International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Volume 8 Issue 2
March-April 2026

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Data-Driven Structural Health Monitoring Using Artificial Intelligence

Author(s) Mr. Surya Dhote, Mr. Rishabh Singh
Country India
Abstract Structural Health Monitoring (SHM) plays a critical role in ensuring the safety and reliability of civil engineering infrastructure. This paper investigates the application of artificial intelligence (AI) techniques for vibration-based structural health monitoring, with a focus on damage detection and damage severity estimation. Four AI models are evaluated: Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) network. Two benchmark SHM datasets are employed for model development and independent validation. The methodology integrates structural dynamics principles with data-driven modeling to ensure engineering relevance. Results demonstrate that deep learning models, particularly CNN and LSTM, outperform classical machine learning methods in damage detection accuracy, achieving 97.6% and 96.9% respectively. LSTM networks exhibit the best severity estimation performance with a mean absolute error of 0.054 and the strongest generalization capability across datasets. Random Forest provides the best trade-off between performance, robustness, and interpretability through feature importance analysis. The study relies on benchmark datasets and acknowledges limitations regarding environmental variability and deep learning interpretability. The comparative analysis offers practical guidance for selecting appropriate AI models for SHM applications in civil engineering. Future directions include physics-informed neural networks, transfer learning, explainable AI, and real-time edge deployment.
Keywords Structural Health Monitoring, Artificial Intelligence, Machine Learning, Deep Learning, Vibration-Based Damage Detection, Civil Engineering Infrastructure, Convolutional Neural Network, Long Short-Term Memory
Field Engineering
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-03-31
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E-ISSN 2582-2160
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