TY  - CONF
TI  - Developing a Genai Methodology for Data Analysis in Industrialized Construction: a Lean View
C1  - Osaka and Kyoto, Japan
C3  - Proceedings of the 33rd Annual Conference of the International Group for Lean Construction (IGLC 33)
SP  - 965
EP  - 975
PY  - 2025
DO  - 10.24928/2025/0231
AU  - Sepúlveda, Italo
AU  - Alarcón, Luis F.
AU  - Barkokebas, Beda
AU  - Ebensperger, Antonia
AD  - PhD Candidate, Department Construction Engineering and Management, Pontificia Universidad Católica de Chile, Santiago, Chile. Professor, Faculty of Architecture, Construction and Environment, Universidad Autónoma de Chile, Santiago, Chile, ilsepulveda@uc.cl, orcid.org/0000-0002-6019-9344
AD  - Professor, Department of Construction Engineering and Management, Pontificia Universidad Católica de Chile, Santiago, Chile, lalarcon@uc.cl, orcid.org/0000-0002-9277-2272
AD  - Assistant Professor, Department of Construction Engineering and Management, Pontificia Universidad Católica de Chile, Santiago, Chile, bbarkokebas@uc.cl,  orcid.org/0000-0002-0054-1320
AD  - Student Researcher, Department of Construction Engineering and Management, Pontificia Universidad Católica de Chile, Santiago, Chile, antonia.ebensperger@uc.cl, orcid.org/0009-0005-0843-5580
ED  - Seppänen, Olli 
ED  - Koskela, Lauri 
ED  - Murata , Koichi 
AB  - The research explores the potential of Generative Artificial Intelligence (GenAI) in enhancing data analysis processes within industrialized construction projects. The central question investigates whether a methodology can be developed to integrate GenAI into research workflows for construction projects. Existing studies highlight the challenges and opportunities of AI adoption in the construction industry but lack practical frameworks for its application in research, underscoring the need for this study.
The study employs GenAI across three phases studies: analyzing standardized data from 13 projects to identify common patterns and best practices, processing 57 interview transcriptions from industry leaders to assess readiness for industrialized construction, and comparing manual versus AI-supported analysis using 39 projects from an online industrialized construction database. The findings reveal that GenAI significantly reduces data processing time, enabling researchers to focus on in-depth analysis. Key lessons include the importance of prompt design, the context of data inputs, and the trade-offs between generic and customized AI models.
Building on these insights, the study proposes a GenAI-based methodology aligned with Lean Construction principles. The methodology was evaluated through a Likert-scale survey with seven construction professionals, confirming its clarity, feasibility, and applicability across various construction contexts.
KW  - Industrialized Construction
KW  - Generative AI
KW  - GenAI
KW  - Data Analytics
KW  - Lean Construction.
PB  - 
T2  - Proceedings of the 33rd Annual Conference of the International Group for Lean Construction (IGLC 33)
DA  - 2025/06/02
CY  - Osaka and Kyoto, Japan
L1  - http://iglc.net/Papers/Details/2393/pdf
L2  - http://iglc.net/Papers/Details/2393
N1  - Export Date: 02 June 2025
DB  - IGLC.net
DP  - IGLC
LA  - English
ER  -