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

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Year : 2026 | Volume : 13 | Issue : 01 | Page : 23 33
By

Ritesh Vishwakarma,

Karthik Nagarajan,

Raju Narwade,

  1. Post Graduate Student, Department of Civil Engineering, Pillai HOC College of Engineering and Technology, Rasayani, University of Mumbai, , india
  2. Assistant Professor, Department of Civil Engineering, Pillai HOC College of Engineering and Technology, Rasayani, University of Mumbai, , India
  3. Associate Professor, Department of Civil Engineering, Pillai HOC College of Engineering and Technology, Rasayani, University of Mumbai, , India

Abstract

Structural health monitoring (SHM) has a critical role in ensuring 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 modeling 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 the
digitalization of urban infrastructure. Smarter decision-making and predictive maintenance by ANNbased SHM enhance safety considerably, cost savings are realized, and smart city sustainability is
enhanced.

Keywords: Structural health monitoring, artificial neural networks, damage detection

[This article belongs to Journal of Structural Engineering and Management ]

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):23-33.
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):23-33. Available from: https://journals.stmjournals.com/josem/article=2026/view=239111

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Regular Issue Subscription Review Article
Volume 13
Issue 01
Received 30/12/2025
Accepted 28/01/2026
Published 30/01/2026
Publication Time 31 Days

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