Rufaizal Che Mamat, Anuar Kasa, and Siti Fatin Mohd Razali, “Performance Prediction of Road Embankment on Soft Soil Stabilized with Prefabricated Vertical Drains Using Numerical Method,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 8, no. 4, pp. 1601-1608, 2019.
[Google Scholar] [Publisher Link]
[9] D. Danilatos, “Bridge Health Monitoring through Neuro-Fuzzy Architectures,” Master of Science Thesis, Missouri University of Science and Technology, 2008.
[Google Scholar]
[10] Duc C. Nguyen et al., “Vibration-Based SHM of Dębica Railway Steel Bridge with Optimized ANN and ANFIS,” Journal of Constructional Steel Research, vol. 215, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Javad Palizvan Zand, Javad Katebi, and Saman Yaghmaei-Sabegh, “A Generalized ANFIS Controller for Vibration Mitigation of Uncertain Building Structure,” Structural Engineering and Mechanics, vol. 87, no. 3, pp. 231-242, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Neda Kiani Mavi et al., “Forecasting Project Success in the Construction Industry Using Adaptive Neuro-Fuzzy Inference System,” International Journal of Construction Management, vol. 24, no. 14, pp. 1550-1568, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Mohammad Mahdi Jafari et al., “Application of a Novel Hybrid Wavelet-ANFIS/Fuzzy C-Means Clustering Model to Predict Groundwater Fluctuations,” Atmosphere, vol. 12, no. 1, pp. 1-15, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Nasser Sekfali, and Brahim Lafifi, “Mechanically Stabilized Earth Wall Reliability Analysis Using Response Surface Methodology, ANN, ANFIS and Multi-Objective Genetic Algorithm,” Periodica Polytechnica Civil Engineering, vol. 69, no. 1, pp. 159-174, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Shivali Chopra et al., “Taxonomy of Adaptive Neuro-Fuzzy Inference System in Modern Engineering Sciences,” Computational Intelligence and Neuroscience, vol. 2021, no. 1, pp. 1-15, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Ahmed F. Bendary et al., “Proposed ANFIS Based Approach for Fault Tracking, Detection, Clearing and Rearrangement for Photovoltaic System,” Sensors, vol. 21, no. 7, pp. 1-16, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] S.O. Sada, and S.C. Ikpeseni, “Evaluation of ANN and ANFIS Modeling Ability in the Prediction of AISI 1050 Steel Machining Performance,” Heliyon, vol. 7, no. 2, pp. 1-9, 2021.
[Google Scholar] [Publisher Link]
[18] Adaptive Neuro-Fuzzy Inference System (ANFIS), Schneppat AI. [Online]. Available : https://schneppat.com/adaptive-neuro-fuzzy-inference-system-anfis.html
[19] Mohd Najib Mohd Salleh, Noureen Talpur, and Kashif Hussain, “Adaptive Neuro-Fuzzy Inference System: Overview, Strengths, Limitations, and Solutions,” Data Mining and Big Data, Lecture Notes in Computer Science, vol. 10387, pp. 527-535, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Sri Sumarlinda, “The Improvement Prediction Model Using Anfis for Medical Dataset,” Journal of Theoretical and Applied Information Technology, vol. 102, no. 5, pp. 1663-1672, 2024.
[Google Scholar] [Publisher Link]
[21] Simon S. Haykin, Neural Networks and Learning Machines, 3rd ed., Pearson, pp. 1-934, 2009.
[Google Scholar] [Publisher Link]
[22] L.A. Zadeh, “Fuzzy Logic = Computing with Words,” IEEE Transactions on Fuzzy Systems, vol. 4, no. 2, pp. 103-111, 1996.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Raymond H. Myers, Douglas C. Montgomery, and Christine M. Anderson-Cook, Response Surface Methodology: Process and Product Optimization Using Designed Experiments, 3rd ed., Wiley, 2009.
[Google Scholar] [Publisher Link]
[24] Alex J. Smola, and Bernhard Schölkopf, “A Tutorial on Support Vector Regression,” Statistics and Computing, vol. 14, pp. 199-222, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Vladimir N. Vapnik, The Nature of Statistical Learning Theory, 2nd ed., Springer New York, NY, 2000.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Ajith Abraham, “Neuro Fuzzy Systems: State-of-the-Art Modeling Techniques,” Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence, Lecture Notes in Computer Science, vol. 2084, pp. 269-276, 2001.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Mohammad Fazle Azeem, Fuzzy Inference System - Theory and Applications, InTechOpen, pp. 1-518, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Robert Fuller, Neural Fuzzy Systems, Abo Akademi University, pp. 1-348, 1995.
[Google Scholar]
[29] Ding Youliang, and Li Aiqun, “Structural Health Monitoring of Long-Span Suspension Bridges Using Wavelet Packet Analysis,” Earthquake Engineering and Engineering Vibration, vol. 6, pp. 289-294, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Mohammad Muzzammil, “ANFIS Approach to the Scour Depth Prediction at a Bridge Abutment,” Journal of Hydroinformatics, vol. 12, no. 4, pp. 474-485, 2010.
[CrossRef] [Google Scholar] [Publisher Link]