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.

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.

The swift evolution of building management systems necessitates the integration of advanced digital technologies to enhance operational efficiency, data security, and predictive maintenance. This thesis explores the potential and implementation challenges of integrating advanced technologies, namely, Blockchain, Digital Twins (DTs), Artificial Intelligence (AI), and the Internet of Things (IoT) within the smart built environment. Through a systematic review of current literature and empirical analysis, this research presents a comprehensive framework for these disruptive technologies' synergistic application, focusing on their impact on Asset Lifecycle Management (ALM).

Key findings demonstrate that Blockchain's immutable and transparent data handling capabilities significantly enhance the reliability and trustworthiness of DT data. The integration of AI with DTs facilitates predictive maintenance and optimization of building operations by providing real-time data analytics and automated decision-making processes. However, the study acknowledges substantial barriers, including the compatibility issues and the fragmented implementation of these technologies in the operation and maintenance (O&M) phase of building facilities throughout ALM.

To address these challenges, a conceptual framework and platform prototype were developed, showcasing practical solutions for integrating these technologies into Building Management Systems (BMS). The proposed framework emphasizes real-time data tracking, secure data storage, and automated maintenance task execution, leading to increased operational efficiency and cost savings.

This research contributes to the body of knowledge by providing a structured approach to integrating advanced technologies into BMS, highlighting the practical implications and sustainability benefits. The findings underscore the importance of stakeholder engagement and the need for continuous technological adaptation to realize the full potential of smart assetmanagement in the built environment.

Den snabba utvecklingen av byggnadsförvaltningssystem kräver integration av avancerade digitala teknologier för att förbättra driftseffektivitet, datasäkerhet och prediktivt underhåll. Denna avhandling utforskar potentialen och implementeringsutmaningarna med att integrera avancerade teknologier, nämligen Blockchain, Digitala tvillingar (DTs), Artificiell Intelligens (AI) och Internet of Things (IoT) inom den smarta byggda miljön. Genom en systematisk översikt av aktuell litteratur och empirisk analys presenterar denna forskning ett omfattande ramverk för synergistisk tillämpning av dessa störande teknologier, med fokus på deras inverkan på Asset Lifecycle Management (ALM).

Huvudfynden visar att Blockchains oföränderliga och transparenta datahantering avsevärt förbättrar tillförlitligheten och trovärdigheten hos DT-data. Integrationen av AI med DTs underlättar prediktivt underhåll och optimering av byggnadsoperationer genom att tillhandahålla realtidsdataanalys och automatiserade beslutsprocesser. Studien erkänner dock betydande hinder, inklusive kompatibilitetsproblem och fragmenterad implementering av dessa teknologier i drift- och underhållsfasen (O&M) av byggnadsanläggningar under ALM.

För att ta itu med dessa utmaningar utvecklades ett konceptuellt ramverk och en prototypplattform som visar praktiska lösningar för att integrera dessa teknologier i fastighetsautomationssystem (BMS). Det föreslagna ramverket betonar spårning av data i realtid, säker datalagring och automatiserad utförande av underhållsuppgifter, vilket leder till ökad driftseffektivitet och kostnadsbesparingar.

Denna forskning bidrar till kunskapsbasen genom att tillhandahålla ett strukturerat tillvägagångssätt för att integrera avancerade teknologier i BMS, med fokus på de praktiska implikationerna och hållbarhetsfördelarna. Resultaten understryker vikten av intressentengagemang och behovet av kontinuerlig teknologianpassning för att realisera hela potentialen av smarttillgångshantering i den byggda miljön.

 This study reviews the recently conducted case studies to explore the innovative integration of Artificial Intelligence (AI) and Machine Learning (ML) in the domain of building facility management and predictive maintenance. It systematically examines recent developments and applications of advanced computational methods, emphasizing their role in enhancing asset management accuracy, energy efficiency, and occupant comfort. The study investigates the implementation of various AI and ML techniques, such as regression methods, Artificial Neural Networks (ANNs), and deep learning models, demonstrating their utility in asset management. It also discusses the synergistic use of ML with domain-specific technologies such as Geographic Building Information Modeling (BIM), Information Systems (GIS), and Digital Twin (DT) technologies. Through a critical analysis of current trends and methodologies, the paper highlights the importance of algorithm selection based on data attributes and operational challenges in deploying sophisticated AI models. The findings underscore the transformative potential of AI and ML in facility management, offering insights into future research directions and the development of more effective, data-driven management strategies.  

