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The purpose of this study is to improve communication success through barrier identification and analysis so that the identified barriers can help project teams establish effective information-exchange strategies.

The recent publications on construction communication about time management are reviewed. Then, the semi-structured interviews are performed with both questionnaires and audio recordings (n1 = 18). Next, the collected data are analyzed using both statistical measures on the questionnaire survey and qualitative coding analysis on the text transcripts from an audio recording. Particularly, the identified barriers are substantiated using a scientometrics approach based on the published articles (2011–2020, n2 = 52,915) for purposeful information-sharing solutions in construction time management. Furthermore, the intervention strategies from the top 10 most-cited articles are analyzed and validated by comparisons with the results from construction surveys and relevant studies.

Based on the discussed communication difficulties, five main barriers were identified during time-cost risk management: probability and statistical concepts, availability of data from external resources, details of team member experiences, graphics (and graphical presentation skills), and spatial and temporal (a.k.a. 4D) simulation skills. For the improvement of communication skills and presentation quality regarding probability and statistical concepts, project teams should emphasize context awareness, case studies and group discussions. Details of communication techniques can be adjusted based on the backgrounds, experiences and expectations of team members.

The dataset n1 has both size and duration limits because of the availability of the invited industry professionals. The dataset n2 considers the literature from 2011 to 2020. Any before-the-date and unpublished studies are not included in the study.

A thorough comprehension of communication barriers can help project teams develop speaking, writing and analytical thinking skills that will enable the teams to better deliver ideas, thoughts and meanings. Additionally, the established discussion on barrier-removal strategies may enhance time management effectiveness, reduce project delays, avoid confusion and misunderstanding and save rework costs.

This research calls for the awareness of communication barriers in construction project execution and team collaboration. The identified barriers and the established solutions enrich the approaches of construction companies to share information with communities and society.

This is the first identification model for communication barriers in the time management of the construction industry to the authors' knowledge. The influencing factors and the countermeasures of communication difficulties highlighted by the research were not examined systematically and holistically in previous studies. The findings provide a new approach to facilitate the development of powerful communication strategies and to improve project execution.

This study aims to introduce a new methodology for generating synthetic images for facility management purposes. The method starts by leveraging the existing 3D open-source BIM models and using them inside a graphic engine to produce a photorealistic representation of indoor spaces enriched with facility-related objects. The virtual environment creates several images by changing lighting conditions, camera poses or material. Moreover, the created images are labeled and ready to be trained in the model.

This paper focuses on the challenges characterizing object detection models to enrich digital twins with facility management-related information. The automatic detection of small objects, such as sockets, power plugs, etc., requires big, labeled data sets that are costly and time-consuming to create. This study proposes a solution based on existing 3D BIM models to produce quick and automatically labeled synthetic images.

The paper presents a conceptual model for creating synthetic images to increase the performance in training object detection models for facility management. The results show that virtually generated images, rather than an alternative to real images, are a powerful tool for integrating existing data sets. In other words, while a base of real images is still needed, introducing synthetic images helps augment the model’s performance and robustness in covering different types of objects.

This study introduced the first pipeline for creating synthetic images for facility management. Moreover, this paper validates this pipeline by proposing a case study where the performance of object detection models trained on real data or a combination of real and synthetic images are compared.

This study aims to thoroughly explore and visualize the trends and developments of digital twin (DT) literature in the construction field while revealing future research directions for further exploration and exploitation.

The research follows a three-stage methodology. First, the bibliographic data is acquired using the Web of Science database. Second, the bibliometric methods are defined to include co-authorship analysis, citation analysis, keywords co-occurrence, thematic mapping while the software tools include MS Excel, VOSviewer and Biblioshiny. Third, analysis and findings include yearly DT publication output, influential DT publications, leading DT contributors, top DT sources and science mapping of DT literature.

This study identifies top-cited DT publications (35 out of 320) in terms of citations score, local citations score and document average citations per year. Furthermore, the key contributors with respect to authors (58 out of 1147), organizations (55 out of 427) and countries (19 out of 51) are recognized in terms of productivity, influence, activeness and scientific value. Similarly, the major publishing sources (24 out of 58) are identified using the same measures. Regarding science mapping, the DT domain comprises four research frontiers, namely, deep learning and smart city, internet of things and blockchain, DT and building information modeling and machine learning and asset management.

Through a mixed-review strategy, this study introduces a comprehensive analysis of DT literature while avoiding the subjectivity/cognitive bias of traditional review approaches. Moreover, it illuminates the promising and rising DT themes for new/seasoned researchers, institutions, editorial boards and funding agencies.

In recent years, deep learning and extended reality (XR) technologies have gained popularity in the built environment, especially in construction engineering and management. A significant amount of research efforts has been thus dedicated to the automation of construction-related activities and visualization of the construction process. The purpose of this study is to investigate potential research opportunities in the integration of deep learning and XR technologies in construction engineering and management.

This study presents a literature review of 164 research articles published in Scopus from 2006 to 2021, based on strict data acquisition criteria. A mixed review method, consisting of a scientometric analysis and systematic review, is conducted in this study to identify research gaps and propose future research directions.

The proposed research directions can be categorized into four areas, including realism of training simulations; integration of visual and audio-based classification; automated hazard detection in head-mounted displays (HMDs); and context awareness in HMDs.

This study contributes to the body of knowledge by identifying the necessity of integrating deep learning and XR technologies in facilitating the construction engineering and management process.

This paper aims to report upon the further development of a hybrid application programming interface (API) plug-in to building information modelling (BIM) entitled confined spaces safety monitoring system “CoSMoS”. Originally designed to engineer-out environmental hazards associated with working in a building’s confined spaces (during the construction phase of a building’s life-cycle), this second generation version is expanded upon to use archival records to proactively learn from data generated within a sensor network during the building’s operations and maintenance (O&M) phase of asset management (AM).

An applied research methodological approach adopted used a two-phase process. In phase one, a conceptual model was created to provide a “blueprint map” to integrate BIM, sensor-based networks and data analytics (DA) into one integral system. A literature review provided the basis for the conceptual model’s further development. In phase two, the conceptual model was transposed into the prototype’s development environment as a proof of concept using primary data accrued from a large educational building.

An amalgamation of BIM, historical sensor data accrued and the application of DA demonstrate that CoSMoS provides an opportunity for the facilities management (FM) team to monitor pertinent environmental conditions and human behaviour within buildings that may impact upon occupant/worker safety. Although working in confined spaces is used to demonstrate the inherent potential of CoSMoS, the system could readily be expanded to analyse sensor-based network’s historical data of other areas of building performance, maintenance and safety.

This novel prototype has automated safety applications for FM during the asset lifecycle and maintenance phase of a building’s O&M phase of AM. Future work is proposed in several key areas, namely, develop instantaneous indicators of current safety performance within a building; and develop lead indicators of future safety performance of buildings.