34th Annual Conference of the International Group for Lean Construction (IGLC 34) - Singapore, Singapore - 2026

https://doi.org/10.24928/2026/0204

Establish a lean AI-driven defect management framework

Akande Tolulope1, Gao Shang2 & Mehran Oraee3

1Doctorate Candidate, Faculty of Architecture, Building and Planning, The University of Melbourne, Melbourne, Australia, [email protected], orcid.org/0009-0008-1291-2281
2Senior Lecturer, Faculty of Architecture, Building and Planning, The University of Melbourne, Melbourne, Australia, [email protected], orcid.org/0000-0002-4161-5592
3Senior Lecturer, Faculty of Architecture, Building and Planning, The University of Melbourne, Melbourne, Australia, [email protected], orcid.org/0000-0002-5928-4760

Abstract

The construction industry continues to face significant performance challenges due to defective work, with rework estimated to account for a substantial portion of project costs. Despite the adoption of Lean Construction principles, effective learning from defects remains hindered by fragmented workflows and the unstructured nature of defect data. Furthermore, while Artificial Intelligence (AI) has shown promise in quality management, existing research predominantly focuses on Computer Vision for reactive detection rather than leveraging textual data for proactive prevention. To address this gap, this study adopts a conceptual research methodology to propose the Lean AI-Driven Defect Management Framework. This framework integrates the philosophical pillars of Lean Construction: Respect for People, Flow, and Value, with the analytical capabilities of Large Language Models (LLMs). The proposed model conceptualises defect data not as a compliance metric, but as a strategic resource for organisational learning. By utilizing LLMs to process unstructured defect records and identify recurring patterns, the framework supports "just-in-time" decision-making and targeted Lean interventions, such as strategic "Muda walks." This study contributes to the field by offering a theoretical foundation for integrating human-centric Lean values with AI-driven knowledge systems, shifting defect management from reactive correction to continuous, data-driven improvement.

Keywords

Lean construction, defect management, artificial intelligence, large language model, Lean Construction 4.0.

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Reference in APA 7th edition format:

Tolulope, A., Shang, G. & Oraee, M.. (2026). Establish a lean AI-driven defect management framework. In Hamzeh, F., Poshdar, M., & Garcia-Lopez,, N. P. (Eds.), Proceedings of the 34th Annual Conference of the International Group for Lean Construction (IGLC 34) (pp. 120–131). https://doi.org/10.24928/2026/0204

Shortened reference for use in IGLC papers:

Tolulope, A., Shang, G. & Oraee, M.. (2026). Establish a lean AI-driven defect management framework. IGLC34. https://doi.org/10.24928/2026/0204

 

Founded in 1993, the IGLC is an international network of researchers from practice and academia in architecture, engineering, and construction (AEC) who feel that the practice, education, and research of the AEC industry have to be radically renewed in order to respond to the global challenges ahead.

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