Research on ITB Contract Terms Classification Model for Risk Management in EPC Projects: Deep Learning-Based PLM Ensemble Techniques


KIPS Transactions on Software and Data Engineering, Vol. 12, No. 11, pp. 471-480, Nov. 2023
https://doi.org/10.3745/KTSDE.2023.12.11.471,   PDF Download:
Keywords: EPC Projects, ITB Documents, Deep Learning, PLM, ELECTRA
Abstract

The Korean construction order volume in South Korea grew significantly from 91.3 trillion won in public orders in 2013 to a total of 212 trillion won in 2021, particularly in the private sector. As the size of the domestic and overseas markets grew, the scale and complexity of EPC (Engineering, Procurement, Construction) projects increased, and risk management of project management and ITB (Invitation to Bid) documents became a critical issue. The time granted to actual construction companies in the bidding process following the EPC project award is not only limited, but also extremely challenging to review all the risk terms in the ITB document due to manpower and cost issues. Previous research attempted to categorize the risk terms in EPC contract documents and detect them based on AI, but there were limitations to practical use due to problems related to data, such as the limit of labeled data utilization and class imbalance. Therefore, this study aims to develop an AI model that can categorize the contract terms based on the FIDIC Yellow 2017(Federation Internationale Des Ingenieurs-Conseils Contract terms) standard in detail, rather than defining and classifying risk terms like previous research. A multi-text classification function is necessary because the contract terms that need to be reviewed in detail may vary depending on the scale and type of the project. To enhance the performance of the multi-text classification model, we developed the ELECTRA PLM (Pre-trained Language Model) capable of efficiently learning the context of text data from the pre-training stage, and conducted a four-step experiment to validate the performance of the model. As a result, the ensemble version of the self-developed ITB-ELECTRA model and Legal-BERT achieved the best performance with a weighted average F1-Score of 76% in the classification of 57 contract terms.


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Cite this article
[IEEE Style]
H. Lee, W. Lee, B. Jo, H. Lee, S. Oh, S. You, M. Nam, H. Lee, "Research on ITB Contract Terms Classification Model for Risk Management in EPC Projects: Deep Learning-Based PLM Ensemble Techniques," KIPS Transactions on Software and Data Engineering, vol. 12, no. 11, pp. 471-480, 2023. DOI: https://doi.org/10.3745/KTSDE.2023.12.11.471.

[ACM Style]
Hyunsang Lee, Wonseok Lee, Bogeun Jo, Heejun Lee, Sangjin Oh, Sangwoo You, Maru Nam, and Hyunsik Lee. 2023. Research on ITB Contract Terms Classification Model for Risk Management in EPC Projects: Deep Learning-Based PLM Ensemble Techniques. KIPS Transactions on Software and Data Engineering, 12, 11, (2023), 471-480. DOI: https://doi.org/10.3745/KTSDE.2023.12.11.471.