Digital Twin-Enabled Smart Construction: Integrating BIM, IoT, and AI for Predictive Infrastructure Maintenance and Sustainability

Abstract

The increasing scale, complexity, and aging of infrastructure, coupled with heightened sustainability demands, have placed pressure
on traditional maintenance and asset management practices. Digital Twin (DT) technology—virtual replicas of physical assets updated
in real time—offers promise for transforming infrastructure maintenance from reactive to predictive, thereby improving performance,
reducing costs, and promoting environmental sustainability. This study explores how integration of Building Information Modeling
(BIM), Internet of Things (IoT) sensor networks, and Artificial Intelligence (AI) methods can underpin a DTenabled smart construction
system for predictive maintenance and sustainable infrastructure. The research develops a conceptual framework and implements a
pilot case study in a midsized transportation infrastructure (road bridge + drainage network) to evaluate performance. Key objectives
are to: (i) define the architecture for integrating BIM, IoT, and AI into a DT system; (ii) develop predictive models for remaining useful life
(RUL) of structural components; (iii) assess sustainability gains (energy, materials, emissions reduction) from optimized maintenance;
(iv) identify barriers and tradeoffs.
Methodologically, the study uses BIM models as baseline asbuilt digital representations, deploys IoT sensors for structural health
monitoring (strain, vibration, humidity, corrosion), streams data into a cloud/edge platform, and applies AI/ML techniques (e.g., LSTM,
anomaly detection, regression) to predict component deterioration and schedule maintenance proactively. The pilot yields reductions
in unplanned failures by ~4560%, maintenance costs by ~2030%, and operations downtime by ~35%, compared to benchmark
reactive maintenance. Sustainability metrics show material waste reduction of ~25%, energy use lowered by ~15%, and greenhousegas
emissions by ~10% over a simulated 5year horizon. Advantages observed include improved asset life, better resource allocation, and
decision support for infrastructure owners. Key disadvantages include high upfront costs, data quality/integration challenges, need for
specialized skills, and privacy/security concerns.
The results suggest that DTenabled systems can substantially improve predictive maintenance and sustainability outcomes for
infrastructure, particularly when BIM, IoT, and AI are well coordinated. For broader adoption, standardization of data models, advanced
interoperability, secure data handling, and costeffective deployment strategies are essential. Future work should focus on scaling to
larger systems, integrating lifecycle sustainability valuation, automating model updating, and leveraging federated learning for privacy.