Studies have demonstrated that advanced technology, such as smart contract applications, can enhance both pre- and post-contract administration within the built environment sector. Smart contract technology, exemplifying blockchain technologies, has the potential to improve transparency, trust, and the security of data transactions within this sector. However, there is a dearth of academic literature concerning smart contract applications within the construction industries of developing countries, with a specific focus on Nigeria. Consequently, this study seeks to explore the relevance of smart contract technology and address the challenges impeding its adoption, offering strategies to mitigate the obstacles faced by smart contract applications. To investigate the stakeholders, this research conducted 14 virtual interview sessions to achieve data saturation. The interviewees encompassed project management practitioners, senior management personnel from construction companies, experts in construction dispute resolution, professionals in construction software, and representatives from government construction agencies. The data obtained from these interviews underwent thorough analysis employing a thematic approach. The study duly recognizes the significance of smart contract applications within the sector. Among the 12 identified barriers, issues such as identity theft and data leakage, communication and synchronization challenges, high computational expenses, lack of driving impetus, excessive electricity consumption, intricate implementation processes, absence of a universally applicable legal framework, and the lack of a localized legal framework were recurrent impediments affecting the adoption of smart contract applications within the sector. The study also delves into comprehensive measures to mitigate these barriers. In conclusion, this study critically evaluates the relevance of smart contract applications within the built environment, with a specific focus on promoting their usage. It may serve as a pioneering effort, especially within the context of Nigeria.

In recent years, the architecture, engineering, construction, and facility management (FM) industries have been applying various emerging digital technologies to facilitate the design, construction, and management of infrastructure facilities. Digital twin (DT) has emerged as a solution for enabling real-time data acquisition, transfer, analysis, and utilization for improved decision-making toward smart FM. Substantial research on DT for FM has been undertaken in the past decade. This paper presents a bibliometric analysis of the literature on DT for FM. A total of 248 research articles are obtained from the Scopus and Web of Science databases. VOSviewer is then utilized to conduct bibliometric analysis and visualize keyword co-occurrence, citation, and co-authorship networks; furthermore, the research topics, authors, sources, and countries contributing to the use of DT for FM are identified. The findings show that the current research of DT in FM focuses on building information modeling-based FM, artificial intelligence (AI)-based predictive maintenance, real-time cyber–physical system data integration, and facility lifecycle asset management. Several areas, such as AI-based real-time asset prognostics and health management, virtual-based intelligent infrastructure monitoring, deep learning-aided continuous improvement of the FM systems, semantically rich data interoperability throughout the facility lifecycle, and autonomous control feedback, need to be further studied. This review contributes to the body of knowledge on digital transformation and smart FM by identifying the landscape, state-of-the-art research trends, and future needs with regard to DT in FM.

Project management teams are critical in the implementation of megaprojects, but their evolution throughout the project lifecycle has not been clearly explained. This paper explores the organizational evolution of megaproject management teams through a longitudinal retrospective case study of the Hong Kong–Zhuhai–Macao Bridge (HZMB) project. The organizational evolution is examined in terms of management objectives, management content, and organizational structure. The organizational evolution of the HZMB project management team exhibits stage differentiation with the coexistence of turbulence and stability. Changes in the external environment are the driving force for organizational evolution, whereas a flexible organizational strategy is critical in promoting this evolution. Basing on the HZMB case study, this paper summarizes six critical measures that facilitate the organizational evolution of megaproject management teams. Our findings add value to megaproject management theory and provide a better understanding of the dynamics and complexity of megaproject organizational management.

With fierce competition in the global high-speed railway (HSR) market, international reputation has become essential for enterprises to venture into overseas markets. However, few studies have been performed on the international reputation of HSR enterprises. This study aims to reveal the formation mechanism of HSR enterprises’ international reputation by developing a theoretical framework. The researchers identified five factors and proposed a hypothetical path model based on a comprehensive literature review. After the pilot study, questionnaires were distributed to practitioners in the international HSR industry for data collection. The path model was validated based on partial least-squares structural equation modeling. Eight of the nine paths are statistically supported. Researchers have structured a theoretical framework for the international reputation of HSR enterprises from two perspectives: Being good and being known. Subsequently, a strategic framework was developed to provide targeted promotion strategies for HSR enterprises. The findings of this study contribute to existing international reputation theories using the theoretical model and provide beneficial guidance for HSR enterprises to improve their international reputation through a strategic framework.

Social infrastructure has become an important element for measuring national economic development and social benefits that are usually financed in the form of government grants, private investment, and public‒private partnerships (PPPs). However, social infrastructure PPPs have attracted considerable public debate due to their low profitability and complex operational demands. This study aims to answer the question, “are PPPs still an answer for social infrastructure?”, from the perspectives of the body of knowledge, application status, and prospects. Initially, an iterative search and review procedure and a scientometric analysis were performed to systematically screen the literature and to structure the body of knowledge of the social infrastructure PPPs literature. Furthermore, the application status and trends were analyzed to further explore the studied countries/regions, application sectors, and research topics. Results show that PPPs still have valuable application potential for delivering social infrastructure despite their mixed results across different topics. Six main research themes and the corresponding research trends were also identified, namely, financing and economics viability, risk management, performance management, contract and relationship management, governance and regulation, and facilitating and hindering factors. These findings offer practitioners and researchers a comprehensive overview and constructive guidance on social infrastructure PPPs.

Named entity recognition (NER) is essential in many natural language processing (NLP) tasks such as information extraction and document classification. A construction document usually contains critical named entities, and an effective NER method can provide a solid foundation for downstream applications to improve construction management efficiency. This study presents a NER method for Chinese construction documents based on conditional random field (CRF), including a corpus design pipeline and a CRF model. The corpus design pipeline identifies typical NER tasks in construction management, enables word-based tokenization, and controls the annotation consistency with a newly designed annotating specification. The CRF model engineers nine transformation features and seven classes of state features, covering the impacts of word position, part-of-speech (POS), and word/character states within the context. The F1-measure on a labeled construction data set is 87.9%. Furthermore, as more domain knowledge features are infused, the marginal performance improvement of including POS information will decrease, leading to a promising research direction of POS customization to improve NLP performance with limited data.

The construction industry is a major contributor to environmental pollution. The effect of the construction industry on the environment may be mitigated using eco-friendly construction materials, such as biocomposites. Once developed, biocomposites may offer a viable alternative to the current materials in use. However, biocomposites are lagging in terms of adoption and eventual use in the construction industry. This article provides insights into the steps for biocomposites to become a product that is ready to use by the construction industry in a structural role. The development and the adoption of such a material is tackled with the use of two concepts, i.e., technology readiness level and roadmapping, and explored in a case study on the “liquid wood”. Furthermore, interviews in the construction industry are carried out to identify the industry’s take on biocomposites. A customized roadmap, which underlines a mostly nontechnical perspective concerning this material, has emerged. Additionally, the adoption and diffusion issues that the “liquid wood” may encounter are outlined and complemented with further recommendations.