Romasha Nethmini Deveni Guruge

Ms. Romasha Nethmini Deveni Guruge


Research Focus & Metrics

Academic Career Length

2Years (2025-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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31 March 2025 in Buildings
Verified
Journal Article
Open access
PDF file
ISIshini Saparamadu
Ishini Saparamadu
Samad SepasgozarSamad Sepasgozar
Samad Sepasgozar
RGRomasha Nethmini Deveni Guruge
Romasha Nethmini Deveni Guruge
HJH. S. Jayasena
H. S. Jayasena
Ali DarejehAli Darejeh
Ali Darejeh
Balachandra Arachchige Isuru ErangaBalachandra Arachchige Isuru Eranga
Balachandra Arachchige Isuru Eranga
Frequent ambiguities in contract terms often lead to costly legal disputes and project delays in the construction industry. Large Language Models (LLMs) offer a promising solution, enhancing accuracy and reducing misinterpretations. As studies pointed out, many professionals use LLMs, such as ChatGPT, to assist with their professional tasks at a minor level, such as information retrieval from the Internet and content editing. With access to a construction regulation database, LLMs can automate contract interpretation. However, the lack of Artificial Intelligence tools tailored to industry regulations hinders their adoption in the construction sector. This research addresses the gap by developing and deploying a publicly available specialised chatbot using the ChatGPT language model. The development process includes architectural design, data preparation, vector embeddings, and model integration. The study uses qualitative and quantitative methodologies to evaluate the chatbot’s role in resolving contract-related issues through standardised tests. The specialised chatbot, trained on construction-specific legal information, achieved an average score of 88%, significantly outperforming ChatGPT’s 36%. The integration of a domain-specific language model promises to revolutionise construction practices through increased precision, efficiency, and innovation. These findings demonstrate the potential of optimised language models to transform construction practices.