In this study, we critically examine the potential of recycled construction materials, focusing on how these materials can significantly reduce greenhouse gas (GHG) emissions and energy usage in the construction sector. By adopting an integrated approach that combines Life Cycle Assessment (LCA) and Material Flow Analysis (MFA) within the circular economy framework, we thoroughly examine the lifecycle environmental performance of these materials. Our findings reveal a promising future where incorporating recycled materials in construction can significantly lower GHG emissions and conserve energy. This underscores their crucial role in advancing sustainable construction practices. Moreover, our study emphasizes the need for robust regulatory frameworks and technological innovations to enhance the adoption of environmentally responsible practices. We encourage policymakers, industry stakeholders, and the academic community to collaborate and promote the adoption of a circular economy strategy in the building sector. Our research contributes to the ongoing discussion on sustainable construction, offering evidence-based insights that can inform future policies and initiatives to improve environmental stewardship in the construction industry. This study aligns with the European Union’s objectives of achieving climate-neutral cities by 2030 and the United Nations’ Sustainable Development Goals outlined for completion by 2030. Overall, this paper contributes to the ongoing dialogue on sustainable construction, providing a fact-driven basis for future policy and initiatives to enhance environmental stewardship in the industry. 

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.

A case study has been chosen to impart a better understanding of the sustainable multicriteria decision-making (MCDM) approach for the selection of sustainable construction materials described in Chapter 4. The timber–concrete composite (TCC) floor system is a competent floor system that can take full advantage of the mechanical properties of both concrete and timber. Designing sustainable TCC floors involves several conflicting design criteria that must be considered simultaneously. The case study demonstrates an MCDM approach for weighting and ranking alternative TCC floors at the design stage. To set the criteria weights, a short survey was conducted on technical and production managers at industrialized house-building companies in both the Swedish and European markets. According to the MCDM results, the TCC floor with a 7.3m span length belonging to comfort class A has the highest ranking and was chosen for the detail design stage as the results of the case study.

This study takes a unique approach by investigating the integration of Brain–Computer Interfaces (BCIs) and Building Information Modeling (BIM) within residential architecture. It explores their combined potential to foster neuro-responsive, sustainable environments within the framework of Construction 5.0. The methodological approach involves real-time BCI data and subjective evaluations of occupants’ experiences to elucidate cognitive and emotional states. These data inform BIM-driven alterations that facilitate adaptable, customized, and sustainability-oriented architectural solutions. The results highlight the ability of BCI–BIM integration to create dynamic, occupant-responsive environments that enhance well-being, promote energy efficiency, and minimize environmental impact. The primary contribution of this work is the demonstration of the viability of neuro-responsive architecture, wherein cognitive input from Brain–Computer Interfaces enables real-time modifications to architectural designs. This technique enhances built environments’ flexibility and user-centered quality by integrating occupant preferences and mental states into the design process. Furthermore, integrating BCI and BIM technologies has significant implications for advancing sustainability and facilitating the design of energy-efficient and ecologically responsible residential areas. The study offers practical insights for architects, engineers, and construction professionals, providing a method for implementing BCI–BIM systems to enhance user experience and promote sustainable design practices. The research examines ethical issues concerning privacy, data security, and informed permission, ensuring these technologies adhere to moral and legal requirements. The study underscores the transformational potential of BCI–BIM integration while acknowledging challenges related to data interoperability, integrity, and scalability. As a result, ongoing innovation and rigorous ethical supervision are crucial for effectively implementing these technologies. The findings provide practical insights for architects, engineers, and industry professionals, offering a roadmap for developing intelligent and ethically sound design practices.

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Practical implications

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.

Originality/value

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 demand for a sustainable built environment is no longer a matter of personal choice, and sustainability performances need to be integrated within all activities in the construction projects. Growing performance objectives, including sustainability with several conflicting performance criteria, impose the application of tools based on a multicriteria decision-making (MCDM) approach for ranking and selecting sustainable building materials to consider all performance criteria simultaneously. Because both weights of criteria and sensitivity of decisions significantly influence the outcome of the decision-making process, it is important to pay particular attention to both the consistency of judgments by the experts in the field and sensitivity analysis. MCDM approach must allow interfacing with other engineering tools to evaluate performance metrics. This chapter examines the process of ranking and selecting sustainable building materials using the MCDM approach and illustrates how sustainability performance can be integrated into the materials selection process for construction projects.

Organizational culture is connected to people's capacity to organize themselves internally and adapt to the outside environment. Contracting companies were asked to complete a questionnaire based on organizational culture characteristics. This study intends to make a substantial theoretical contribution by demonstrating a broader perspective on exposing the essential cultural features using a quantitative approach with the fewest possible questions. Exploratory factor analysis and Principal Component Analysis were used to identify the most critical variables and extract a more manageable number of components from multiple related variables. Twenty-eight variables were reduced to thirteen to eliminate statistical cross-loading. Furthermore, the organizational culture was simplified from eight to three dimensions. Thirteen factors were created based on cultural habits in the construction sector, with three inclusive dimensions: multiple motivational, one–bipolar, and many bipolar. This study provides a more succinct survey that quantifies cultural habits and their influence on sectoral performance.

 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.