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Rapid advancements in blockchain technology transform various sectors, attracting the attention of industrialists, practitioners, policymakers and academics, and profoundly affect construction businesses through smart contracts and crypto-economics. This paper explores the blockchain innovation ecosystem in construction.

Through a qualitative study of 23 diverse interviewees, the study explores how open or closed the blockchain innovation ecosystem in construction is and who its emerging orchestrators are.

The data showed that construction aims towards an open innovation blockchain ecosystem, although there are elements of hybridisation and closedness, each system pointing out to different orchestrators.

The study has implications for governments and large companies in construction, showing that open innovation initiatives need to be encouraged by policymakers through rules, regulations and government-sponsored demonstrator projects.

The data showed that there is lack of readiness for business model change to support open innovation blockchain ecosystems in construction.

This is the first study applying the open innovation theory in the construction industry and sheds light into the phenomenon of blockchain, suggesting routes for further democratisation of the technology for policymakers and practitioners.

The purpose of this paper is to discuss a production planning and control model known as the Lean construction management (LCM) model, which applies a number of visual tools in a systematic way to the planning and control process. The application of the visual tools in this way facilitates the flow of information, thus improving transparency between the interfaces of planning, execution and control.

Design Science research is adopted for this investigation, which analyses the original development of the model and reports on its testing and refinement over different types of projects. The research is divided into three parts, each part focussing on a different stage of development and construction project type.

The main findings are related to the benefits of visual management in the construction planning and control process, such as maintaining consistency between different planning levels, so that feasible execution plans are created; control becomes more focussed on prevention rather than correction, and creates opportunities for collaborative problem solving. Moreover, the physical display of the visual tools in a discrete planning area on-site encourages a regular exchange between participants on actual work progress as it unfolds, leading to more timely reaction to the problems at hand.

The problem of a lack of transparency in construction planning and control leads to communication issues on-site, poor process orientation and high levels of waste. LCM improves process transparency by making information related to system-wide processes more readily available to project participants. This enables them to foresee problems in a timely manner and to take necessary measures to resolve them or to adapt the process to current circumstances. The LCM model proposes a new way of applying visual tools and controls systematically to improve transparency in construction planning and control.

Predictive digital twin technology, which amalgamates digital twins (DT), the internet of Things (IoT) and artificial intelligence (AI) for data collection, simulation and predictive purposes, has demonstrated its effectiveness across a wide array of industries. Nonetheless, there is a conspicuous lack of comprehensive research in the built environment domain. This study endeavours to fill this void by exploring and analysing the capabilities of individual technologies to better understand and develop successful integration use cases.

This study uses a mixed literature review approach, which involves using bibliometric techniques as well as thematic and critical assessments of 137 relevant academic papers. Three separate lists were created using the Scopus database, covering AI and IoT, as well as DT, since AI and IoT are crucial in creating predictive DT. Clear criteria were applied to create the three lists, including limiting the results to only Q1 journals and English publications from 2019 to 2023, in order to include the most recent and highest quality publications. The collected data for the three technologies was analysed using the bibliometric package in R Studio.

Findings reveal asymmetric attention to various components of the predictive digital twin’s system. There is a relatively greater body of research on IoT and DT, representing 43 and 47%, respectively. In contrast, direct research on the use of AI for net-zero solutions constitutes only 10%. Similarly, the findings underscore the necessity of integrating these three technologies to develop predictive digital twin solutions for carbon emission prediction.

The results indicate that there is a clear need for more case studies investigating the use of large-scale IoT networks to collect carbon data from buildings and construction sites. Furthermore, the development of advanced and precise AI models is imperative for predicting the production of renewable energy sources and the demand for housing.

This paper makes a significant contribution to the field by providing a strong theoretical foundation. It also serves as a catalyst for future research within this domain. For practitioners and policymakers, this paper offers a reliable point of reference.

The impact of building information modeling (BIM) on various aspects of project management has attracted much attention in the past decade. However, previous studies have focused on a particular facet of project management (e.g., safety, quality, facility management) and within identified target journals. Despite numerous existing studies, there is limited research on the mainstream research topics, gaps and future research directions on BIM in project management. This study aims to conduct a bibliometric and science mapping review of published articles on BIM in project management and to identify mainstream research topics, research gaps and future research directions in this domain.

