Articles

Digital Intelligence in Building Lifecycle Management

What is it: A new academic study published in MDPI Sustainability explores a digital twin and genetic algorithm (GA)-based approach for optimizing facade maintenance in commercial buildings. Developed by engineering faculty across Chinese institutions, the research presents a multi-objective framework integrating building lifecycle data with evolutionary algorithms to improve long-term maintenance planning. The method enables cost and performance optimization, balancing trade-offs between safety, aesthetics, and lifecycle value.

The authors argue that digital twin integration—when combined with GA modeling—offers a breakthrough in automating complex decisions in building operations. Using simulated aging models and data from Chinese commercial properties, the study demonstrates measurable gains in maintenance scheduling efficiency and cost-effectiveness.

This research aligns closely with the Building Lifecycle Management (BLM) framework, which advocates for data-driven, lifecycle-integrated decision-making across design, construction, and operations.

Read the full paper here

Stakeholder Audience: Facility Operations, Corporate-Institutional Owners, Technology Providers-Integrators, Building Owners, AEC, Sustainability-Resiliency, Service Providers-Consultants, Organizational Leadership

Inform or Action: Informational

#BLMI #IFMA #Autodesk #DigitalTwins #GeneticAlgorithms #BuildingMaintenance #LifecycleOptimization #SmartBuildings #Sustainability #MDPI