Purpose: The objective of this research is to investigate the capabilities of the ChatGPT GPT-4 model, a form of artificial intelligence (AI), in comparison to human experts in the context of construction project risk management.Design/methodology/approach: Employing a mixed-methods approach, the study draws a qualitative and quantitative comparison between 16 human risk management experts from Finnish construction companies and the ChatGPT AI model utilizing anonymous peer reviews. It focuses primarily on the areas of risk identification, analysis, and control.Findings: ChatGPT has demonstrated a superior ability to generate comprehensive risk management plans, with its quantitative scores significantly surpassing the human average. Nonetheless, the AI model's strategies are found to lack practicality and specificity, areas where human expertise excels.Originality/value: This study marks a significant advancement in construction project risk management research by conducting a pioneering blind-review study that assesses the capabilities of the advanced AI model, GPT-4, against those of human experts. Emphasizing the evolution from earlier GPT models, this research not only underscores the innovative application of ChatGPT-4 but also the critical role of anonymized peer evaluations in enhancing the objectivity of findings. It illuminates the synergistic potential of AI and human expertise, advocating for a collaborative model where AI serves as an augmentative tool, thereby optimizing human performance in identifying and managing risks.

Currently, digital situational awareness systems are popular in complex infrastructure construction projects. These systems monitor and assess environmental events, progress, resource availability, risks, and other project decision-making variables to support decision-making. However, little is known about how infrastructure construction (IC) professionals who utilize situational awareness systems perceive how they support or hinder situational management. The purpose of this exploratory research is to study, in depth, the relevance, challenges, and adoption of situational management in IC projects using digital systems. The data were collected via semistructured interviews with 21 IC managers and situational awareness management experts from 11 companies involved in railway projects. The main findings indicate that problem-solving improved with situational management in general, especially with digital situational awareness systems. Seizing the possibilities for transparency that accompany digital situational awareness systems helped in discussing emerging problems and making project choices. Expectations about the realism of such expectations were easier to align with historical event data. On the other hand, the informants reported difficulty in motivating contractors to collect situational data in digital form, possibly because of a lack of understanding about the purpose of data collection, the manual nature of data collection, the perceived excessiveness of data collection, or the manual transfer of collected data into digital form. For these reasons, the informants reported limited faith in these systems. A perceived drawback of situational management, whether supported by digital situational awareness systems or not, was its lack of applicability to the realities of a construction site. Systems were designed for project management needs but not tailored to the needs of construction projects. The interviewees’ statements indicate that maintaining situational awareness requires active interaction and constant checking of the provided information, even requiring pressure on the contractors providing the information. This study highlights the need for practical human approaches to effectively use digital situational awareness technologies and situational management in IC.