Work package 5
Development of a Centralized Digital Platform
Starting Month: 13/48
Lead Beneficiary : BUILT CoLAB
Objectives: WP5 aims to develop a centralized digital platform that integrates BIM, databases, and digital tools to support the entire lifecycle management of construction projects. This platform will enhance resource utilization, sustainability, and facilitate the integration of predictive maintenance through digital twin technology, ensuring dynamic adaptation to changing conditions and enabling stakeholder engagement with advanced data management and analysis tools.
Description of Work: The following activities comprising various tasks will be completed to meet the needs of WP5:
Task 5.1. Design a centralized digital platform integrating BIM, databases and digital tools (Leader: BUILTCOLAB, PM=19.0M): The goal is to develop a comprehensive system that supports the lifecycle management of construction projects, from initial design through to deconstruction, ensuring optimal use of resources and enhanced sustainability. It involves architecting a platform that seamlessly combines BIM data with real-time insights provided by digital twins, allowing for more accurate simulations, predictive maintenance, and dynamic adaptation of construction projects to changing conditions. Role of Participants: ACU: Assisting in the creation and management of material banks within the platform. BUILTCOLAB: Provides knowledge in sustainable design and construction practices, helping to embed DfD principles into the BIM process. AIDIGITS: supports the implementation of AI and ML algorithms for data analysis and decision support. INFERSENCE: Ensures data integrity and security, particularly for cloud-based databases and material bank information.
Task 5.2. Data management and analysis tools (Leader AIDIGITS, PM=11.0M): This task is dedicated to the implementation of sophisticated data management and analysis tools within the centralized digital platform developed in Task 5.1. Considering the scalability of data storage, NoSQL Databases will be used for unstructured or semi structured data, standardized databases like MongoDB or Cassandra offer scalability and flexibility. Data Lakes will be implemented utilizing platforms like Apache Hadoop or Amazon S3 for storing vast amounts of raw data in its native format. For data processing and analysis, Apache Spark stands out as a versatile engine capable of managing both batch and real-time analytics, while Apache Kafka excels in constructing efficient real-time data pipelines, essential for handling diverse data streams. Predictive analytics and ML are powered by TensorFlow, which is instrumental in developing models to foresee project outcomes, complemented by Scikit-learn for a range of data analysis and modeling tasks. Visualization and reporting tools like Power BI transform complex data sets into interactive insights, with Grafana or Kibana providing detailed time-series data visualizations. Ensuring data security and compliance is paramount; thus, AES/RSA encryption safeguards data, and GDPR compliance tools like OneTrust ensure regulatory adherence. The platform's integration and interoperability are bolstered by API management platforms such as Apigee, and ETL tools like Talend streamline data transformation, ensuring a cohesive, secure, and insightful data ecosystem. Role of Participants: AIDIGITS: Contributes to the implementation of predictive analytics and machine learning algorithms. NAPSLAB: Will focus on the data visualization and reporting aspect. UCY: Will ensure the security and compliance of the data management system, implementing data encryption standards like AES or RSA and integrating GDPR compliance tools to adhere to data privacy regulations. BUILTCOLAB: Will be responsible for ensuring that the data management system is interoperable and can seamlessly integrate with other system components, using API management platforms and ETL tools like Talend or Informatica.
Task 5.3. Enhancement of predictive maintenance via digital twin integration (Leader UOA, PM=19.0M): Creating digital replicas of physical construction assets, will enable real-time monitoring, performance assessment, and predictive analytics, leading to informed decision-making and proactive maintenance strategies. The integration of BIM-based digital twins will facilitate the evaluation of various scenarios, including the impact of using recycled materials, ensuring their effective utilization without compromising structural integrity or performance. Algorithms will be developed to monitor the real-time condition of materials and components, using IoT sensor data integrated with digital twins. Techniques like time-series analysis or anomaly detection can be applied to identify patterns indicating the need for maintenance. Reliability engineering principles and models, such as Weibull Analysis or Reliability Centered Maintenance (RCM), within the digital twin framework to optimize maintenance schedules and ensure the durability of construction materials. Role of Participants: UOA: Implements the digital twin framework, ensuring its seamless integration with the centralized digital platform. JUST: Will contribute, enhancing the predictive capabilities of the digital twins by incorporating advanced simulation models that account for material characteristics and structural behaviours. NTUA: Will enhance the digital twins with AI-driven predictive analytics, capable of forecasting maintenance requirements and potential structural issues. INS: Will ensure the integrity and security of data flowing to and from the digital twins, employing blockchain technology for traceability and smart contracts for automated maintenance scheduling and compliance.
Task 5.4. Development of stakeholder engagement interfaces and material lifecycle tracking systems (Leader: UCY, PM=10.0M): Interfaces will be designed to provide easy access to information on material availability, specifications, and suitability for reuse, thereby supporting decision-making in sustainable construction practice, facilitating effective communication and collaboration among various stakeholders in the construction industry, including architects, engineers, contractors, and material suppliers. ML algorithms will improve the functionality of the interfaces enabling the intelligent matching of material supply with project requirements, optimizing the reuse and recycling of materials. Role of Participants: UCY: Leads the design and development of user-friendly interfaces for stakeholder engagement, ensuring ease of access to material lifecycle information and fostering collaboration across the construction supply chain. AIDIGITS: Contributes machine learning and data analytics expertise to enhance interface functionality, enabling intelligent material matching and lifecycle tracking to support sustainable construction practices.