AI-Driven Structural Health Monitoring in Civil Infrastructure Using Real-Time IoT Sensor Networks and Edge Analytics

R. Suganya, K. Madhu Suganya, K. Suresh, S. Kannadhasan, M. Bhagavanthu, Syed Zahidur Rashid

2025

Abstract

It must develop robust, intelligent, and scalable solutions for structural health monitoring (SHM) to cope with the ever-increasing complexity and ageing of civil infrastructure. We provide a new AI-driven framework that combines IoT sensor networks (SN), edge analytics, and federated learning to achieve continuous, precise, explainable monitoring of structural integrity. Existing SHM systems depend extensively on cloud-based models and/or limited sensor modalities (e.g., only vibration), whereas our method utilizes multi-modality sensing (vibration, displacement, and environmental) fused with edge-enabled preprocessing to maximize detection-speed at minimal latencies. It is scalable for larger infrastructures and can integrate nicely to digital twin platforms for predictive simulations and long-term asset management. Incorporating these explainable AI (XAI) components can improve the interpretability of predictions, thereby increasing trust in the model towards civil engineers and stakeholders. Federated learning and edge-level encryption secure privacy while minimizing risks related to centralized data storage. It also offers drift detection, automatic fault correction, and a low-bandwidth mode for deployment in remote locations. The proposed solution has been tested on real life deployments, achieving better accuracy, responsiveness and reliability than existing models.

Paper Citation

in Harvard Style

Suganya R., Suganya K., Suresh K., Kannadhasan S., Bhagavanthu M. and Rashid S. (2025). AI-Driven Structural Health Monitoring in Civil Infrastructure Using Real-Time IoT Sensor Networks and Edge Analytics. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 779-788. DOI: 10.5220/0013889800004919

in Bibtex Style

@conference{icrdicct`2525,
author={R. Suganya and K. Suganya and K. Suresh and S. Kannadhasan and M. Bhagavanthu and Syed Rashid},
title={AI-Driven Structural Health Monitoring in Civil Infrastructure Using Real-Time IoT Sensor Networks and Edge Analytics},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={779-788},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013889800004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - AI-Driven Structural Health Monitoring in Civil Infrastructure Using Real-Time IoT Sensor Networks and Edge Analytics
SN - 978-989-758-777-1
AU - Suganya R.
AU - Suganya K.
AU - Suresh K.
AU - Kannadhasan S.
AU - Bhagavanthu M.
AU - Rashid S.
PY - 2025
SP - 779
EP - 788
DO - 10.5220/0013889800004919
PB - SciTePress