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Hellenborn, B., Eliasson, O., Yitmen, I. & Sadri, H. (2024). Asset information requirements for blockchain-based digital twins: a data-driven predictive analytics perspective. Smart and Sustainable Built Environment, 13(1), 22-41
Open this publication in new window or tab >>Asset information requirements for blockchain-based digital twins: a data-driven predictive analytics perspective
2024 (English)In: Smart and Sustainable Built Environment, ISSN 2046-6099, E-ISSN 2046-6102, Vol. 13, no 1, p. 22-41Article in journal (Refereed) Published
Abstract [en]

Purpose

The purpose of this study is to identify the key data categories and characteristics defined by asset information requirements (AIR) and how this affects the development and maintenance of an asset information model (AIM) for a blockchain-based digital twin (DT).

Design/methodology/approach

A mixed-method approach involving qualitative and quantitative analysis was used to gather empirical data through semistructured interviews and a digital questionnaire survey with an emphasis on AIR for blockchain-based DTs from a data-driven predictive analytics perspective.

Findings

Based on the analysis of results three key data categories were identified, core data, static operation and maintenance (OM) data, and dynamic OM data, along with the data characteristics required to perform data-driven predictive analytics through artificial intelligence (AI) in a blockchain-based DT platform. The findings also include how the creation and maintenance of an AIM is affected in this context.

Practical implications

The key data categories and characteristics specified through AIR to support predictive data-driven analytics through AI in a blockchain-based DT will contribute to the development and maintenance of an AIM.

Originality/value

The research explores the process of defining, delivering and maintaining the AIM and the potential use of blockchain technology (BCT) as a facilitator for data trust, integrity and security.

Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2024
Keywords
Asset management, Asset information requirements, Asset information model, Digital twins, Blockchain, Artificial intelligence and machine learning
National Category
Construction Management Information Systems
Identifiers
urn:nbn:se:hj:diva-59436 (URN)10.1108/SASBE-08-2022-0183 (DOI)000913702700001 ()2-s2.0-85146335947 (Scopus ID)HOA;;857743 (Local ID)HOA;;857743 (Archive number)HOA;;857743 (OAI)
Note

This study is funded by Smart Built Environment (Sweden) (Grant No. 2021-00296). The authors would like to acknowledge the project industrial partners Pythagoras AB and Plan B BIM AB for their contribution to the project.

Available from: 2023-01-25 Created: 2023-01-25 Last updated: 2024-01-15Bibliographically approved
Tavakoli, P., Yitmen, I., Sadri, H. & Taheri, A. (2024). Blockchain-based digital twin data provenance for predictive asset management in building facilities. Smart and Sustainable Built Environment, 13(1), 4-21
Open this publication in new window or tab >>Blockchain-based digital twin data provenance for predictive asset management in building facilities
2024 (English)In: Smart and Sustainable Built Environment, ISSN 2046-6099, E-ISSN 2046-6102, Vol. 13, no 1, p. 4-21Article in journal (Refereed) Published
Abstract [en]

Purpose: The purpose of this study is to focus on structured data provision and asset information model maintenance and develop a data provenance model on a blockchain-based digital twin smart and sustainable built environment (DT) for predictive asset management (PAM) in building facilities.

Design/methodology/approach: Qualitative research data were collected through a comprehensive scoping review of secondary sources. Additionally, primary data were gathered through interviews with industry specialists. The analysis of the data served as the basis for developing blockchain-based DT data provenance models and scenarios. A case study involving a conference room in an office building in Stockholm was conducted to assess the proposed data provenance model. The implementation utilized the Remix Ethereum platform and Sepolia testnet.

Findings: Based on the analysis of results, a data provenance model on blockchain-based DT which ensures the reliability and trustworthiness of data used in PAM processes was developed. This was achieved by providing a transparent and immutable record of data origin, ownership and lineage.

Practical implications: The proposed model enables decentralized applications (DApps) to publish real-time data obtained from dynamic operations and maintenance processes, enhancing the reliability and effectiveness of data for PAM.

Originality/value: The research presents a data provenance model on a blockchain-based DT, specifically tailored to PAM in building facilities. The proposed model enhances decision-making processes related to PAM by ensuring data reliability and trustworthiness and providing valuable insights for specialists and stakeholders interested in the application of blockchain technology in asset management and data provenance.

Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2024
Keywords
Asset information model, Asset information requirements, Blockchain, Data provenance, Digital twins, Predictive asset management, Asset management, Decision making, Information theory, Office buildings, Reliability analysis, Asset information requirement, Assets management, Block-chain, In-buildings, Information Modeling, Information requirement, Provenance models
National Category
Construction Management
Identifiers
urn:nbn:se:hj:diva-62988 (URN)10.1108/SASBE-07-2023-0169 (DOI)001102241000001 ()2-s2.0-85176915900 (Scopus ID)HOA;;918800 (Local ID)HOA;;918800 (Archive number)HOA;;918800 (OAI)
Note

This study is supported by Smart Built Environment (Sweden) (Grant No. 2021-00296). The authors would like to acknowledge the project industrial partners Pythagoras AB and Plan B BIM AB for their contribution to the project.

Available from: 2023-12-04 Created: 2023-12-04 Last updated: 2024-01-15Bibliographically approved
Sadri, H., Yitmen, I., Tagliabue, L. C. & Westphal, F. (2023). A Conceptual Framework For Blockchain and Ai-Driven Digital Twins For Predictive Operation and Maintenance. In: Proceedings of the 2023 European Conference on Computing in Construction and the 40th International CIB W78 Conference: . Paper presented at 2023 European Conference on Computing in Construction and Summer School 2023 CIB W78 40th International Conference and Charles M. Eastman PhD Award Heraklion 10 July 2023 through 12 July 2023. European Council on Computing in Construction (EC3)
Open this publication in new window or tab >>A Conceptual Framework For Blockchain and Ai-Driven Digital Twins For Predictive Operation and Maintenance
2023 (English)In: Proceedings of the 2023 European Conference on Computing in Construction and the 40th International CIB W78 Conference, European Council on Computing in Construction (EC3) , 2023Conference paper, Published paper (Refereed)
Abstract [en]

Digital Twins (DTs), enriched with Artificial Intelligence (AI) and Blockchain technology, promise a revolutionary breakthrough in smart asset management and predictive maintenance in the built environment. This study aims to portray a conceptual framework of Blockchain and AIbased DTs and outline its key characteristics, requirements, and system architecture by composing a functional model using IDEF0. Such an approach is expected to enhance predictive maintenance in building facilities, simplify the management and operation of smart built environments, and ultimately deliver valuable outcomes for facility operators, real estate practitioners, and end-users. 

Place, publisher, year, edition, pages
European Council on Computing in Construction (EC3), 2023
National Category
Construction Management
Identifiers
urn:nbn:se:hj:diva-62964 (URN)10.35490/EC3.2023.219 (DOI)2-s2.0-85177229586 (Scopus ID)
Conference
2023 European Conference on Computing in Construction and Summer School 2023 CIB W78 40th International Conference and Charles M. Eastman PhD Award Heraklion 10 July 2023 through 12 July 2023
Available from: 2023-11-29 Created: 2023-11-29 Last updated: 2024-02-09Bibliographically approved
Yitmen, I., Sadri, H. & Taheri, A. (2023). AI-Driven Digital Twins for Predictive Operation and Maintenance in Building Facilities. In: I. Yitmen, S. Alizadehsalehi (Ed.), Cognitive Digital Twins for Smart Lifecycle Management of Built Environment and Infrastructure Challenges, Opportunities and Practices: (pp. 65-78). CRC Press
Open this publication in new window or tab >>AI-Driven Digital Twins for Predictive Operation and Maintenance in Building Facilities
2023 (English)In: Cognitive Digital Twins for Smart Lifecycle Management of Built Environment and Infrastructure Challenges, Opportunities and Practices / [ed] I. Yitmen, S. Alizadehsalehi, CRC Press , 2023, p. 65-78Chapter in book (Refereed)
Abstract [en]

By virtue of Artificial Intelligence (AI) and Machine Learning (ML) techniques, integrated with building Digital Twins (DTs), predictive maintenance plays a highly beneficial role in smart asset operation in the built environment. Therefore, this chapter attempts to portray a conceptual framework of AI-based DTs, illustrating how real-time data can be gathered from the building facilities and processed to optimize their operation and maintenance plan. To this end, different enabling and supporting technologies as well as the constituting component of the framework are elaborated. Putting such a framework into practice can enhance the maintenance activities in various types of building facilities such as mechanical and electrical installations, lighting, security, and HVAC systems in order for indoor quality improvement, energy optimization, and cost reduction.

