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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 paper is to present a review of the implications building information modelling (BIM) is having on the building energy modelling (BEM) and design of buildings. It addresses the issues surrounding exchange of information throughout the design process, and where BIM may be useful in contributing to effective design progression and information availability.

Through review of current design procedures and examination of the concurrency between architectural and thermophysical design modelling, a procedure for information generation relevant to design stakeholders is created, and applied to a high-performance building project currently under development.

The extents of information key to the successful design of a buildings energy performance in relation to its architectural objectives are given, with indication of the level of development required at each stage of the design process.

BIM offers an extensible medium for parametric information storage, and its implementation in design development offers the capability to include BEM parameter-integrated construction information. The extent of information required for accurate BEM at stages of a building’s design is key to understanding how best to record performance information in a BIM environment.

This paper contributes to the discussion around the integration of concurrent design procedures and a common data environment. It presents a framework for the creation and dissemination of information during design, exemplifies this on a real building project and evaluates the barriers experienced in successful implementation.

This study aims to facilitate consistency of information in building information modelling (BIM) and address the current BIM gaps through the perspectives of the productization concept and product structure (PS).

The study follows a conceptual research approach in conjunction with a single case study. First, the previous studies on BIM implementation, productization and PS are reviewed. Further, a case study is used to analyse the current state of productization in the construction sector and develop a functional PS for construction.

A Part-Phase-Elements Matrix is proposed as a construction-specific PS to facilitate consistency in information and to enhance BIM. The proposed matrix provides new avenues to facilitate consistent information exchange through the interconnection between conceptual PS and standard building objects library, and encourage collaborative communication between stakeholders.

This study explores the core of the productization concept and PS as means to facilitate consistency of information and thus address the current gaps in BIM. This as building projects progressively move towards systematic modular and prefabricated construction where the flow of reliable information about product and construction offerings becomes increasingly important.

The construction sector is increasingly engaged to explore the potential application of building information modelling (BIM) for the sustainability of the buildings. BIM can be applied conjugated to certification methods to evaluate the sustainability of the buildings, and it is known as building sustainability assessment (BSA). This article aims to explore applications of BIM in the sustainability analysis of buildings, including five types of BSA.

A literature review from 2009 to 2020 considering a combination of search terms related to BIM and BSA complemented the analysis. The bibliometric analysis indicates an increase in the number of publications on this topic of study in the last five years. A peak occurred in 2015 and 2020, with 51 and 66 publications, respectively.

The results showed that BIM contributes to achieving part of the sustainability requirements from BSA, especially quantitative requirements that are related to material and resources. However, a complete analysis between BIM and BSA is still a challenge since part of BSA's have a lack information in their requirements and BIM softwares present interoperability problems that difficult an integrated analysis to extract information directly from the software to attend BSA's requirements.

The results contribute to analysing the evolution of studies about BIM and to understand the limitation in the applicability with sustainability rating systems. Future directions of research indicate the necessity to improve the use of BIM in the sustainability analysis of the buildings, but with a good perspective regarding the application of BIM in material and resources requirements, and waste management in construction sites.

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 paper aims to gain insight in how the involvement of facilities management (FM) and clinical employees are practiced in new Norwegian hospital projects and to study the benefits and lessons learned from the involvement.

This study is conducted by cross-sectional case studies of eight hospital projects by using a literature review, interviews and document studies of FM and clinical employees and project leaders (PLs) among Sykehusbygg.

The service design approach with a structured interaction between the PL’s of Sykehusbygg, and the different disciplines of clinical employees and FM specialist was rewarding and efficient. The facilitator role of Sykehusbygg is essential to manage such a broad and complex involvement process using a wide range of various techniques at the different stages of the projects (dialogue meetings, review meetings, workshops, post-it notes, 2-D drawings, mock-up and 3-D models, as well as virtual reality (VR) and Building Information Modeling technology). The clinical employees’ framework is stronger and much more structured than the involvement of FM competences through the different stages of the projects. The property management competences were involved at the early concept phase and design phase, whereas the Operation and Maintenance (O&M) competences were getting involved through the construction and commissioning phase. The value of FM involvement in all stages of the project is seen beneficial, particularly when the FM specialist become a part of the design team and located physically at the same place. The main reported benefits of early FM involvement are cost-effective technical solutions and installations, less design flaws and improved functionality, as well as a stronger ownership and mutual respect between the clinical and FM disciplines. However, not all hospital organizations see the benefits of the FM involvement of all stages, as they are driven by reducing capital cost. In one of the new projects, other ways of involving the FM competences were tested. Additionally, particularly for the O&M competences, a dialogue meeting with a clear focus of sharing experiences with different technical solution was found rewarding in terms of cost benefits.

This study does not consider the social impact of the choices made in the design phase. The findings also indicated a certain development of the FM involvement. This is not studied in two of the newest projects where they are still in the design phase and the FM role was not interviewed.

The PL role is important as a facilitator role of the involvement process.

A dialogue meeting with a group of O&M people was found rewarding and valuable for knowledge sharing. This methodology can be further developed and tested, as this group of stakeholders is not always available for giving input in the project.

The value of this study is the description of the interaction between the PLs and the hospital organization in the eight projects and lessons learned by the involvement of FM competences and clinical employees.

