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

• Autores: Velezmoro-Abanto, Lesly, Cuba-Lagos, Rocío, Taico-Valverde, Bryan, Iparraguirre-Villanueva, Orlando, Cabanillas-Carbonell, Michael
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Universidad Peruana de Ciencias Aplicadas
• Repositorio: UPC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorioacademico.upc.edu.pe:10757/675741
• Enlace del recurso: https://doi.org/10.3991/ijoe.v20i03.46769; http://hdl.handle.net/10757/675741
• Nivel de acceso: acceso abierto
• Materia: artificial intelligence; construction project management; lean construction; lean tools; machine learning; https://purl.org/pe-repo/ocde/ford#3.00.00

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.