Place, publisher, year, edition, pages
CRC Press, 2023
National Category
Construction Management
Identifiers
urn:nbn:se:hj:diva-62147 (URN)10.1201/9781003230199-4 (DOI)2-s2.0-85163543557 (Scopus ID)9781003230199 (ISBN)
Available from: 2023-08-16 Created: 2023-08-16 Last updated: 2024-02-09Bibliographically approved
Kor, M., Yitmen, I. & Alizadehsalehi, S. (2023). An investigation for integration of deep learning and digital twins towards Construction 4.0. Smart and Sustainable Built Environment, 12(3), 461-487
Open this publication in new window or tab >>An investigation for integration of deep learning and digital twins towards Construction 4.0
2023 (English)In: Smart and Sustainable Built Environment, ISSN 2046-6099, E-ISSN 2046-6102, Vol. 12, no 3, p. 461-487Article in journal (Refereed) Published
Abstract [en]

Purpose

The purpose of this paper is to investigate the potential integration of deep learning (DL) and digital twins (DT), referred to as (DDT), to facilitate Construction 4.0 through an exploratory analysis.

Design/methodology/approach

A mixed approach involving qualitative and quantitative analysis was applied to collect data from global industry experts via interviews, focus groups and a questionnaire survey, with an emphasis on the practicality and interoperability of DDT with decision-support capabilities for process optimization.

Findings

Based on the analysis of results, a conceptual model of the framework has been developed. The research findings validate that DL integrated DT model facilitating Construction 4.0 will incorporate cognitive abilities to detect complex and unpredictable actions and reasoning about dynamic process optimization strategies to support decision-making.

Practical implications

The DL integrated DT model will establish an interoperable functionality and develop typologies of models described for autonomous real-time interpretation and decision-making support of complex building systems development based on cognitive capabilities of DT.

Originality/value

The research explores how the technologies work collaboratively to integrate data from different environments in real-time through the interplay of the optimization and simulation during planning and construction. The framework model is a step for the next level of DT involving process automation and control towards Construction 4.0 to be implemented for different phases of the project lifecycle (design–planning–construction).

Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2023
Keywords
Digital twins, Deep learning, Artificial intelligence, Internet of things, Simulation, Optimization, Construction 4.0
National Category
Construction Management Computer Sciences
Identifiers
urn:nbn:se:hj:diva-55976 (URN)10.1108/SASBE-08-2021-0148 (DOI)000763531500001 ()2-s2.0-85125955106 (Scopus ID)HOA;intsam;798536 (Local ID)HOA;intsam;798536 (Archive number)HOA;intsam;798536 (OAI)
Available from: 2022-03-03 Created: 2022-03-03 Last updated: 2023-04-21Bibliographically approved
Almusaed, A. & Yitmen, I. (2023). Architectural Reply for Smart Building Design Concepts Based on Artificial Intelligence Simulation Models and Digital Twins. Sustainability, 15(6), Article ID 4955.
Open this publication in new window or tab >>Architectural Reply for Smart Building Design Concepts Based on Artificial Intelligence Simulation Models and Digital Twins
2023 (English)In: Sustainability, E-ISSN 2071-1050, Vol. 15, no 6, article id 4955Article in journal (Refereed) Published
Abstract [en]

Artificial Intelligence (AI) simulation models and digital twins (DT) are used in designingand treating the activities, layout, and functions for the new generation of buildings to enhanceuser experience and optimize building performance. These models use data about a building’s use,configuration, functions, and environment to simulate different design options and predict theireffects on house function efficiency, comfort, and safety. On the one hand, AI algorithms are usedto analyze this data and find patterns and trends that can guide the design process. On the otherhand, DTs are digital recreations of actual structures that can replicate building performance in realtime. These models would evaluate alternative design options, the performance of the building, andways to improve user comfort and building efficiency. This study examined the important role ofintelligent building design aspects, such as activities using multi-layout and the creation of particularfunctions based on AI simulation models, in developing DT-based smart building systems. Theempirical data came from a study of architecture and engineering firms throughout the globe usinga CSAQ (computer-administered, self-completed survey). For this purpose, the study employedstructural equation modeling (SEM) to examine the hypotheses and build the relationship model. Theresearch verifies the relevance of AI-based simulation models supporting the creation of intelligentbuilding design features (activities, layout, functionalities), enabling the construction of DT-basedsmart building systems. Furthermore, this study highlights the need for further exploration ofAI-based simulation models’ role and integration with DT in smart building design.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
smart building design; artificial intelligence; AI simulations models; digital twins
National Category
Building Technologies Computer Sciences
Identifiers
urn:nbn:se:hj:diva-59987 (URN)10.3390/su15064955 (DOI)000958891200001 ()2-s2.0-85151519691 (Scopus ID)GOA;;1743137 (Local ID)GOA;;1743137 (Archive number)GOA;;1743137 (OAI)
Available from: 2023-03-14 Created: 2023-03-14 Last updated: 2023-04-17Bibliographically approved
Almusaed, A., Almssad, A., Alasadi, A., Yitmen, I. & Al-Samaraee, S. (2023). Assessing the Role and Efficiency of Thermal Insulation by the "BIO-GREEN PANEL" in Enhancing Sustainability in a Built Environment. Sustainability, 15(13), Article ID 10418.
Open this publication in new window or tab >>Assessing the Role and Efficiency of Thermal Insulation by the "BIO-GREEN PANEL" in Enhancing Sustainability in a Built Environment
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2023 (English)In: Sustainability, E-ISSN 2071-1050, Vol. 15, no 13, article id 10418Article in journal (Refereed) Published
Abstract [en]

