• 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.

• Journal of Korean Society of Forest Science
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• v.113 no.3
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• pp.282-291
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• 2024

In Korea, pine forests are continuously declining as they mature. Along with vegetation succession, mushroom succession also occurs. The productivity of pine mushroom, a mycorrhizal species that was introduced as a source of short-term forest income in the late 20th century, is also declining. Although the forest fires was originally blamed as the main cause of pine forests decline, it has not been identified as a significant factor in the decline of pine mushroom productivity. Pine mushrooms are mainly produced in pine-dominant stands ranging in age classes from 3 to 8, with the highest productivity occurring between 4 and 6 classes. Accordingly, the aging of pine forests and their succession to mixed forests were evaluated as the most important factors contributing to the decrease in pine mushroom productivity. Since tending practices in pine mushroom-producing forests contribute to maintaining and increasing the productivity of pine mushrooms in mature pine forests, production can be sustained through proactive forest/soil management. In recent years, attempts have been made to cultivate pine mushrooms. The method of producing a mycorrhizae-infected pine tree by planting a pine in front of a fairy ring of Tricholoma matsutake and then transplanting it to create a new pine mushroom-producing stand has been successful. However, the method may cause damage to the fairy ring and has difficulties to preserve the viability of mycorrhizal fungus during transplantation. Mycorrhizae-inoculated seedling production is proposed as an alternative technology, but difficulties remain in keeping the fungus in the seedling during transplantation. Research on the microbiome of the fairy ring of T. matsutake is being conducted to solve this problem. Bacteria and fungi that contribute to the mycelial growth of T. matsutake have been discovered. It is expected that the era of industrial cultivation of pine mushroom will soon be possible through a microbial ecological approach.