As a disruptive information and communication technology (ICT) in the architecture, engineering, construction, and operation (AECO) industry, building information modeling (BIM) enables project teams to manage a project via a model-based cooperative approach. Although it has a widespread impact on the industry, the systematic implementation of BIM in projects faces challenges. This study integrated BIM into the lifecycle of a building project with the introduction of a conceptual framework constituted by BIM Information Flow, BIM Model Chain, BIM Workflow, BIM Institutional Environment, and BIM-based Project Management Information System (PMIS). This conceptual framework identifies the key areas for integrating BIM into the project lifecycle and explains how BIM works for project management practice. Through an ethnographic action research approach, the study developed a BIM roadmap for the project lifecycle by systematically implementing BIM in the building project. The major findings and pieces of evidence derived via the implementation support the conceptual framework. The following discussions explain how BIM disrupts the project from the view of organization design and clarify the contributions of this study in project management, as well as BIM adoption and integration. Finally, the conclusions focus on the development of this research, the role of the conceptual framework to underlie the BIM roadmap, and the research limitations. Recommendations are provided toward future research works.

The recent coronavirus outbreak has captured worldwide attention. This study investigated the anxiety of the Chinese public and its relationship with social support during the early stage of the COVID-19 pandemic, thereby providing empirical support for interventions on improving the public’s mental health. On the basis of an online questionnaire survey conducted on 10–18 February 2020, this study shows that 19.8%, 68.5%, and 11.1% of the respondents suffered mild anxiety, moderate anxiety, and severe anxiety, respectively. Significant differences are reported in state anxiety between people with different household incomes. There are significant differences in trait anxiety and state anxiety between different social support groups. Social support and trait anxiety are negatively correlated. Social support and state anxiety are negatively correlated. Social support affects state anxiety both directly and indirectly (through the mediation of trait anxiety). Therefore, during the COVID-19 pandemic, increasing public support for society can effectively reduce public anxiety.

As a powerful artificial intelligence tool, the Artificial Neural Network (ANN) has been increasingly applied in the field of construction management (CM) during the last few decades. However, few papers have attempted to draw up a systematic commentary to appraise the state-of-the-art research on ANNs in CM except the one published in 2000. In the present study, a scientometric analysis was conducted to comprehensively analyze 112 related articles retrieved from seven selected authoritative journals published between 2000 and 2020. The analysis identified co-authorship networks, collaboration networks of countries/regions, co-occurrence networks of keywords, and timeline visualization of keywords, together with the strongest citation burst, the active research authors, countries/regions, and main research interests, as well as their evolution trends and collaborative relationships in the past 20 years. This paper finds that there is still a lack of systematic research and sufficient attention to the application of ANNs in CM. Furthermore, ANN applications still face many challenges such as data collection, cleaning and storage, the collaboration of different stakeholders, researchers and countries/regions, as well as the systematic design for the needed platforms. The findings are valuable to both the researchers and industry practitioners who are committed to ANNs in CM.

Artificial neural networks (ANN) exhibit excellent performance in complex problems and have been increasingly applied in the research field of construction management (CM) over the last few decades. However, few papers draw up a systematic review to evaluate the state-of-the-art research on ANN in CM. In this paper, content analysis is performed to comprehensively analyze 112 related bibliographic records retrieved from seven selected top journals published between 2000 and 2020. The results indicate that the applications of ANN of interest in CM research have been significantly increasing since 2015. Back-propagation was the most widely used algorithm in training ANN. Integrated ANN with fuzzy logic/genetic algorithm was the most commonly employed way of addressing the CM problem. In addition, 11 application fields and 31 research topics were identified, with the primary research interests focusing on cost, performance, and safety. Lastly, challenges and future directions for ANN in CM were put forward from four main areas of input data, modeling, application fields, and emerging technologies. This paper provides a comprehensive understanding of the application of ANN in CM research and useful reference for the future.