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Commercial building facility systems are costly, and due to the complexity of operation and maintenance (O&M), some failures can cause irreversible expensive damage. Currently, few capabilities of building information modeling (BIM) have been used in the facility maintenance operation. The purpose of this paper is to use a tool for making it easier to predict preventable defects in commercial building facility systems by examining a complete BIM.

To this end, a list of frequent defects in the facility system was achieved by a review of commercial building literature. Then, by examining a complete BIM model, the available methods and tools for O&M facilities during the operation period are identified and analyzed.

The results of the paper show that the solution presented based on total productive maintenance (TPM) subsets can effectively prevent facility system defects during the operation and maintenance period. An expert team assesses the validation of results.

The findings can help facility managers to adopt measures using TPM methods to prevent defects through an integrated BIM platform. The examination is based on case studies, surveys, expert experience and a thorough literature review.

Effective use of post-occupancy evaluation (POE) data – quantitative physical measurements and qualitative occupants’ perceptions are limited due to practical challenges and research gaps. Although building information modelling (BIM) has enabled a paradigm shift in the architecture, engineering and construction (AEC) industries, its use in facility management (FM) is still infancy. Limited research has used building performance data to enable changes to BIM models for the benefit of FM. This paper aims to propose the innovative process to collect and contextualize two fragmented types of POE data sets by filling methodological gap in POE research. Moreover, it presents innovative modelling techniques to facilitate BIM as a more effective platform to visualize such currently fragmented data sets in real-time while enabling a decision-making model to benefit facility managers.

The paper presents a process of capturing cloud-based POE data, both wireless sensor network-based physical measurement data and mobile app-based occupant perception data. Real-time capture and visualization of such building performance data was demonstrated through a pilot data collection. Subsequently, the innovative visualization of the cloud connected data is supported by a prototype game engine-based BIM model.

Cloud-based POE data, both quantitative physical measurements and qualitative occupants’ reported perceptions, can be effectively used in FM practice with the use of innovative data capture and visualization techniques in a beneficial manner for facility operation and management decisions. This paper also demonstrates the ability of BIM to serve as a “single source of truth” to support post-construction building performance data.

While addressing a number of research gaps, the paper provides a holistic approach to BIM-based performance monitoring for smart FM to achieve the ultimate vision of BIM enabled FM. The innovative system is expected to provide a powerful and practical tool for data collection, analysis and visualization for intelligent facility management decision making. This paper contributes to fill an important research and practice gap in the area of next generation smart building management practices.

The purpose of this paper is to create a value-based facilities management (FM) workplace by integrating lean principles, lean concepts, and specifically visual management technologies to improve efficiency, transparency, and value in the built environment.

The original research methodology used design science research framework with an action research (AR) approach. The chosen qualitative research methods included questionnaires, unstructured and semi-structured interviews, and participatory observation in action.

The facilities asset management department needs to align itself more strategically with organization leadership by understanding core business values, mission, and vision. It is essential they become a partner in creating a value-based contribution to the organizations bottom line and strategic plan. This can be established by embracing lean principles, concepts, and visual technologies, strategically linking the FM and asset management department to the organization holistically. The research acknowledged that a lean visual workplace management system could be introduced in facilities asset management with success. The findings suggest that change management should form part of the lean journey.

The original research was limited in scope to one UK university estates and property services department’s lean journey and their third party outsourced FM service provider.

By using the visual workplace management system to integrate lean and visual management technologies in FM asset management systems value-based FM services and recognition from the C-suite will be achieved. Additionally, a new design science framework with an AR approach was developed as a research method

Perceived benefits of building information modelling (BIM) have been discussed for some time, but cost–benefit benchmarking has been inconsistent. The purpose of this paper is to investigate BIM feasibility and evaluate investment worth to elucidate and develop the current understanding of BIM merit. The aim of the study is to propose a research agenda towards a more holistic perspective of BIM use incorporating quantifying investment return.

An in-depth examination of research patterns has been conducted to identify challenges in the assessment of the investment value and return on investment (ROI) for BIM in the construction industry. A total of 75 research articles were considered for the final literature review. An evaluation of the literature is conducted using a combination of bibliometric analysis and systematic reviews.

