Arbetet i "Integrering av Blockchain och Digital Tvilling för Smart Fastighetsförvaltning" har lett till utvecklingen av en blockchainaktiverad Digitala Tvillingar – plattformsprototyp, utformad för att frigöra den fulla potentialen hos smart Livscykelhantering för tillgångar inom ett digitalt ekosystem. Plattformen möjliggör säkra, transparenta, spårbara och informationsrika Digital Twin-tillämpningar samtidigt som den erbjuder avancerade funktioner för rapportering och beslutsstöd. Genom att kombinera Digital Twin-teknologi med ett distribuerat blockkedjebaserat system för informationslagring möjliggör den samverkande plattformen en omfattande integration, analys och hantering av tillgångsdata genom hela tillgångens livscykel.
Vi har pratat med projektledare Ibrahim Yitmen, Tekniska Högskolan i Jönköping.
What are the most important, or most interesting, results of the project?
– The project has developed a blockchain-enabled digital twin platform prototype that integrates several emerging technologies – Digital Twins, Blockchain, Artificial Intelligence, Machine Learning, and IoT – into a cohesive ecosystem for smart Asset Lifecycle Management (ALM).
Among the most notable results are:
A fully functional platform prototype that combines digital twin technology with a distributed blockchain-based record-keeping system. The platform enables secure, transparent, traceable, and information-rich asset data management throughout the entire lifecycle.
Implementation of smart contracts that automatically trigger predefined actions – such as payments, information transfer, procurement processes, or maintenance workflows – when specified conditions are met. This reduces manual intervention, improves workflow efficiency, and supports more reliable O&M processes.
An ontology-based Asset Information Model (AIM) for predictive maintenance, developed through interviews with industry experts. The ontology captures essential classes such as life expectancy, maintenance activities, intervals, costs, personnel, sensor data, and measurement units, enabling semantic interoperability and automated reasoning.
A user dashboard with six integrated modules that visualize historical and real-time data, indoor comfort, predictive analytics, ongoing maintenance activities, blockchain transactions, and sustainability indicators (Green Metrics).
Predictive analytics where AI algorithms estimate the probability of anomalies and generate early warnings, enabling proactive preventive maintenance measures before failures occur.
A data provenance framework that establishes a structured sequence of data collection, tagging, validation, processing, storage, and subsequent use for predictive purposes, with a hybrid architecture combining on-chain and off-chain storage.
Who do you see as the recipient of the results?
The results are aimed at several target groups within the built environment sector:
Facility managers and operations staff – who gain access to secure, real-time, and traceable information to optimise operations and maintenance.
Property owners and real estate companies – who can make better decisions regarding asset lifecycles and achieve sustainability goals through improved operational efficiency and reduced costs.
Service providers and contractors – who can automate workflows through smart contracts and ensure transparency in delivered services.
Municipalities and public organizations – who can use the platform to promote the adoption of digital technologies for sustainable and intelligent asset management.
Academia and researchers – who receive a scientific foundation for continued research on the integration of blockchain, digital twins, and AI in the built environment.
Tenants and end-users – who benefit from improved indoor environmental quality and increased transparency in building operations.
How do you hope the results will be taken forward?
The project has identified several important pathways for the results to achieve impact:
Large-scale validation – The platform needs to be tested in more buildings, over longer periods, and in different operational contexts to quantify improvements in maintenance accuracy, downtime, energy use, and cost savings.
Standardization of ontology-based AIM – The ontology should be expanded and aligned with established standards such as BrickSchema, RealEstateCore, IFC, and SAREF. Automated ontology generation using NLP and Large Language Models (LLMs) should be explored to reduce manual effort.
Improved AI/ML for predictive maintenance – Using larger and more diverse datasets, as well as comparisons of different algorithms. Explainable AI should be integrated so that facility managers understand the reasoning behind predicted failures.
Interoperability, cybersecurity, and privacy – Common protocols and standardized APIs are required to connect BIM, AIM, DT, IoT, BOMS, blockchain, and enterprise systems. Particular attention should be given to identity management, access control, and compliance with data protection requirements.
User-centred evaluation – Facility managers, owners, service providers, tenants, and municipalities should participate in structured usability evaluations and long-term pilot deployments. Business models, lifecycle costs, and organizational readiness must also be assessed.
If you were to run another project, what would it be about?
Based on the project's recommendations, a new project could focus on:
"Scalable and secure implementation of blockchain-enabled digital twins for property portfolios"
Such a project would address several of the identified knowledge gaps:
Scalability and performance – evaluating hybrid architectures combining blockchain, edge computing, and cloud services to handle large volumes of IoT data with low latency and high transaction capacity.
Standardized ontologies and knowledge graphs – automating the creation of semantically enriched Asset Information Models using NLP and Large Language Models, enabling intelligent and interoperable asset management at scale.
Cybersecurity and privacy – developing frameworks for identity management, access control, and privacy-preserving mechanisms in decentralized digital twin environments.
User acceptance and business models – conducting long-term pilot projects with multiple stakeholders to validate business value and organizational readiness.