Big Data, Data Science, and Artificial Intelligence for Project Management in the Architecture, Engineering, and Construction Industry: A Systematic Review

Abstract

The high volume of information produced by project management and its quality have become a challenge for organizations. Due to this, emerging technologies such as big data, data science and artificial intelligence (ETs) have become an alternative in the project life cycle. This article aims to present a systematic review of the literature on the use of these technologies in the architecture, engineering, and construction industry. A methodology of collection, purification, evaluation, bibliometric, and categorical analysis was used. A total of 224 articles were found, which, using the PRISMA method, finally generated 57 articles. The categorical analysis focused on determining the technologies used, the most common methodologies, the most-discussed project management areas, and the contributions to the AEC industry. The review found that there is international leadership by China, the United States, and the United Kingdom. The type of research most used is quantitative. The areas of knowledge where ETs are most used are Cost, Quality, Time, and Scope. Finally, among the most outstanding contributions are as follows: prediction in the development of projects, the identification of critical factors, the detailed identification of risks, the optimization of planning, the automation of tasks, and the increase in efficiency; all of these to facilitate management decision making.

Keywords

• artificial intelligence
• architecture, engineering, and construction (AEC) industry
• big data
• data science
• digital twins
• Internet of Things