Digital twin engineering can significantly enhance predictive maintenance by continuously monitoring the condition of assets in real-time. Sensors embedded in physical assets collect performance data, which is reflected in the digital twin. Machine learning algorithms analyze these data to detect patterns and predict when an asset will require maintenance or is likely to fail. This predictive capability helps avoid downtime, optimize asset lifespan, and reduce maintenance costs, thereby improving the overall efficiency of construction projects.