A science mapping approach consisting of bibliometric search, scientometric analysis and qualitative discussion was used to analyze 521 journal articles that were retrieved from the Scopus database and related to BIM in project management. In the scientometric analysis, keyword co-occurrence analysis and document analysis were performed. This was followed by a qualitative discussion that seeks to propose a framework summarizing the interconnection between the mainstream research topics, research gaps and future research directions.

Six mainstream research topics were found including (1) BIM-enabled advanced digital technologies, (2) BIM-based reinforcement and enhancement, (3) BIM and project composition, (4) BIM project elements and attributes, (5) BIM-based collaboration and communication and (6) BIM-based information and data. Moreover, this study discussed six research gaps, namely, (1) integration of BIM and other digital technologies, (2) future maturity of BIM applications in project management, (3) application of BIM in project components and processes, (4) role of BIM application in project elements and attributes, (5) impact of collaboration and communication in BIM application and (6) stability of information and data interaction. Furthermore, future research directions were discussed.

The findings and proposed framework contribute to providing a deeper understanding to researchers, policymakers and practitioners in the development of related research and practice in the domain of BIM in project management, thus, promoting digital transformation in project management. Overall, it adds to the global knowledge domain in BIM and promotes the need for digital and data integration, BIM maturity and BIM collaboration.

Construction is a risky industry. Therefore, organizations are seeking ways towards improving their safety performance. Among these, the integration of technology into health and safety leads to enhanced safety performance. Considering the benefits observed in using technology in safety, this study aims to explore digital technologies' use and potential benefits in construction health and safety.

An extensive bibliometrics analysis was conducted to reveal which technologies are at the forefront of others and how these technologies are used in safety operations. The study used two different databases, Web of Science (WoS) and Scopus, to scan the literature in a systemic way.

The systemic analysis of several studies showed that the digital technologies use in construction are still a niche theme and need more assessment. The study provided that sensors and wireless technology are of utmost importance in terms of construction safety. Moreover, the study revealed that artificial intelligence, machine learning, building information modeling (BIM), sensors and wireless technologies are trending technologies compared to unmanned aerial vehicles, serious games and the Internet of things. On the other hand, the study provided that the technologies are even more effective with integrated use like in the case of BIM and sensors or unmanned aerial vehicles. It was observed that the use of these technologies varies with respect to studies conducted in different countries. The study further revealed that the studies conducted on this topic are mostly published in some selected journals and international collaboration efforts in terms of researching the topic have been observed.

This study provides an extensive analysis of WoS and Scopus databases and an in-depth review of the use of digital technologies in construction safety. The review consists of the most recent studies showing the benefits of using such technologies and showing the usage on a systemic level from which both scientists and practitioners can benefit to devise new strategies in technology usage.

The purpose of this study is to explore the challenges hindering the adoption of Industry 4.0 (I4.0) among construction companies.

The construction industry needs innovative technologies due to its complex and dynamic nature. In this respect, the latest trends such as digitalization, building information modeling (BIM), Internet of things (IoT) are of utmost importance in terms of fostering the change in managing projects and encouraging industry practitioners to adopt the change for better performance. This paper focuses on I4.0adoption among construction companies. In this respect, a questionnaire was designed and administered to construction professionals to reveal the challenges in I4.0 adoption among construction firms. The respondents were requested to fill in the questionnaire on the I4.0 efforts of their companies. The questionnaire was intended to collect the perceptions of industry practitioners working at large construction companies. Based on these, the challenges listed were ranked based on their relative importance and success indices. Finally, the Mann–Whitney U test was conducted to test whether statistically significant responses exist among groups of respondents (i.e. young and old companies, large and small, high and low revenue and main area of expertise).

The results of the study indicated that resistance to change, unclear benefits and gains and cost of implementation are the major important challenges in terms of I4.0 adoption in construction projects. On the other hand, the data analysis implied that the majority of construction organizations successfully deal with the problems arising from lack of standardization, legal and contractual issues and cost of implementing in terms of promoting I4.0 adoption.

The study is expected to guide construction practitioners in terms of benefitting from I4.0 applications and deliver projects with better outcomes. This study might be used as a guide for the companies aiming to start their I4.0 transformation knowing the challenges and develop strategies for how to handle them. A concrete plan would help them achieve greater performance and benefit from the I4.0 implementation at the maximum level. Finally, the study implies that construction firms shall prepare action plans for handling each challenge listed and monitor their performance based on the planned and actual data of their projects.

