| Title |
Development of a Prediction System for Construction Arbitral Awards Based on Cases Using Generative AI and BERT |
| Authors |
Su Hyeon Choe ; Han Soo Kim |
| DOI |
https://dx.doi.org/10.6106/KJCEM.2026.27.2.003 |
| Keywords |
Construction Arbitration; Generative AI; GPT; BERT; Construction Arbitration Award Prediction System |
| Abstract |
Construction projects are prone to disputes due to the involvement of multiple stakeholders and complex site conditions. Among these disputes, conflicts between clients and contractors occur most frequently, often leading to substantial financial losses. Arbitration provides a streamlined and legally binding resolution mechanism, and it is widely adopted in the construction sector. The objective of this study is to develop a system for predicting arbitral awards. To achieve this, the system incorporates GPT-based labeling to structure raw case data and BERTbased embeddings to automatically retrieve semantically similar arbitration cases, which then serve as the basis for prediction. A three-run repeated evaluation with identical inputs showed an average cosine similarity greater than 0.89 across outputs, thereby confirming the semantic consistency of the system’s predictions. These results demonstrate that the proposed approach can serve as a practical natural language-based tool for predicting arbitration award outcomes in the construction industry. |