Contact : 9561854529 Email : submitpaper@ijaea.com editorinchief.ijaea@gmail.com Working Hours: 9am to 9PM

International Journal of Advanced Engineering Application

ISSN: 3048-6807

Submit Paper Apply for Reviewer

IoT-Enabled Real-Time Structural Health Monitoring of Reinforced Concrete Bridges

Author(s):Arjun Venkataraman, Deepa Krishnaswamy, Suresh Babu

Affiliation: Department of Civil Engineering, National Institute of Technology Karnataka, Surathkal, India Department of Electronics & Communication Engineering, Indian Institute of Technology Hyderabad, Hyderabad, India

Page No: 41-48

Volume issue & Publishing Year: Volume 3, Issue 6, 2026/06/08

Journal: International Journal of Advanced Engineering Application (IJAEA)

ISSN NO: 3048-6807

DOI:

Download PDF

Abstract:
The accelerating deterioration of India's aging reinforced concrete (RC) bridge stock — with an estimated 42% of the National Highway Authority of India (NHAI) inventory exceeding 25 years of service life — demands cost-effective, continuous structural health monitoring (SHM) solutions capable of detecting incipient damage before catastrophic failure. Traditional periodic visual inspection supplemented by non-destructive testing (NDT) is resource-intensive, subjective, and temporally sparse, creating detection gaps that climate-accelerated corrosion and heavy axle loading progressively exploit. This study presents the design, deployment, and 18-month performance validation of a wireless sensor network (WSN)-based SHM system integrating MEMS accelerometers, fibre Bragg grating (FBG) strain sensors, corrosion potential probes, and ambient temperature-humidity nodes across three RC bridges on NH-75 in coastal Karnataka — a region combining aggressive marine chloride exposure (Cl⁻ concentration 380–620 mg/L groundwater), high relative humidity (annual mean RH 78%), and heterogeneous traffic loading including overloaded goods vehicles averaging 42 tonnes gross vehicle weight (GVW). The proposed architecture employs edge computing nodes (Raspberry Pi 4B with TensorFlow Lite) for real-time feature extraction and a lightweight LSTM-based anomaly detection algorithm that achieves 94.3% detection accuracy for simulated damage scenarios with a false alarm rate of 2.1%, outperforming threshold-based detection (82.7% accuracy, 8.4% FAR) and wavelet energy analysis (89.1% accuracy, 5.3% FAR). Sensor fusion combining accelerometric modal frequency tracking with FBG strain redistribution indicators reduces missed detections by 31% relative to single-modality systems. Over the 18-month monitoring period, the system identified progressive neutral axis elevation in Bridge B (indicating concrete cracking at mid-span) confirmed by subsequent core sampling, and rebar corrosion initiation signals in Bridge C's pier caps corroborated by half-cell potential mapping. Cloud-based data aggregation via AWS IoT Core enables centralised fleet monitoring and automated deterioration trajectory forecasting using a physics-informed neural network (PINN) surrogate model calibrated against finite element reference models developed in ANSYS Mechanical.

Keywords: structural health monitoring, SHM, IoT sensors, wireless sensor network, MEMS accelerometer, fibre Bragg grating, edge computing, LSTM anomaly detection, reinforced concrete bridges, corrosion monitoring, India, NH-75, physics-informed neural network

Reference:

  • [1] Brincker, R., & Ventura, C. (2015). Introduction to operational modal analysis. Wiley.
  • [2] Broomfield, J. P. (2007). Corrosion of steel in concrete: Understanding, investigation and repair (2nd ed.). Taylor & Francis.
  • [3] Doebling, S. W., Farrar, C. R., & Prime, M. B. (1998). A summary review of vibration-based damage identification methods. Shock and Vibration Digest, 30(2), 91–105.
  • [4] Farrar, C. R., & Worden, K. (2012). Structural health monitoring: A machine learning perspective. Wiley.
  • [5] Glisic, B., & Inaudi, D. (2007). Fibre optic methods for structural health monitoring. Wiley.
  • [6] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
  • [7] IS 456:2000. Plain and reinforced concrete – code of practice (4th revision). Bureau of Indian Standards.
  • [8] IRC:SP:35-1990. Guidelines for inspection and maintenance of bridges. Indian Roads Congress.
  • [9] Pandey, A. K., & Biswas, M. (1994). Damage detection in structures using changes in flexibility. Journal of Sound and Vibration, 169(1), 3–17.
  • [10] Peeters, B., & De Roeck, G. (2001). One-year monitoring of the Z24-bridge: Environmental effects versus damage events. Earthquake Engineering & Structural Dynamics, 30(2), 149–171.
  • [11] Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems. Journal of Computational Physics, 378, 686–707.
  • [12] Sharma, R. K., Mehta, V. P., & Singh, A. K. (2022). Corrosion assessment of highway bridges in coastal India using electrochemical monitoring. Journal of Bridge Engineering (ASCE), 27(4), 04022009.
  • [13] Suresh Kumar, M., & Raghunath, P. N. (2021). Structural health monitoring of reinforced concrete bridges: Review and challenges for Indian infrastructure. Structural Control and Health Monitoring, 28(9), e2784.
  • [14] Worden, K., Farrar, C. R., Manson, G., & Park, G. (2007). The fundamental axioms of structural health monitoring. Proceedings of the Royal Society A, 463(2082), 1639–1664.
  • [15] Ye, X. W., Dong, C. Z., & Liu, T. (2016). A review of machine vision-based structural health monitoring: Methodologies and applications. Journal of Sensors, 2016, Article 7103039.
  • [16] Zhang, Y., Miyamori, Y., Mikami, S., & Saito, T. (2019). Vibration-based structural state identification by a 1D convolutional neural network. Computer-Aided Civil and Infrastructure Engineering, 34(9), 822–839.
  • [17] Zhao, J., & DeWolf, J. T. (1999). Sensitivity study for vibrational parameters used in damage detection. Journal of Structural Engineering (ASCE), 125(4), 410–416.