• Korean Journal of Construction Engineering and Management
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• v.5 no.3 s.19
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• pp.120-127
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• 2004

In May 2004, Poland with nine other Eastern European countries joined the European Union(EU). After accession to the EU, Poland will accelerate the realization of investment in road sector, and wants to achieve important development of its road network condition in year 2005. Polish government is not able to afford the whole investment needed for construction of its motorways, so it wants to develop effective techniques of project funding based on the Public Private Partnerships(PPP). Without having high-performance transport networks, one country's economy cannot be competitive. It was the reason for establishing the Trans European Network(TEN) in the European Union. It creates the links, which are still missing between the fifteen countries of Western Europe and the new member countries like Poland. Consequently, Poland will also need to establish a proper transport infrastructure network. This paper will discuss how the PPP scheme can be applied for the transport infrastructure development in Poland as a EU member.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.11
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• pp.471-480
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• 2023

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.