Lean Construction Strategies Supported by Artificial Intelligence Techniques for Construction Project Management—A Review

Authors

  • Lesly Velezmoro-Abanto Universidad Peruana de Ciencias Aplicadas, Lima, Perú
  • Rocío Cuba-Lagos University of Birmingham, Birmingham, United Kingdom
  • Bryan Taico-Valverde Escuela Universitaria de Posgrado, Universidad Nacional Federico Villarreal, Lima, Perú https://orcid.org/0000-0001-6314-4210
  • Orlando Iparraguirre-Villanueva Universidad Tecnológica del Perú, Chimbote, Perú https://orcid.org/0000-0001-8185-2034
  • Michael Cabanillas-Carbonell Facultad de Ingeniería, Universidad Privada del Norte, Lima, Perú

DOI:

https://doi.org/10.3991/ijoe.v20i03.46769

Keywords:

Construction Project management, Lean Construction, Lean Tools, Artificial Intelligence, Machine Learning

Abstract


This paper analyzes the application of artificial intelligence (AI) techniques in lean construction (LC) and their potential to enhance project management (PM) for improved cost and schedule efficiency. The PRISMA methodology is used to select relevant articles in four steps. Furthermore, a bibliometric analysis of keywords and their occurrences is conducted. The study emphasizes the different methods of utilizing lean tools and AI techniques to attain optimal results in the construction industry. By combining a variety of tools and techniques, it is possible to create an environment that fosters improved project outcomes while minimizing risks and inefficiencies. According to the articles reviewed, the LC methodology and its tools are becoming increasingly relevant in general practice (GP). Machine learning (ML) techniques, particularly artificial neural networks (ANN), have been extensively researched as a tool to enhance construction projects by minimizing delays, fostering collaboration, cutting costs, saving time, and boosting productivity. Combining LC with ML can enhance profitability and align with lean principles, leading to successful outcomes for construction projects.

Author Biography

Orlando Iparraguirre-Villanueva, Universidad Tecnológica del Perú, Chimbote, Perú

Systems Engineer with a Master's Degree in Information Technology Management, PhD in Systems Engineering from Universidad Nacional Federico Villarreal - Peru. ITIL® Foundation Certificate in IT Service, Specialization in Business Continuity Management, Scrum Fundamentals Certification (SFC). National and international speaker/panelist (Panama, Colombia, Ecuador, Venezuela, Mexico). Undergraduate and postgraduate teacher in different universities in the country. Advisor and jury of thesis in different universities. Consultant in information technologies in public and private institutions. Coordinator, director in different private institutions. Specialist in software development, IoT, Business Intelligence, open source software, Augmented Reality, Machine Learning, text mining and virtual environments.

Cover iJOE – Vol 20, No 03, 2024

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Published

2024-02-27

How to Cite

Velezmoro-Abanto, L., Cuba-Lagos, R., Taico-Valverde, B., Iparraguirre-Villanueva, O., & Cabanillas-Carbonell, M. (2024). Lean Construction Strategies Supported by Artificial Intelligence Techniques for Construction Project Management—A Review. International Journal of Online and Biomedical Engineering (iJOE), 20(03), pp. 99–114. https://doi.org/10.3991/ijoe.v20i03.46769

Issue

Vol. 20 No. 03 (2024)

Section

Papers

License

Copyright (c) 2023 Orlando Iparraguirre-Villanueva, Bryan Taico-Valverde, Rocío Cuba-Lagos, Lesly Velezmoro-Abanto, Michael Cabanillas-Carbonell

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