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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 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.

Post-disaster housing reconstruction (PDHR) demands a considerable percentage of global property investment, yet the post-disaster environment presents intricate challenges to reconstruction financing for governments and at the same time, revenue uncertainty for private investors. The purpose of this study is to develop a methodology for tackling land shortage and the financial challenges of PDHR in the aftermath of a disaster.

This study developed a methodology based on a combined minimum revenue guarantee and maximum revenue cap model using a well-established real options analysis (ROA) for revenue risk sharing in PDHR projects and land readjustment (LR) for finance. The applicability of the purported model is demonstrated through an illustrative example.

The results show that flexibility in the options could increase the PDHR contractor’s risk profile by increasing the expected value of the contractor investment and reducing the probability of investment loss. On the other side, a cap on the contractor revenue stream would allow the government to benefit from any excess in revenue and would counterbalance the value of the option.

The framework proposed in this study could serve as a practical risk-revenue sharing in PDHR projects. Governments and policymakers could use the findings to enable the successful delivery of PDHR projects and consequently bring the quality of life of affected people to pre-disaster conditions.

This study can be considered as a first attempt toward the use of the Australian barrier style options structure, and the trinomial lattice valuation model in PDHR projects, which incorporates LR, public-private partnerships, governmental guarantees and PDHR concepts in one ROA-based framework.

The purpose of this research is to (1) identify factors affecting food choices of young adults in Canada based on environmental perceptions, personal and behavioral factors as determinants of eating behaviors; (2) segment Canadian young adults based on the importance of the identified factors in their food choices.

An online survey was administered to Canadians aged between 18 and 24 to collect data on socio-demographic factors and eating behaviors (N = 297). An exploratory factor analysis (EFA) was used to identify the main factors affecting eating behaviors in young adults, followed by K-means clustering to categorize the respondents into consumer segments based on their propensity to agree with the factors.

Six factors were extracted: beliefs (ethical, environmental and personal); familiarity and convenience; joy and experience; food influencers and sociability; cultural identity; and body image. Using these factors, six consumer segments were identified, whereby members of each segment have more similar scores on each factor than members of other segments. The six consumer segments were: “conventional”; “concerned”; “indifferent”; “non-trend follower”; “tradition-follower”; and “eat what you love”.

Identifying major factors influencing eating behaviors and consumer segmentation provides insights on how eating behaviors might be shaped. Furthermore, the outcomes of this study are important for designing effective interventions for shaping eating behaviors particularly improving sustainable eating habits.