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The concept of Construction 5.0 has emerged as the next frontier in construction practices and is characterized by the integration of advanced technologies with human-centered approaches, sustainable practices and resilience considerations to build smart and future-ready buildings. However, there is currently a gap in research that provides a comprehensive perspective on the opportunities and challenges of facilitating Construction 5.0. This study aims to explore the opportunities and challenges in facilitating Construction 5.0 and its potential to implement smart, sustainable and resilient buildings.

The structural equation modeling (SEM) method was used to evaluate the research model and investigate the opportunities and challenges related to Construction 5.0 in its implementation for smart, sustainable and resilient buildings.

The results show that adopting human-centric technology, sustaining resilience and maintaining sustainability in the architecture, engineering and construction (AEC) industry seizes the opportunities to overcome the challenges for facilitating Construction 5.0 in the implementation of smart, sustainable and resilient buildings.

The AEC industry facilitating Construction 5.0 has the potential to redefine the future of construction, creating a built environment that is not only intelligent, sustainable and resilient but also deeply connected with the well-being and values of the communities it serves.

The research illuminates the path forward for a holistic understanding of Construction 5.0, envisioning a future where smart, sustainable and resilient buildings stand as testaments to the harmonious collaboration between humans and technology.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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).

The purpose of this research is to develop a generic framework of a digital twin (DT)-based automated construction progress monitoring through reality capture to extended reality (RC-to-XR).

IDEF0 data modeling method has been designed to establish an integration of reality capturing technologies by using BIM, DTs and XR for automated construction progress monitoring. Structural equation modeling (SEM) method has been used to test the proposed hypotheses and develop the skill model to examine the reliability, validity and contribution of the framework to understand the DRX model's effectiveness if implemented in real practice.

The research findings validate the positive impact and importance of utilizing technology integration in a logical framework such as DRX, which provides trustable, real-time, transparent and digital construction progress monitoring.

DRX system captures accurate, real-time and comprehensive data at construction stage, analyses data and information precisely and quickly, visualizes information and reports in a real scale environment, facilitates information flows and communication, learns from itself, historical data and accessible online data to predict future actions, provides semantic and digitalize construction information with analytical capabilities and optimizes decision-making process.

The research presents a framework of an automated construction progress monitoring system that integrates BIM, various reality capturing technologies, DT and XR technologies (VR, AR and MR), arraying the steps on how these technologies work collaboratively to create, capture, generate, analyze, manage and visualize construction progress data, information and reports.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

Each building project demands an integrated method for information and requirement management in its life cycle. The main purpose of this paper is to explore the major obstacles in integrated life cycle information management and recognize the potentials of CoClass as the new Swedish digital classification system to tackle them throughout asset life cycle.

The industry viewpoint toward the current status of asset information management considering ISO 19650-1 principles and the existing obstacles and the industry practitioners' ideas regarding CoClass capabilities and applicability were captured and analyzed. A total of 13 semistructured interviews were conducted with the AECO industry professionals to have an understanding of information requirement management. Then the results were analyzed qualitatively, using the NVivo 12 software. Different attributes of a component (heating panel) in a meeting room according to CoClass and data deviations throughout the asset life cycle were elaborated.

This study reveals some obstacles in information management process in seven categories in relation to: (1) the need to employ information exchange platforms as common data environments (CDEs) by all actors from early stages; (2) the communication issues caused by lack of utilizing common languages; (3) the costly and time-consuming implementation process; (4) the misunderstandings in terms of data communication between service providers and owners; (5) the definition and fulfillment of information requirements as well as keeping track of data deviations throughout asset life cycle; (6) the information update difficulty; and (7) the need for training practitioners dealing with new systems such as CoClass.

The research explores the major obstacles in information requirement management concerning the practical implementation of the new Swedish classification system, CoClass, supporting the asset life cycle.

Decisions taken during the early design of adaptive façades involving kinetic, active and responsive envelope for complex commercial buildings have a substantial effect on inclusive building functioning and the comfort level of inhabitants. This study aims to present the application of an analytic network process (ANP) model indicating the order of priority for high performance criteria that must be taken into account in the assessment of the performance of adaptive façade systems for complex commercial buildings.

The nominal group technique (NGT) stimulating and refining group judgments are used to find and categorize relevant high performance attributes of the adaptive façade systems and their relative pair-wise significance scores. An ANP model is applied to prioritize these high performance objectives and criteria for the adaptive façade systems.

Embodied energy and CO₂ emission, sustainability, energy saving, daylight and operation maintenance were as the most likely and crucial high performance criteria. The criteria and the weights presented in this study could be used as guidelines for evaluating the performance of adaptive façade systems for commercial buildings in planning and design phases.

This research primarily provides the required actions and evaluations for design managers in accomplishing a high performance adaptive façade system, with the support of an ANP method. Before beginning the adaptive façade system of a building design process, the design manager must determine the significance of each of these attributes as high performance primacies will affect the results all through the entire design process.

In this research, a relatively innovative, systematic and practical approach is proposed to sustain the decision-making procedure for evaluation of the high performance criteria of adaptive façade systems in complex commercial buildings.

The purpose of this paper is to investigate the practice of stakeholder engagement as a social network dynamics for stakeholder satisfaction and project success in the lifecycle of mega transport infrastructure projects (MTIPs).

Hypotheses indicating the positive relationships between stakeholders’ effective attributes, stakeholder engagement as social network dynamics and project success through stakeholders’ satisfaction have been developed. Based on a questionnaire survey and semi-structured interviews, responses have been gathered from the representative groups and organizations on their social network dynamics for their satisfaction and project success. A hypothesized structural equation model has been tested using AMOS statistical software package.

The analysis highlighted the engagement of the stakeholders within the strategic intents of the project with the public needs and expectations. The model depicts the processes of building social network models based on the capturing of the project’s data in relation to the stakeholders’ communication and satisfaction across the key issues for success in the lifecycle of MTIP.

The model is applicable on most MTIP with a diverse stakeholder base and the underlying complexity associated with the community participation and consultation processes. The model will also support wider stakeholder engagement in the planning of MTIP with optimal operationalization and service delivery from a community perspective.

The research involves an approach for rationalizing the stakeholder engagement policies of the MTIPs by providing an empirically grounded model simultaneously linking various aspects of stakeholder effective attributes, stakeholder engagement and their relationships to stakeholder satisfaction and project success in MTIPs.