• Korean Journal of Construction Engineering and Management
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• v.12 no.6
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• pp.65-78
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• 2011

Construction business, which is complex and long-term business, requires accurate estimation and verification in construction costs and payment procedure from project planning to the completion of construction phase. And more importantly, it is necessary to investigate and determine the risk factors related to construction costs during the entire process including design planning, construction drawings, and quantity calculating. But, currently, it is not seem to be adequate to cope with the risk and increased construction costs against the operational budget in terms of actual costs when screening and estimating the bidding cost of public apartment. Therefore, this study selected and analyzed 40 sites' report of construction completion account from 2004 to 2010 focused on the adequacy on the modification of contract and design planning and on the complication of the budget in the beginning of the project. This study deducted various risk causes and results by analyzing actual costs according to year, architectural area, region, construction cost and sale/lease classification. We could find out construction risk according to annual variation of government policy and economy, and also deducted risk items by construction characteristic according to region and architectural area. Study result, we first found out the problems of lowest price award system according to the construction costs. The weight of the cost increase risk was analyzed that subcontract and material costs are very high. Roof and tile work were analyzed highly in subcontract cost risk and reinforcing bar and cement were analyzed highly in material cost risk, among direct construction cost. Finally, this study results could be used in comparing the categories of the construction costs made by specific construction process, belonging to the construction costs, with the operational budget made in the beginning of the project that can enable to grasp unpredictable risks over the construction costs and making quantitative analysis for it through analyzing the range of fluctuation and variations led by the fluctuations in the actual construction costs.

• 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.