The purpose of this paper is twofold. First, it intends to explore the link between internal social capital, logistics capabilities, supply chain risk management (SCRM) capabilities and supplier performance. Second, the mediating effect of logistics capabilities between internal social capital and SCRM capabilities, and that of SCRM capabilities between logistics capabilities and supplier performance are also examined.

A theoretical model developed from the extant literature was empirically validated through a cross-sectional survey of 122 respondents in 52 public healthcare facilities in Uganda. The data were analysed using partial least square structural equation modeling (PLS-SEM).

The study found that internal social capital and SCRM capabilities are significant predictors of supplier performance. Internal social capital is positively and significantly related to logistics capabilities, and logistics capabilities are positively and significantly related to SCRM capabilities. The authors also found non-significant relationships between internal social capital and SCRM capabilities, and between logistics capabilities and supplier performance. Furthermore, it was revealed that logistics capabilities play a partial mediating role in the relationship between internal social capital and SCRM capabilities, while SCRM capabilities fully mediate between logistics capabilities and supplier performance.

Further to providing empirical evidence of the antecedents of supplier performance in the public healthcare in a developing economy, which has been evidently scant, this study provides initial empirical evidence of the mediating role of logistics capabilities in the relationship between internal social capital and SCRM capabilities and that of SCRM capabilities in the relationship between logistics capabilities and supplier performance. This is important for understanding the mechanism through which supplier performance can be enhanced.

Low productivity in construction is typically blamed on the seemingly complex and chaotic nature of construction, which emerges as the stakeholders do not have an adequate picture of the evolving situation. The ever-increasing volume of situation data owing to the recent advances in IoT devices and reality capture platforms provide a unique opportunity to capture the actual situation data of construction projects accurately at a fraction of the cost compared to manual status tracking and reporting. This paper aims to investigate the concept of a situation picture, challenges in collecting situation data and its benefits.

Empirical data is collected through interviews in California and Finland, and by organizing workshops.

We contribute to literature on managing operational information by defining the concept of a situation picture in the context of construction, specifically from the blue-collar’s perspective during on-site activities. We present the key components of a conceptual information model that represents a situation picture in construction.

The applicability of conceptual information model of situation picture is not tested in practice, but the model will provide a starting point for research to comprehensively integrate social and digital information exchange for improving workflow.

The paper claims that designing and building comprehensive information management infrastructure would contribute to solving the problems of low productivity, quality and safety in construction projects.

Research on situation picture and situation awareness is scarce in the context of construction. The study links various information management technologies and practices to actual construction productivity.

Effective information management can help real estate operators improve asset performance during use, reducing environmental impact. The purpose of this exploratory study is to identify and prioritise key drivers, challenges and opportunities relating to information management, from the point of view of a diverse cohort of facilities practitioners, with the aim of guiding future research direction and contributing to a comprehensive domain understanding.

Nine interviews are conducted across a broad sample of real estate sectors, the respondents including six facility managers and three data managers. A thematic analysis results in the identification and ranking in terms of importance of 44 emergent themes. These themes are then grouped into abstracted categories for analysis and synthesis.

This study indicates that systemic rather than technical issues are the greatest barrier to effective information management for facilities practitioners, the interviews providing examples of practical measures which address these challenges, promoting lifecycle thinking. Alignment is also found between the facilities and data management cohorts regarding lifecycle thinking towards both physical assets and information.

This study provides direction for future developments in the facilities sector, suggesting the pursuit to address systemic issues as being both worthwhile and feasible.

The novelty of this study is the ranking and synthesis of practitioner priorities with regard to high-level information management issues which is lacking in the literature, with a focus to-date on case-specific technical integration.

This paper aims to develop predictive methods, based on recurrent neural networks, useful to support facility managers in building maintenance tasks, by collecting information coming from a computerized maintenance management system (CMMS).

This study applies data-driven and text-mining approaches to a CMMS data set comprising more than 14,500 end-users’ requests for corrective maintenance actions, collected over 14 months. Unidirectional long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM) recurrent neural networks are trained to predict the priority of each maintenance request and the related technical staff assignment. The data set is also used to depict an overview of corrective maintenance needs and related performances and to verify the most relevant elements in the building and how the current facility management (FM) relates to the requests.

The study shows that LSTM and Bi-LSTM recurrent neural networks can properly recognize the words contained in the requests, thus correctly and automatically assigning the priority and predicting the technical staff to assign for each end-user’s maintenance request. The obtained global accuracy is very high, reaching 93.3% for priority identification and 96.7% for technical staff assignment. Results also show the main critical building elements for maintenance requests and the related intervention timings.

This work shows that LSTM and Bi-LSTM recurrent neural networks can automate the assignment process of end-users’ maintenance requests if trained with historical CMMS data. Results are promising; however, the trained LSTM and Bi-LSTM RNN can be applied only to different hospitals adopting similar categorization.

The data-driven and text-mining approaches can be integrated into the CMMS to support corrective maintenance management by facilities management contractors, i.e. to properly and timely identify the actions to be carried out and the technical staff to assign.

The improvement of the maintenance of the health-care system is a key component of improving health service delivery. This work shows how to reduce health-care service interruptions due to maintenance needs through machine learning methods.

This study develops original methods and tools easily integrable into IT workflow systems (i.e. CMMS) in the FM field.