## 1 Introduction  
In the realm of construction engineering, the established standards and specifications epitomize the industry's extensive research findings and accumulated practical experience. These norms serve as a fundamental basis for ensuring the meticulous design, execution, and final inspection of building projects [1]. Furthermore, they constitute a critical reference point for subsequent operation and maintenance activities. Adherence to these guidelines facilitates not only the structural integrity and safety of constructions but also optimizes their operational efficiency and longevity. Hence, such standards are indispensable for fostering excellence within the construction engineering domain [2, 3]. Due to the wide range of knowledge involved in construction engineering, the number of relevant norms and frequent updates make it a challenging task for engineers to master the knowledge about all existing standards and specifications. Against this background, the advent of the era of artificial intelligence (AI) provides the possibility to solve this problem. As one of the important achievements of AI technology, large language model (LLM) can become an ideal choice of knowledge service in the field of construction engineering. The applications of LLMs in the construction engineering field primarily encompass standard interpretation [4], design optimization [5], intelligent Q&A support [6], result prediction [7], risk control [8] and construction management [9], significantly enhancing efficiency, reducing costs, and driving industry innovation. Although some progress has been made in the application of construction engineering, there are still shortcomings in some specific application scenarios. The field of construction engineering is notably characterized by its high degree of professionalism and intricate standard framework, requiring practitioners to possess profound professional knowledge and strictly adhere to numerous standards. In addition, specific applications frequently require supplementary contextual information, which often leads to poor performance of general LLMs in those contexts. To solve these issues, researchers have proposed various technical solutions. Among them, Retrieval-Augmented Generation (RAG) technology stands out as an effective approach. By integrating a retrieval mechanism with generative models, RAG enables LLMs to not only respond based on learned knowledge but also retrieve the latest information from external databases in real time, thereby providing more accurate and up-to-date answers. However, within the RAG framework, traditional static text segmentation--such as dividing texts into fixed lengths based on paragraphs or word counts--can lead to semantic fragmentation, adversely affecting retrieval accuracy. To address this issue, the Entropy-Optimized Dynamic Text Segmentation (EDTS) method has been proposed. EDTS aims to optimize the segmentation process to maintain semantic integrity while improving retrieval efficiency. This is particularly beneficial for ensuring high-quality information retrieval and enhancing overall system performance. In response to the need for the knowledge bases that incorporate the latest standards and regulations in fields like construction engineering, this paper introduces a methodology for constructing a domain-specific knowledge base using LLMs and RAG technology. This approach employs the EDTS method, which adaptively determines chunk boundaries based on information entropy. The main contributions include:  
1. Proposing the use of RAG technology to enhance the ability of LLMs in construction engineering standards and facilitate the cost-effective and efficient establishment of a specialized knowledge base through the utilization of standard texts. 2. Presenting the entropy-optimized dynamic text segmentation (EDTS) method for standard texts to ensure the clarity and predictability of information within chunks of limited length. 3.