AI-Powered Digital Twin Platforms for Next-Generation Structural Health Monitoring: From Concept to Intelligent Decision-Making
Toheeb Abbey Animashaun, Omolayo Sunday, Emmanuel Ogunleye, Ogonna Kizzito Agbahiwe, Oladele Nicholas Afolayan, Oghenetega A. Okpoko, Amienye Babatunde Omo Enabulele, Benjamin Osaze Enobakhare, Ebuka Stephen Ifionu
Journal of Engineering Research and Reports · pp. 12–37 · Published 22 Sep 2025
10.9734/jerr/2025/v27i101652Abstract
Combining the Artificial Intelligence (AI) together with Digital Twin (DT) technologies is redefining Structural Health Monitoring (SHM) to shift maintenance of the infrastructure to proactive across aspects. The design of a digital twin framework that incorporates a cadre of machine learning and neural-network-based tools into a predictive maintenance approach that can continuously sense, learn, and respond through closed-loop feedback. The framework is based on sensor networks using the IoT, sophisticated models of AI, and immersive visualizations and can provide real-time knowledge about the structural state. Field applications within civil infrastructure, aerospace and renewable energy have proven it to be effective at predicting remaining useful life and limit downtime, improve safety and minimize costs of operations. The results indicate the potential of AI-powered digital twins to establish self-sustaining SHM systems and lead to more resistant and intelligent infrastructure.
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