Optimization Of Structural Health Monitoring For Steel Bridges Using Wireless Sensor Networks and Machine Learning Algorithms
DOI:
https://doi.org/10.61132/ijmecie.v1i2.65Keywords:
Structural Health Monitoring, Steel Bridges, Wireless Sensor Networks, Machine Learning, Anomaly DetectionAbstract
This study presents an advanced structural health monitoring (SHM) system for steel bridges based on wireless sensor networks (WSN) integrated with machine learning algorithms. The proposed system monitors and predicts structural integrity under various load conditions. The research focuses on developing a machine learning model capable of real-time anomaly detection, allowing for early warnings of potential failures. Experimental results from both simulation and field tests demonstrate the system’s effectiveness in prolonging bridge lifespan while reducing maintenance costs.
References
Brown, T., & Smith, J. (2020). Structural health monitoring for steel bridges using wireless sensor networks: A review. Journal of Structural Engineering, 146(2), 04020005.
Chen, T., & Li, H. (2022). Optimizing sensor placements in SHM systems using genetic algorithms and machine learning. Computer-Aided Civil and Infrastructure Engineering, 37(3), 235–248.
Choi, M., & Park, S. (2021). Machine learning-based anomaly detection in bridge monitoring systems. Computers and Structures, 242, 106366.
Garcia, J., & Martinez, R. (2019). Integration of wireless sensors and machine learning for bridge condition monitoring. Sensors, 19(8), 1886.
Jones, P., & Taylor, S. (2022). Optimization algorithms in SHM for steel bridges with wireless sensor networks. Journal of Civil Structural Health Monitoring, 12(1), 29–42.
Kim, H. J., & Lee, D. (2018). Optimization of wireless sensor nodes in SHM systems for bridge structures. IEEE Sensors Journal, 18(5), 2098–2105.
Lee, C., & Huang, W. (2018). Dynamic structural health monitoring using WSN and ML algorithms. Mechanical Systems and Signal Processing, 107, 74–86.
Liu, Z., & Chen, X. (2020). Wireless sensor network design for structural health monitoring on large steel bridges. Engineering Structures, 223, 111215.
Rogers, K., & Powell, A. (2020). Real-time damage detection in steel bridges using WSN and machine learning. IEEE Transactions on Industrial Informatics, 16(9), 5706–5715.
Singh, A., & Gupta, P. (2019). A review of wireless sensor networks for SHM in civil engineering. Journal of Sensors, 2019, 8637241.
Thompson, R., & White, P. (2017). Application of machine learning in SHM of civil infrastructure. Journal of Infrastructure Systems, 23(1), 04016030.
Wang, L., & Liu, Y. (2019). Wireless sensor network-based SHM in steel bridges: Advances and challenges. Automation in Construction, 100, 86–98.
Wang, Q., & Li, Z. (2021). Enhanced structural health monitoring with wireless sensor networks and machine learning algorithms. Advances in Civil Engineering, 2021, 6679180.
Xu, M., & Zhao, Y. (2021). Data-driven approaches for structural health monitoring using machine learning techniques. Structural Safety, 89, 102070.
Zhang, Y., & Sun, L. (2022). Machine learning for fault detection in bridge monitoring applications. Structural Control and Health Monitoring, 29(3), e2903.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 International Journal of Mechanical, Electrical and Civil Engineering
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
