###### Abstract  
Building Information Modeling (BIM) has excellent potential to enhance building operation and maintenance. However, as a standardized data format in the architecture, engineering, and construction (AEC) industry, the retrieval of BIM information generally requires specialized software. Cumbersome software operations prevent its effective application in the actual operation and management of buildings. This paper presents BIMCoder, a model designed to translate natural language queries into structured query statements compatible with professional BIM software (e.g., BIMserver v1.5). It serves as an intermediary component between users and various BIM platforms, facilitating access for users without specialized BIM knowledge. A dedicated BIM information query dataset was constructed, comprising 1680 natural language query and structured BIM query string pairs, categorized into 12 groups. Three classical pre-trained large language models (LLMs) (ERNIE 3.0, Llama-13B, and SQLCoder) were evaluated on this dataset. A fine-tuned model based on SQLCoder was then trained. Subsequently, a fusion model (BIMCoder) integrating ERNIE and SQLCoder was designed. Test results demonstrate that the proposed BIMCoder model achieves an outstanding accurate matching rate of 87.16% and an Execution Accuracy rate of 88.75% for natural language-based BIM information retrieval. This study confirms the feasibility of natural language-based BIM information retrieval and offers a novel solution to reduce the complexity of BIM system interaction. BIM information retrieval; large language model; natural language based BIM operation 2022 15 7647 20  
CONNECT Edition etc.) based on complex structured query language systems [1]. The specialized terminologies and complex operations of existing software systems hinder effective interaction between non-expert users and BIM models. Such challenges directly cause many BIM models to be only used for visual presentation rather than to support building management. Furthermore, even experts often struggle to quickly locate required information from massive BIM data, especially in emergencies where inefficiencies in information retrieval can lead to severe consequences [2]. Therefore, optimizing BIM model interaction and making it more user-friendly is a critical issue that puts BIM models into real construction management work. Along with the rapid development of natural language processing, building a BIM semantic interaction system based on natural language question-answering is a potential way to reduce the interaction barriers of BIM systems for non-professional users [3]. Such a system does not require users to understand and apply professional programming languages and can respond to users' natural language queries in real time with automatic BIM information retrieval. In recent years, breakthroughs in large-scale pre-trained language modeling (LLM) have provided powerful tools for computers to understand human natural language. It shows excellent potential in language translation and offers efficient methods for generating structured query statements [4]. Many kinds of LLMs are currently employed in open-domain text generation, e.g., GPT, ERNIE, LLaMA, and Mistral. They exhibit strong language understanding and generation capabilities since they are trained on massive open-domain text data. This study demonstrates that with few-shot prompting or data fine-tuning, these models can seamlessly convert natural language queries into structured computer query language (SQL), the NL2 SQL tasks. However, for professional fields such as the construction and building industry, due to limited training data, LLMs inevitably have hallucination problems, which may generate fact errors. Based on the classical SQLCoder model, we trained a BIM-structured query generation model to handle this challenge. To enhance the feasibility of the fine-tuning SQLCoder model, BIMCoder is designed by applying the ERNIE 3.0 model as a Wrapper to preprocess the input of the fine-tuning SQLCoder.