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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 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., 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.

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., 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.

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.

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.

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.

This article explores the possible ramifications of incorporating ideas from AEC Industry 6.0 into the design and construction of intelligent, environmentally friendly, and long-lasting structures. This statement highlights the need to shift away from the current methods seen in the AEC Industry 5.0 to effectively respond to the increasing requirement for creative and environmentally sustainable infrastructures. Modern building techniques have been made more efficient and long-lasting because of AEC Industry 6.0’s cutting-edge equipment, cutting-edge digitalization, and ecologically concerned methods. The academic community has thoroughly dissected the many benefits of AEC Industry 5.0. Examples are increased stakeholder involvement, automation, robotics for optimization, decision structures based on data, and careful resource management. However, the difficulties of implementing AEC Industry 6.0 principles are laid bare in this research. It calls for skilled experts who are current on the latest technologies, coordinate the technical expertise of many stakeholders, orchestrate interoperable standards, and strengthen cybersecurity procedures. This study evaluates how well the principles of Industry 6.0 can create smart, long-lasting, and ecologically sound structures. The goal is to specify how these ideas may revolutionize the building industry. In addition, this research provides an in-depth analysis of how the AEC industry might best adopt AEC Industry 6.0, underscoring the sector-wide significance of this paradigm change. This study thoroughly analyzes AEC Industry 6.0 about big data analytics, the IoT, and collaborative robotics. To better understand the potential and potential pitfalls of incorporating AEC Industry 6.0 principles into the construction of buildings, this study examines the interaction between organizational dynamics, human actors, and robotic systems.