This study investigates the major challenges of I4.0 among construction companies. This is one of the important studies, which puts I4.0 focus forefront of the construction industry with a clear identification of challenges that construction organizations have to address to transform their organizations into construction 4.0. The study has the potential to guide both industry practitioners and researchers to develop awareness for the benefits of using the latest technology and fostering innovation. This is expected to create value for construction clients in terms of achieving the product with serious gains such as time and cost.

The purpose of this paper is to investigate the current visual management (VM) practices in highways construction projects in England.

Following a comprehensive literature review, the research topic was investigated by using five case studies and focus groups.

The main findings are the current implementation of VM is limited, particularly on the construction field, there are some identified points (suggestions) that require attention to disseminate and advance the current practices further many conventional and Building Information Modelling (BIM)-based opportunities to extend the current VM implementations exist for the sector.

The highways construction and maintenance sector in England has been systematically deploying lean construction techniques in its operations for a while. One of those lean techniques is a close-range visual communication strategy called VM. The literature on the VM implementation in construction is scarce and generally limited to the building construction context. This paper documents the current industry practice in conventional and BIM-based VM and identifies a set of recommendations and some VM ideas for future implementation efforts in the highways construction and maintenance sector.

The purpose of this paper is to explore the benefits of visual management (VM) systems in transportation construction projects in England.

Following a comprehensive literature review, the benefits of VM were investigated through action and case study research executed within two construction projects in England.

The main findings are: VM can contribute to increased self-management, better team coordination, better promises or an increasing plan percent complete, easier control for the management and improved workplace conditions in the transportation sector. It is important for the management to obtain the engagement of their workforce for VM through increased participation and show the actual benefits. However, managerial monitoring and control on the systems should not be underestimated.

The transportation sector in England has been systematically deploying Lean construction techniques in its operations for a while. One of those Lean techniques is a close-range visual communication strategy called VM. The literature on the VM implementation in construction is scarce and generally limited to the building construction context. This paper documents the benefits of VM systems for the transportation sector by using data captured through both qualitative and quantitative data collection methods. The paper also identifies a set of recommendations for similar research efforts in the transportation context in the future.

This paper aims to explore the current condition of the Big Data concept with its related barriers, drivers, opportunities and perceptions in the architecture, engineering and construction (AEC) industry with an emphasis on facilities management (FM).

Following a comprehensive literature review, the Big Data concept was investigated through two scoping workshops with industry experts and academics.

The value in data analytics and Big Data is perceived by the industry, yet the industry needs guidance and leadership. Also, the industry recognises the imbalance between data capturing and data analytics. Large IT vendors’ developing AEC industry-focused analytics solutions and better interoperability among different vendors are needed. The general concerns for Big Data analytics mostly apply to the AEC industry as well. Additionally, however, the industry suffers from a structural fragmentation for data integration with many small-sized companies operating in its supply chains. This paper also identifies a number of drivers, challenges and way-forwards that calls for future actions for Big Data in FM in the AEC industry.

The nature of data in the business world has dramatically changed over the past 20 years. This phenomenon is often broadly dubbed as “Big Data” with its distinctive characteristics, opportunities and challenges. Some industries have already started to effectively exploit “Big Data” in their business operations. However, despite many perceived benefits, the AEC industry has been slow in discussing and adopting the Big Data concept. Empirical research efforts investigating Big Data for the AEC industry are also scarce. This paper aims at outlining the benefits, challenges and future directions (what to do) for Big Data in the AEC industry with an FM focus.

The purpose of this paper is to explore the current challenges and drivers for data mining in the AEC sector.

Following a comprehensive literature review, the data mining concept was investigated through a workshop with industry experts and academics.

The results showed that the key drivers for using data mining within the AEC sector is associated with the sustainability, process improvement, market intelligence, cost certainty and cost reduction, performance certainty and decision support systems agendas in the sector. As for the processes with the greatest potential for data mining application, design, construction, procurement, forensic analysis, sustainability and energy consumption and reuse of digital components were perceived as the main process areas. While the key challenges were perceived as being, data issues due to the fragmented nature of the construction process, the need for a cultural change, IT systems used in silos, skills requirements and having clearly defined business goals.

With the increasing abundance of data, business intelligence and analytics and its related concepts, data mining and Big Data have captured the attention of practitioners and academics for the last 20 years. On the other hand, and despite the growing amount of data in its business context, the AEC sector still lags behind in utilising those concepts in its end products and daily operations with limited research conducted to explore those issues at the sector level. This paper investigates the main opportunities and barriers for data mining in the AEC sector with a practical focus.