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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.

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.

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.

This study aims to assess the status and determinants of personal social capital (PSC) in Syrian refugees in Jordan, comparing the inside and outside camp residents.

A descriptive, cross-sectional design was used to collect data from 151 Syrian refugees residing inside and outside the camp.

The overall PSC score was relatively low (M = 27.5 ± 7.15, 95% CI: 26.3–28.6). Scores were significantly associated with age group, marital status, educational level, income level and living place (p < 0.05). Two steps multiple hierarchal regressing showed that income and place of residence are the only significant predictors of PSC (p ≤ 0.05).

Social support programs are required among Syrian refugees residing outside the camp, specifically among families with older and poorly educated parents, single-parent families and low-income refugees.

The construction industry faces public criticism for issues like wastefulness, inefficiency, slim profits, scheduling setbacks, budget overruns, quality concerns, trust deficits, transparency, coordination, communication and fraud. This paper aims to assess the nexus between barriers and drivers for adopting blockchain in construction and its impact on construction lifecycle.

A quantitative research approach was used to collect data using a well-structured questionnaire survey. The survey, which used snowball sampling, included 155 Nigerian construction experts that included architects, builders, quantity surveyors and engineers in the built environment. The data were analysed using partial least squares structural equation modelling (PLS-SEM), which allowed for a thorough evaluation of the proposed relationships as well as industry-specific insights.

The study's findings validate the conceptual framework established. The results indicate that implementing blockchain across all stages of construction projects has the potential to improve the construction process by 88.2% through its drivers. However, there were no significant relationships found between the barriers to adopting blockchain and the potential application areas in the construction lifecycle.

This research was carried out in the South-western which is one of the six geo-political zones/regions in Nigeria, using a cross-sectional survey method. The study did not investigate the interdependence of the identified categories of drivers and barriers, limiting a comprehensive understanding of the complex dynamics and interactions influencing blockchain adoption in construction. The study is expected to stimulate further exploration and generate new insights on how blockchain technology (BT) can influence various stages of the construction lifecycle.

The findings will be immensely beneficial to both professionals and practitioners in the Nigerian construction industry in learning about the potential of BT application in improving the construction lifecycle.

This paper developed and assessed a conceptual framework by investigating the interrelationships between the constructs. The findings have important implications for the construction industry, as they offer opportunities to improve the construction process and overall lifecycle. The findings are useful for researchers interested in the potential impact of BT on the construction lifecycle and its wider implications.