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This paper aims to present a novel proof-of-concept framework for implementing building information modeling (BIM) Digital Objects (BDO) to automate construction product manufacturers’ processes and augment lean manufacturing.

A mixed interpretivist and post-positivist epistemological lens is adopted to pursue the proof-of-concept’s development. From an operational perspective, a synthesis of literature using interpretivism provides the foundation for deductive research inquiry implemented within a case study approach. Within the case study, participatory action research (PAR) is implemented to test the proof of concept via three “waterfall” research phases, namely, literature diagnosis and BIM package selection, BDO development and validation and evaluation.

The findings illustrate that a BDO (which represents the digital twin of manufacturing products) can augment and drive automation processes and workflows for construction product manufacturers within a contractor’s supply chain. The developed framework illustrates the benefits of a BDO, by reducing the number of manufacturing processes to effectively eliminate early errors in the model, generates financial savings and reduces material wastage.

This research provides a seminal case study that implements BDO to automate construction product manufacturing processes and demonstrates the utilisation of BDO at an operational (vis-à-vis theoretical) level. Future research is proposed to implement a longitudinal approach to measure and report upon the success (or otherwise) of the proof of concept when implemented on fabrications and shop floor procedures.

Construction manufacturers predominantly rely upon antiquated manual design and production processes and procedures because they lack technical skills needed to automate working practices. This paper aims to automate manufacturing processes by optimising the utilisation of BIM digital objects (BDO) via the development of a conceptual model. Concomitant objectives seek to reduce design errors; eliminate unnecessary costs; automate the generation of quantity bills; and maximise productivity performance.

An inductive approach was adopted through a post positivist epistemological lens set within the context of a case study of a small- and medium-sized enterprise. From an operational perspective, both qualitative and quantitative data were collected and analysed via a novel four-phase waterfall design, namely, literature diagnosis; recording contemporary practice; mapping manufacturing workflow and procedures; and evaluation and proof of concept development.

The work illustrates that BDO enhances manufacturing workflow, reduces product manufacturing lead time and augments quality assurance throughout the whole life cycle of a manufactured product. The conceptual model developed provides a pragmatic and comprehensive solution to automate construction manufacturing procedures and to improve the facilitation of information exchanged between all stakeholders involved.

This study presents the first comprehensive case study of BDO application within a manufacturing context. Future research is however, needed to test and validate the conceptual model presented in practice. In doing so, the model can be further refined using practitioner input and real-life manufacturing processes and procedures.

Master information delivery plan (MIDP) is a key requirement for building information modelling (BIM) execution plan (BEP) that enlists all information deliverables in BIM-based project, containing information about what would be prepared, when, by who, as well as the procedures and protocols to be used. In a well-conceived BEP, the MIDP facilitates collaboration among stakeholders. However, current approaches to generating MIDP are manual, making it tedious, error-prone and inconsistent, thereby limiting some expected benefits of BIM implementation. The purpose of this study is to automate the MIDP and demonstrate a collaborative BIM system that overcomes the problems associated with the traditional approach.

A BIM cloud-based system (named Auto-BIMApp) involving naming that automated MIDP generation is presented. A participatory action research methodology involving academia and industry stakeholders is followed to design and validate the Auto-BIMApp.

A mixed-method experiment is conducted to compare the proposed automated generation of MIDP using Auto-BIMApp with the traditional practice of using spreadsheets. The quantitative results show over 500% increased work efficiency, with improved and error-free collaboration among team members through Auto-BIMApp. Moreover, the responses from the participants using Auto-BIMApp during the experiment shows positive feedback in term of ease of use and automated functionalities of the Auto-BIMApp.

The replacement of traditional practices to a complete automated collaborative system for the generation of MIDP, with substantial productivity improvement, brings novelty to the present research. The Auto-BIMApp involve multidimensional information, multiple platforms, multiple types and levels of users, and generates three different representations of MIDP.

This paper explores the quality and flow of facade product information and the capabilities for avoiding the risk of facade fires early in the design process.

A qualitative case study using the process tracing method is conducted in two stages. First, a thematic analysis of reports and literature identified two categories for the problems that caused fast fire spread across the Grenfell Tower facade. This enabled classifying the identified problems into four stages of a facade life cycle: product design and manufacturing, procurement, facade design and construction. Second, the capabilities for avoiding the problems were explored by conducting in-depth interviews with 18 experts in nine countries, analyzing design processes and designers' expertise and examining the usability of three digital interfaces in providing required information for designing fire-safe facades.

The results show fundamental flaws in the quality of facade product information and usability of digital interfaces concerning fire safety. These flaws, fragmented design processes and overreliance on other specialists increase the risk of design defects that cause fast fire spread across facades.

The findings have implications for standardization of building product information, digitalization in industrialized construction and facade design management.

This research adds to the body of knowledge on sustainability in the built environment. It is the first study to highlight the fundamental problem of facade product information, which requires urgent attention in the rapid transition toward digital and industrialized construction.

Based on the systematic literature review, this paper aims to propose a framework of Construction 4.0 (C4.0) scenarios, identifying Industry 4.0 (I4.0) enabling technologies and their applications in the construction industry. The paper reviews C4.0 trends and potential areas for development.

In this research, a systematic literature review (SLR) methodology has been applied, including bibliographic coupling analysis (BCA), co-citation network analysis of keywords, the content analysis with the visualisation of similarities (VOSviewer) software and aggregative thematic analysis (ATA). In total, 170 articles from the top 22 top construction journals in the Scopus database between 2013 and 2021 were analysed.