This study, which analysed 75 articles, unveils key findings in quantifying BIM benefits, primarily through ROI calculation. Two major research gaps are identified: the absence of a standardized BIM ROI method and insufficient exploration of intangible benefits. Research focus varies across phases, emphasizing design and construction integration and exploring post-construction phases. The study categorizes quantifiable factors, including productivity, changes and rework reduction, requests for information reduction, schedule efficiency, safety, environmental sustainability and operations and facility management. These findings offer vital insights for researchers and practitioners, enhancing understanding of ’BIM’s financial benefits and signalling areas for further exploration in construction.

The ’study’s outcomes offer the latest insights for researchers and practitioners to create effective approaches for quantifying ’BIM’s financial benefits. Additionally, the proposed research agenda aims to improve the current limited understanding of BIM feasibility and investment worth evaluation. Results of the study could assist practitioners in overcoming limitations associated with BIM investment and economic evaluations in the construction industry.

Building information modelling (BIM) is a paradigm shift in the architectural, engineering and construction industries which transforms processes to achieve greater efficiency and effectiveness. The purpose of this paper is to highlight the implementation of BIM in Hong Kong and the USA and recommend strategies for BIM implementation in Hong Kong.

A literature review of BIM initiatives taken in Hong Kong and the USA was conducted including the government policy, guidelines, standards and implementation status. Findings from the literature review were complemented with insights obtained through focused interviews of experts in the field for arriving at recommendations based on the research. The principal investigator of this research also participated in the establishment of a BIM professional institute as one of the founding board members.

As a result of studies performed for this paper, guidelines are proposed for BIM implementation in Hong Kong. The role of government in implementing BIM in different phases of a project lifecycle is also highlighted.

The research is limited mainly to the initiatives of BIM taken by the public sector (government). The roles of other stakeholders are not specifically studied. Results from this study should be considered in the context of a qualitative research methodology adopted and the limited number of responses obtained for arriving at the findings of this paper.

Results from this study can be used as guidelines for the implementation of BIM elsewhere and this implementation can be compared with other countries.

This paper highlights the best practices of BIM implementation, which is useful for the effective and efficient implementation of BIM in Hong Kong.

Digital transformation (DT) innovation is a monumental contribution that has had a profound effect on several worldwide industries. The aim of this research is to evaluate the current and future trends in DT specifically focusing in construction industry.

This study adopts a qualitative analysis approach grounded on descriptive and bibliometric analyses. In total, 283 papers from Scopus between January 2015 and April 2023 were retrieved in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) review methodology. This study examines the publishing trends, most productive nation, university, publications and authors. Keyword co-occurrence analysis and thematic evolution were analyzed through Vosviewer and Biblioshiny.

The results illustrate a growing desire to use digital technologies in the construction industry, which shows the topic's power and expanding popularity. This research reveals various emerging themes based on technology usage in construction sector. Out of 14 themes, occupational health and safety, mass customization, virtual reality and artificial intelligence were identified as isolated themes. Further, this study elaborates the difficulties encountered by the construction industry while employing digital technologies and examines the interrelationships among various keywords in DT and reveals the paradoxes and hotspots.

This research adds to the body of literature as it identifies the research areas and gaps in the existing DT domain in construction industry. The integration of technology in this sector has an intense positive future vision as various subareas have immense potential for technology application.

The purpose is two-fold: (1) to explore the interactions of robotic systems and lean construction in the context of offsite construction (OC) that were addressed in the literature published between 2008 and 2019 and (2) to identify the gaps in such interactions while discussing how addressing those gaps can benefit not only OC but the architecture, engineering and construction (AEC) industry as a whole.

First, a systematic literature review (SLR) identified journal papers addressing the interactions of automation and lean in OC. Then, the researchers focused the analysis on the under-researched subtopic of robotic systems. The focused analysis includes discussing the interactions identified in the SLR through a matrix of interactions and utilizing literature beyond the previously identified articles for future research directions on robotic systems and lean construction in OC.

The study found 35 journal papers that addressed automation and lean in the context of OC. Most of the identified literature focused on interactions of BIM and lean construction, while only nine focused on the interactions of robotic systems and lean construction. Identified literature related to robotic systems mainly addressed robots and automated equipment. Additional interactions were identified in the realm of wearable devices, unmanned aerial vehicles/automated guided vehicles and digital fabrication/computer numerical control (CNC) machines.

This is one of the first studies dedicated to exploring the interactions of robotic systems and lean construction in OC. Also, it proposes a categorization for construction automation and a matrix of interactions between construction automation and lean construction.

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.