OM-GPT: A Knowledge-Augmented and Fine-Tuned Large Language Model for Prefabricated Building Operation and Maintenance Management

The operation and maintenance (O&M) management of prefabricated buildings often struggles with fragmented knowledge and low reusability, relying predominantly on expert experience. While large language models (LLMs) offer a potential solution, their inherent hallucination issues significantly hinder practical application. To address these issues, this study proposes a knowledge base-augmented OM-GPT for prefabricated buildings O&M, built on a hybrid architecture that combines domain-specific fine-tuning with graph-based retrieval-augmented generation (GraphRAG). Specifically, it first fine-tuned the LLM Qwen2.5 using specialized O&M data to enhance its understanding of O&M tasks. It then constructed a multi-relational knowledge graph within a GraphRAG framework to effectively mitigate model hallucinations. Experimental results demonstrate that the Fine-Tuned Model achieved excellent Recall-Oriented Understudy for Gisting Evaluation (ROUGE) scores, validating the success of domain adaptation. In a five-dimensional evaluation, knowledge base-augmented OM-GPT significantly outperformed both GPT-4 and DeepSeek. Furthermore, two-way ANOVA tests confirmed the model’s advantages generalize across all five evaluation dimensions.