Six C4.0 scenarios of applications were identified. Out of nine I4.0 technology domains, Industrial Internet of Things (IIoT), Cloud Computing, Big Data and Analytics had the most references in C4.0 research, while applications of augmented/virtual reality, vertical and horizontal integration and autonomous robotics yet provide ample avenues for the future applied research. The C4.0 application scenarios include efficient energy usage, prefabricated construction, sustainability, safety and environmental management, indoor occupant comfort and efficient asset utilisation.

This research contributes to the body of knowledge by offering a framework of C4.0 scenarios revealing the status quo of research published in the top construction journals into I4.0 technology applications in the sector. The framework evaluates current C4.0 research trends and gaps in relation to nine I4.0 technology domains as compared with more advanced industry sectors and informs academic community, practitioners and strategic policymakers with interest in C4.0 trends.

The construction industry has embraced building information modelling (BIM) as a practical methodology towards digitalisation. However, critics believe BIM has reached a plateau in addressing distinctive construction challenges. As a result, literature has seen an increase in the development and use of BIM-based plugins. Overall, the plugins have been critical in delivering custom-built solutions to longstanding construction challenges. The purpose of this study is to empirically investigate this trend and the potential barriers undermining the wider development of BIM plugins, by that contributing to a stimulating research topic and a growing knowledge gap.

Methodologically, this study used a quantitative approach to collect data through a carefully designed questionnaire. This study achieved a sample size of 39 experts who have been involved in developing, experimenting and publishing BIM-based plugins for specific construction activities.

The findings led to the identification of the key barriers to using BIM-based plugins and the identification of the key strategies to overcome them across the three situational awareness (SA) phases. Results also suggest that the development and use of plugins are destined to increase, and the research community can now rely on the insights of this paper as a departure point to address the technological plateau in BIM-related research.

To the best of the authors’ knowledge, this is the first study to empirically identify and assess the barriers undermining the wider development of BIM-based plugins. This study contributes to theory by building on SA, by challenging existing wisdom and fostering new knowledge around strategies to overcome the evaluated barriers.

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.

Digital twin (DT) and building information modeling (BIM) are interconnected in some ways. However, there has been some misconception about how DT differs from BIM. As a result, industry professionals reject DT even in BIM-based construction projects due to reluctance to innovate. Furthermore, researchers have repeatedly developed tools and techniques with the same goals using DT and BIM to assist practitioners in construction projects. Therefore, this study aims to assist industry professionals and researchers in understanding the relationship between DT and BIM and synthesize existing works on DT and BIM.

A systematic review was conducted on published articles related to DT and BIM. A total record of 54 journal articles were identified and analyzed.

The analysis of the selected journal articles revealed four types of relationships between DT and BIM: BIM is a subset of DT, DT is a subset of BIM, BIM is DT, and no relationship between BIM and DT. The existing research on DT and BIM in construction projects targets improvements in five areas: planning, design, construction, operations and maintenance, and decommissioning. In addition, several areas have emerged, such as developing geo-referencing approaches for infrastructure projects, applying the proposed methodology to other construction geometries and creating 3D visualization using color schemes.

This study contributed to the existing body of knowledge by overviewing existing research related to DT and BIM in construction projects. Also, it reveals research gaps in the body of knowledge to point out directions for future research.

This study aims to present a state-of-the-art review of building information modelling (BIM) in the Swedish construction practice with a focus on wood construction. It focuses on examining the extent, maturity and actual practices of BIM in the Swedish wood construction industry, by analysing practitioners’ perspectives on the current state of BIM and its perceived benefits.

A qualitative approach was selected, given the study’s exploratory character. Initially, an extensive review was undertaken to examine the current state of BIM utilisation and its associated advantages within the construction industry. Subsequently, empirical data were acquired through semi-structured interviews featuring open-ended questions, aimed at comprehensively assessing the prevailing extent of BIM integration within the Swedish wood construction sector.

The research concluded that the wood construction industry in Sweden is shifting towards BIM on different levels, where in some cases, the level of implementation is still modest. It should be emphasised that the wood construction industry in Sweden is not realising the full potential of BIM. The industry is still using a combination of BIM and traditional methods, thus, limiting the benefits that full BIM implementation could offer the industry.

This study provided empirical evidence on the current perceptions and state of practice of the Swedish wood construction industry regarding BIM maturity.

Accurate and rapid tracking and counting of building materials are crucial in managing on-site construction processes and evaluating their progress. Such processes are typically conducted by visual inspection, making them time-consuming and error prone. This paper aims to propose a video-based deep-learning approach to the automated detection and counting of building materials.

A framework for accurately counting building materials at indoor construction sites with low light levels was developed using state-of-the-art deep learning methods. An existing object-detection model, the You Only Look Once version 4 (YOLO v4) algorithm, was adapted to achieve rapid convergence and accurate detection of materials and site operatives. Then, DenseNet was deployed to recognise these objects. Finally, a material-counting module based on morphology operations and the Hough transform was applied to automatically count stacks of building materials.

The proposed approach was tested by counting site operatives and stacks of elevated floor tiles in video footage from a real indoor construction site. The proposed YOLO v4 object-detection system provided higher average accuracy within a shorter time than the traditional YOLO v4 approach.

The proposed framework makes it feasible to separately monitor stockpiled, installed and waste materials in low-light construction environments. The improved YOLO v4 detection method is superior to the current YOLO v4 approach and advances the existing object detection algorithm. This framework can potentially reduce the time required to track construction progress and count materials, thereby increasing the efficiency of work-in-progress evaluation. It also exhibits great potential for developing a more reliable system for monitoring construction materials and activities.