AI-Powered Digital Twin Platforms for Next-Generation Structural Health Monitoring: From Concept to Intelligent Decision-Making
Toheeb Abbey Animashaun
*
Department of Civil Engineering, Obafemi Awolowo University, Ile – Ife, Nigeria.
Omolayo Sunday
Department of Natural Science, Kwara State University, Nigeria.
Emmanuel Ogunleye
Quantity Surveying department, The Federal Polytechnic, Ado Ekiti, Nigeria.
Ogonna Kizzito Agbahiwe
Department of Civil engineering, Chukwuemeka Odumegwu Ojukwu University, Uli, Anambra State, Nigeria.
Oladele Nicholas Afolayan
Vascular and Diabetics Team, Salisbury NHS Trust Fund, Salisbury, United Kingdom.
Oghenetega A. Okpoko
Engineering Standards and Applications, ComEd, IL, United States of America.
Amienye Babatunde Omo Enabulele
MSc Project Management, Project Management Professional (PMP), College of Business, Missouri State University, Springfield, Missouri, United States.
Benjamin Osaze Enobakhare
Project Management Professional (PMP), COREN, Nigeria and Peterbilt Motors, Denton, Texas, United States of America.
Ebuka Stephen Ifionu
Department of Civil Engineering, Chukwuemeka Odumegwu Ojukwu University, Uli, Anambra State, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
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.
Keywords: Artificial Intelligence, digital twin, infrastructure resilience, predictive maintenance, structural health monitoring