Affiliation:
1. Especialización en Gerencia de Proyectos/Ingeniería Industrial, Rectoría Virtual, Corporación Universitaria Minuto de Dios, Bogotá D.C 111021, Colombia
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.
Subject
Building and Construction,Civil and Structural Engineering,Architecture
Reference104 articles.
1. Oxford Economics (2021). Future of Construction, Oxford Economics.
2. Cooke, B., and Williams, P. (2013). Construction Planning, Programming and Control, John Wiley & Sons.
3. Saltz, J.S. (November, January 29). The Need for New Processes, Methodologies and Tools to Support Big Data Teams and Improve Big Data Project Effectiveness. Proceedings of the 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, USA.
4. Building a Rough Sets-Based Prediction Model for Classifying Large-Scale Construction Projects Based on Sustainable Success Index;Akbari;ECAM,2018
5. Larson, E., and Gray, C. (2014). Project Management: The Managerial Process 6e, McGraw Hill.
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