The pressing concern of climate change and the imperative to mitigate CO2 emissions have significantly influenced the selection of outdoor plant species. Consequently, evaluating CO2's environmental effects on plants has become integral to the decision-making process. Notably, reducing greenhouse gas (GHG) emissions from buildings is significant in tackling the consequences of climate change and addressing energy deficiencies. This article presents a novel approach by introducing plant panels as an integral component in future building designs, epitomizing the next generation of sustainable structures and offering a new and sustainable building solution. The integration of environmentally friendly building materials enhances buildings' indoor environments. Consequently, it becomes crucial to analyze manufacturing processes in order to reduce energy consumption, minimize waste generation, and incorporate green technologies. In this context, experimentation was conducted on six distinct plant species, revealing that the energy-saving potential of different plant types on buildings varies significantly. This finding contributes to the economy's improvement and fosters enhanced health-related and environmental responsibility. The proposed plant panels harmonize various building components and embody a strategic approach to promote health and well-being through bio-innovation. Furthermore, this innovative solution seeks to provide a sustainable alternative by addressing the challenges of unsustainable practices, outdated standards, limited implementation of new technologies, and excessive administrative barriers in the construction industry. The obtained outcomes will provide stakeholders within the building sector with pertinent data concerning performance and durability. Furthermore, these results will enable producers to acquire essential information, facilitating product improvement.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
climate change, biophilic design, bio-basis product, passport materials, built environment, thermal insulations
National Category
Construction Management
Identifiers
urn:nbn:se:hj:diva-62207 (URN)10.3390/su151310418 (DOI)001028210800001 ()2-s2.0-85165064791 (Scopus ID)GOA;intsam;897653 (Local ID)GOA;intsam;897653 (Archive number)GOA;intsam;897653 (OAI)
Available from: 2023-08-18 Created: 2023-08-18 Last updated: 2023-08-18Bibliographically approved
Papadonikolaki, E., Tezel, A., Yitmen, I. & Hilletofth, P. (2023). Blockchain innovation ecosystems orchestration in construction. Industrial management & data systems, 123(2), 672-694
Open this publication in new window or tab >>Blockchain innovation ecosystems orchestration in construction
2023 (English)In: Industrial management & data systems, ISSN 0263-5577, E-ISSN 1758-5783, Vol. 123, no 2, p. 672-694Article in journal (Refereed) Published
Abstract [en]

Purpose: Rapid advancements in blockchain technology transform various sectors, attracting the attention of industrialists, practitioners, policymakers and academics, and profoundly affect construction businesses through smart contracts and crypto-economics. This paper explores the blockchain innovation ecosystem in construction.

Design/methodology/approach: Through a qualitative study of 23 diverse interviewees, the study explores how open or closed the blockchain innovation ecosystem in construction is and who its emerging orchestrators are.

Findings: The data showed that construction aims towards an open innovation blockchain ecosystem, although there are elements of hybridisation and closedness, each system pointing out to different orchestrators.

Practical implications: The study has implications for governments and large companies in construction, showing that open innovation initiatives need to be encouraged by policymakers through rules, regulations and government-sponsored demonstrator projects.

Social implications: The data showed that there is lack of readiness for business model change to support open innovation blockchain ecosystems in construction.

Originality/value: This is the first study applying the open innovation theory in the construction industry and sheds light into the phenomenon of blockchain, suggesting routes for further democratisation of the technology for policymakers and practitioners.

Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2023
Keywords
Architectural design, Blockchain, Construction industry, Block-chain, Building information modeling, Building Information Modelling, Construction business, Design/methodology/approach, Ecosystem orchestrations, Innovation, Open innovation, Policy makers, Qualitative study, Ecosystems, Building information modelling (BIM), Ecosystem, Ecosystem orchestration
National Category
Construction Management
Identifiers
urn:nbn:se:hj:diva-59080 (URN)10.1108/IMDS-03-2022-0134 (DOI)000889682000001 ()2-s2.0-85142713134 (Scopus ID)HOA;intsam;1716077 (Local ID)HOA;intsam;1716077 (Archive number)HOA;intsam;1716077 (OAI)
Available from: 2022-12-05 Created: 2022-12-05 Last updated: 2023-08-30Bibliographically approved
Yitmen, I. (Ed.). (2023). Cognitive Digital Twins for Smart Lifecycle Management of Built Environment and Infrastructure: Challenges, Opportunities and Practices. Boca Raton: CRC Press
Open this publication in new window or tab >>Cognitive Digital Twins for Smart Lifecycle Management of Built Environment and Infrastructure: Challenges, Opportunities and Practices
2023 (English)Collection (editor) (Refereed)
Abstract [en]

This book provides knowledge into Cognitive Digital Twins for smart lifecycle management of built environment and infrastructure focusing on challenges and opportunities. It focuses on the challenges and opportunities of data-driven cognitive systems by integrating the heterogeneous data from multiple resources that can easily be used in a machine learning model and adjust the algorithms. It comprises Digital Twins incorporating cognitive features that will enable sensing complex and unpredicted behavior and reason about dynamic strategies for process optimization to support decision-making in lifecycle management of the built environment and infrastructure. The book introduces the Knowledge Graph (KG)-centric framework for Cognitive Digital Twins involving process modeling and simulation, ontology-based Knowledge Graph, analytics for process optimizations, and interfaces for data operability. It offers contributions of Cognitive Digital Twins for the integration of IoT, Big data, AI, smart sensors, machine learning and communication technologies, all connected to a novel paradigm of self-learning hybrid models with proactive cognitive capabilities. The book presents the topologies of models described for autonomous real time interpretation and decision-making support of complex system development based on Cognitive Digital Twins with applications in critical domains such as maintenance of complex engineering assets in built environment and infrastructure. It offers the essential material to enlighten pertinent research communities of the state-of-the-art research and the latest development in the area of Cognitive Digital Twins, as well as a valuable reference for planners, designers, developers, and ICT experts who are working towards the development and implementation of autonomous Cognitive IoT based on big data analytics and context–aware computing.

Place, publisher, year, edition, pages
Boca Raton: CRC Press, 2023. p. 240
National Category
Construction Management
Identifiers
urn:nbn:se:hj:diva-62148 (URN)10.1201/9781003230199 (DOI)2-s2.0-85163533694 (Scopus ID)9781003230199 (ISBN)
Available from: 2023-08-16 Created: 2023-08-16 Last updated: 2024-02-09Bibliographically approved
Lindholm, J., Johansson, P. & Yitmen, I. (2023). Collaborative digital platform for integrated design and production planning and control: a literature review. In: Proceedings of the 2023 European Conference on Computing in Construction and the 40th International CIB W78 Conference: . Paper presented at 2023 European Conference on Computing in Construction and the 40th International CIB W78 Conference, Crete, Greece, 10-12 July, 2023. European Council on Computing in Construction
Open this publication in new window or tab >>Collaborative digital platform for integrated design and production planning and control: a literature review
2023 (English)In: Proceedings of the 2023 European Conference on Computing in Construction and the 40th International CIB W78 Conference, European Council on Computing in Construction , 2023Conference paper, Published paper (Refereed)
Abstract [en]

There is a lack of a cohesive, integrated approach to design and production planning and control, where real-time data can be captured to support various management functions in an interconnected autonomous platform for collaboration. This paper presents a systematic literature review on collaborative digital platforms for BIM-based projects to facilitate synchronous updates of workflows based on real-time project data. The results show that a combined model, including Knowledge Graphs, Common Data Environments, and Digital Twins have potential to support a fully integrated, automated system. Future studies should therefore investigate how to connect these technologies together in an integrated project platform.

Place, publisher, year, edition, pages
European Council on Computing in Construction, 2023
Keywords
Design and production planning and control, Collaborative Digital Platform, Knowledge Graph, Common Data Environments, Digital Twin
National Category
Construction Management
Identifiers
urn:nbn:se:hj:diva-62389 (URN)10.35490/EC3.2023.203 (DOI)2-s2.0-85177185052 (Scopus ID)978-0-701702-73-1 (ISBN)
Conference
2023 European Conference on Computing in Construction and the 40th International CIB W78 Conference, Crete, Greece, 10-12 July, 2023
Available from: 2023-09-05 Created: 2023-09-05 Last updated: 2023-11-29Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-4288-9904

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