Survey Paper on Enhancing Damage and Collapse Alert System for Bridges

Abstract

The project focuses on detecting bridge health and preventing collapse using sensors. It monitors factors like cracks, vibrations, water level, and pressure to assess the bridge’s condition. When danger is detected, the system automatically alerts nearby authorities such as police stations, PHCs, and transportation departments, and activates signals and barriers to stop traffic and prevent accidents. Using Structural Health Monitoring (SHM), the system provides real-time data to help engineers ensure safety, plan maintenance, and extend the bridge’s lifespan. This enhances public safety, reduces accidents, and supports timely emergency response.

Introduction

The project proposes an IoT-based Bridge Health Monitoring and Safety System designed to continuously monitor structural conditions and prevent accidents. Using sensors to detect vibrations, cracks, and water levels, the system identifies abnormal conditions such as structural damage, flooding, or potential collapse. When a risk is detected, it automatically sends alerts to nearby government authorities (police, PHCs, transportation departments), activates traffic barriers and warning signals up to 5 km around the bridge, and displays safety messages to prevent vehicles from crossing.

The system is built on Structural Health Monitoring (SHM) principles and uses an Event-Triggered Threshold Algorithm (ETTA), which compares real-time sensor data against predefined safety thresholds. This approach enables fast decision-making, reduces power consumption, and ensures timely emergency response. Data is processed by an Arduino microcontroller and transmitted via GSM/Wi-Fi to a cloud platform for real-time monitoring and long-term analysis.

The methodology includes sensor deployment at critical bridge points, real-time data acquisition, wireless communication, automated alert generation, and traffic control. A mathematical risk model combines vibration, water level, and crack data to predict collapse risk. The layered system architecture—sensing, processing, communication, and application/cloud—ensures reliable monitoring, early fault detection, and efficient infrastructure management.

Overall, the system enhances public safety, supports proactive maintenance, reduces the risk of sudden bridge failures, and contributes to sustainable and smart transportation infrastructure using modern IoT and automation technologies.

Conclusion

As previously there were no forecasted notifications of bridge health, proposed system will send notifications to specified authorities . Accelerations from sensors distributed over the bridge will be analysed using an accelerometer. The environmental conditions will be taken into considerations during updating health of structure. The water level sensor will monitor the level of water that will be displayed using Arduino board. The overall data will be analyse on the cloud Finally, the Bridge diagnosis system using sensor network and sensor module is introduced. Furthermore, predictive insights obtained through continuous monitoring help engineers plan maintenance activities, extend the lifespan of bridges, and ensure public safety. Overall, this project presents a unified, intelligent, and highly scalable solution for modern bridge safety management.

References

[1] S. Pasika and S. T. Gandla, \"Real-Time Monitoring System for Bridge Safety Using IoT,\" International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), vol. 3, no. 9, pp. 120– 125, May 2023. [2] K. R. Kavitha and A. J. Aravind, \"Detecting Under Bridge Flood Risks—An IoT Approach,\" Journal of Current Research in Engineering and Science (JCRES), vol. 2, no. 2, pp. 70–75, Feb. 2023. [3] S. K. Sharma, \"IoT-Based Bridge Monitoring System with Flood Detection,\" International Research Journal of Modernization in Engineering Technology and Science (IRJMETS). [4] R. Al-Ali, S. Beheiry, A. Alnabulsi, S. Obaid, N. Mansoor, N. Odeh, and A. Mostafa, \"An IoT Based Road Bridge Health Monitoring and Warning System,\" Sensors, vol. 24, no. 2,p. 469, Jan. 2024. [5] Harutoshi Ogai, Jong-In Cheon, Ming- Yuan Hsieh, Hiroshi Inujima, Noriyoshi Yamauchi, Development of Bridge Diagnosis System by Using Sensor Network and Independent Component Analysis, in 16th IEEE International Conference on Control Applications, Part of IEEE Multi-conference on Systems and Control, Singapore,1-3 October 2007. [6] Shinae Jang and Billie F. Spencer, Jr., Structural Health Monitoring for Bridge Structures using Smart Sensors, in NSEL Report Series Report No. NSEL-03 5 May 2015. [7] Aspectral-based clustering for structural health monitoring of the Sydney Harbour Bridge Mehrisadat Makki Alamdari , Thierry Rakotoarivelo, Nguyen Lu Dang Khoa CSIRO,Data61, 13 Garden Street, Eveleigh, NSW 2015, Australia.

Copyright

Copyright © 2026 Dr. S. P. Pingat , Pooja Patil, Vrushali Shinde, Gayatri Bogawar, Pratiksha More. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.