  The purpose of this paper is to examine the opportunities and barriers of
Integrated Human-Machine Intelligence (IHMI) in civil engineering. Integrating
artificial intelligence's high efficiency and repeatability with humans'
adaptability in various contexts can advance timely and reliable
decision-making during civil engineering projects and emergencies. Successful
cases in other domains, such as biomedical science, healthcare, and
transportation, showed the potential of IHMI in data-driven, knowledge-based
decision-making in numerous civil engineering applications. However, whether
the industry and academia are ready to embrace the era of IHMI and maximize its
benefit to the industry is still questionable due to several knowledge gaps.
This paper thus calls for future studies in exploring the value, method, and
challenges of applying IHMI in civil engineering. Our systematic review of the
literature and motivating cases has identified four knowledge gaps in achieving
effective IHMI in civil engineering. First, it is unknown what types of tasks
in the civil engineering domain can be assisted by AI and to what extent.
Second, the interface between human and AI in civil engineering-related tasks
need more precise and formal definition. Third, the barriers that impede
collecting detailed behavioral data from humans and contextual environments
deserve systematic classification and prototyping. Lastly, it is unknown what
expected and unexpected impacts will IHMI have on the AEC industry and
entrepreneurship. Analyzing these knowledge gaps led to a list of identified
research questions. This paper will lay the foundation for identifying relevant
studies to form a research roadmap to address the four knowledge gaps
identified.
