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Towards the Development of a Budget Categorisation Machine Learning Tool: A Review

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Trends on Construction in the Digital Era (ISIC 2022)

Abstract

Engineering, procurement, and construction (EPC) contracts include time, budget, quality, and safety, among other issues. In budgeting, construction companies must assess each task's scope and map the client's expectations (expressed in the bill of quantities) to an internal database of tasks, resources, and costs. The results from this classification will determine the quality of the tenders issued by the company and are thus contractually binding. Construction companies must achieve their contractual targets in order to make a profit.

In this paper, we review the literature and explore the latest advancements regarding the automatisation of these processes to find the methods that yield the best results in the classification of bills of quantities and works in the construction industry.

Although full automation is not within our reach in the short term, especially due to the lack of standard construction specifications, machine learning can provide useful support tools. This communication is part of the authors’ study aiming to develop a framework and tool to automate the process of task classification in a construction contract.

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Acknowledgements

This work was financially supported by: Core Funding-UIDB/04708/2020 of CONSTRUCT-Institute of R&D in Structures and Constructions-funded by national funds through FCT/MCTES (PIDDAC). This work is also co-funded by the European Social Fund (ESF), through the Northern Regional Operational Programme (Norte 2020) [Funding Reference: NORTE-06–3559-FSE-000176].”

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Correspondence to Luís Jacques de Sousa .

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Jacques de Sousa, L., Poças Martins, J., Santos Baptista, J., Sanhudo, L. (2023). Towards the Development of a Budget Categorisation Machine Learning Tool: A Review. In: Gomes Correia, A., Azenha, M., Cruz, P.J.S., Novais, P., Pereira, P. (eds) Trends on Construction in the Digital Era. ISIC 2022. Lecture Notes in Civil Engineering, vol 306. Springer, Cham. https://doi.org/10.1007/978-3-031-20241-4_8

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  • DOI: https://doi.org/10.1007/978-3-031-20241-4_8

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