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. 

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.

Purpose: The purpose of this research is to develop a framework of an ontology-based Asset Information Model (AIM) for a Digital Twin (DT) platform and enhance predictive maintenance practices in building facilities that could enable proactive and data-driven decision-making during the Operation and Maintenance (O&M) process. Design/methodology/approach: A scoping literature review was accomplished to establish the theoretical foundation for the current investigation. A study on developing an ontology-based AIM for predictive maintenance in building facilities was conducted. Semi-structured interviews were conducted with industry professionals to gather qualitative data for ontology-based AIM framework validation and insights. Findings: The research findings indicate that while the development of ontology faced challenges in defining missing entities and relations in the context of predictive maintenance, insights gained from the interviews enabled the establishment of a comprehensive framework for ontology-based AIM adoption in the Facility Management (FM) sector. Practical implications: The proposed ontology-based AIM has the potential to enable proactive and data-driven decision-making during the process, optimizing predictive maintenance practices and ultimately enhancing energy efficiency and sustainability in the building industry. Originality/value: The research contributes to a practical guide for ontology development processes and presents a framework of an Ontology-based AIM for a Digital Twin platform.

Modular production has been recognized as a pivotal approach for enhancing productivity and cost reduction within the industrialized building industry. In the pursuit of further optimization of production processes, the concept of cognitive modular production (CMP) has been proposed, aiming to integrate digital twins (DTs), artificial intelligence (AI), and Internet of Things (IoT) technologies into modular production systems. This fusion would imbue these systems with perception and decision-making capabilities, enabling autonomous operations. However, the efficacy of this approach critically hinges upon the ability to comprehend the production process and its variations, as well as the utilization of IoT and cognitive functionalities. Knowledge graphs (KGs) represent a type of graph database that organizes data into interconnected nodes (entities) and edges (relationships), thereby providing a visual and intuitive representation of intricate systems. This study seeks to investigate the potential fusion of KGs into CMP to bolster decision-making processes on the production line. Empirical data were collected through a computerized self-administered questionnaire (CSAQ) survey, with a specific emphasis on exploring the potential benefits of incorporating KGs into CMP. The quantitative analysis findings underscore the effectiveness of integrating KGs into CMP, particularly through the utilization of visual representations that depict the relationships between diverse components and subprocesses within a virtual environment. This fusion facilitates the real-time monitoring and control of the physical production process. By harnessing the power of KGs, CMP can attain a comprehensive understanding of the manufacturing process, thereby supporting interoperability and decision-making capabilities within modular production systems in the industrialized building industry.

The integration of blockchain and digital twins (DT) for better building-lifecycle data management has recently received much attention from researchers in the field. In this respect, the adoption of enabling technologies such as artificial intelligence (AI) and machine learning (ML), the Internet of Things (IoT), cloud and edge computing, Big Data analytics, etc., has also been investigated in an abundance of studies. The present review inspects the recent studies to shed light on the foremost among those enabling technologies and their scope, challenges, and integration potential. To this end, 86 scientific papers, recognized and retrieved from the Scopus and Web of Science databases, were reviewed and a thorough bibliometric analysis was performed on them. The obtained results demonstrate the nascency of the research in this field and the necessity of further implementation of practical methods to discover and prove the real potential of these technologies and their fusion. It was also found that the integration of these technologies can be beneficial for addressing the implementation challenges they face individually. In the end, an abstract descriptive model is presented to provide a better understanding of how the technologies can become integrated into a unified system for smartening the built environment.

Regulatory uncertainty is typically the inevitable consequence of introducing new regulations or even changing the old ones by the state agencies that interacts with the normative structures in the society. This study seeks to shed light on this relational impact in the Swedish construction industry by examining the managers' impression of the recently introduced Climate Declaration act by Boverket. Based on the results of a survey of the industry executives' opinions, it was established that the desired impacts of the regulative measures will require application of binding levers, whether as incentives or deterrents, the lack of which is evident in Boverket's Climate Declaration act. The main notion of the respondents was that the ultimate outcome of such regulations should be to lead the industry towards the selection of products and processes that produce less carbon dioxide over the whole life cycle and actualize a circular economy. The findings also acknowledge that the effectiveness of the Climate Declaration act will appear in the long run and this convention needs to be reviewed and amended over several stages to result in the expected payoffs.