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

https://doi.org/10.24928/2026/0206

Embedded machine learning as an enabler of lean construction

Björn Bernhard Vauk1, Lars Eric Mentrup2 & Felix Archibald Enge3

1PhD Candidate, Institute for Production Technology and Systems (IPTS), Leuphana University Lüneburg, Germany, [email protected], orcid.org/0009-0006-3464-3049
2PhD Candidate, TU Berlin, Germany, [email protected], orcid.org/0009-0000-2270-5667
3Dr.-Ing., CEO, Makeo, Germany, [email protected], orcid.org/0009-0006-5605-646X

Abstract

Lean Construction aims to continuously improve construction processes through consistent alignment with customer value. Accordingly, research indicates the necessity of objective and scalable methods for the continuous capture of construction processes. Manual observations enable in-depth contextual analysis but produce discontinuous and sample-based data. Observer-independent measurement approaches for continuous acquisition of time expenditures per construction activity support the provision of scalable data across workers, work packages, shifts, trades, and construction projects. To address this need, an approach is presented for automated recognition of construction activities using embedded machine learning. In a painting trade case study, a single wrist-worn sensor system classifies main activities at 6 s intervals, achieving an accuracy of 96.4 %. Time-series analysis under open-set site conditions consistently aggregates activity sequences, validated against video-based ground truth. This enables the reconstruction of chronological process sequences and their quantification in terms of time expenditures per activity. This approach can make production flow observable, support the assessment of performance targets within work packages and takts and the analysis of trade-offs between flow and resource efficiency. Linking activity-based time expenditures with construction outputs may support the derivation of labour consumption rates and thereby contribute to the implementation of Lean Construction in construction management.

Keywords

Lean construction, process, production, work flow, AI.

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Reference in APA 7th edition format:

Vauk, B. B., Mentrup, L. E. & Enge, F. A.. (2026). Embedded machine learning as an enabler of lean construction. 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. 132–142). https://doi.org/10.24928/2026/0206

Shortened reference for use in IGLC papers:

Vauk, B. B., Mentrup, L. E. & Enge, F. A.. (2026). Embedded machine learning as an enabler of lean construction. IGLC34. https://doi.org/10.24928/2026/0206

 

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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