Optimization of Structural Health Monitoring Using Artificial Neural Network and Comparison with Traditional Method: A Comprehensive Review

Year : 2026 | Volume : 13 | 01 | Page :
    By

    Ritesh Vishwakarma,

  • Karthik Nagarajan,

  • Raju Narwade,

Abstract

Structural Health Monitoring (SHM) has a critical role to ensure civil infrastructure safety, reliability,
and durability through real-time, condition-based monitoring. Traditional SHM systems employ hundreds
of sensors such as accelerometers, strain gauges, and displacement transducers for monitoring vast
amounts of data for structural inspection but do not effectively manage complicated nonlinear data. This
research paper, “Optimization of Structural Health Monitoring Using Artificial Neural Network and
Comparison with Traditional Methods,” investigates the feasibility of the optimization of SHM
performance by employing Artificial Neural Networks (ANN) for improved interpretation of data,
accuracy of prediction, and decision-making in maintenance. The research process constituted extensive
literature review, bridge modelling as a simulation platform, and experimentation with ANN models like
feedforward, convolutional, and recurrent networks. ANN enables efficient analysis of sensor outputs,
pattern recognition of damage, and prediction of damage growth with increased accuracy in structural
diagnosis. Comparative investigation with conventional SHM methods verifies that ANN-based systems
exhibit better computational efficiency, accuracy, and real-time performance. But needs such as data
quality demands, interpretability of the model, and computationally intensive analysis remain. The results
highlight that the integration of ANN makes SHM an intelligent, dynamic, and futuristic system, and thus
a leap and bound improvement in digitalization of urban infrastructure. Smarter decision-making and
predictive maintenance by ANN- based SHM enhance safety considerably, cost savings are realized, and
smart city sustainability is enhanced.

Keywords: Structural Health Monitoring, Artificial Neural Networks, Damage Detection.

How to cite this article:
Ritesh Vishwakarma, Karthik Nagarajan, Raju Narwade. Optimization of Structural Health Monitoring Using Artificial Neural Network and Comparison with Traditional Method: A Comprehensive Review. Journal of Structural Engineering and Management. 2026; 13(01):-.
How to cite this URL:
Ritesh Vishwakarma, Karthik Nagarajan, Raju Narwade. Optimization of Structural Health Monitoring Using Artificial Neural Network and Comparison with Traditional Method: A Comprehensive Review. Journal of Structural Engineering and Management. 2026; 13(01):-. Available from: https://journals.stmjournals.com/josem/article=2026/view=238023


References

Ahead of Print Subscription Review Article
Volume 13
01
Received 30/12/2025
Accepted 28/01/2026
Published 30/01/2026
Publication Time 31 Days
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