An Integrated Application of an NLP-Based Construction Engineering Specification Search Engine and AutoCAD Industrial Software

As the digital transformation of the construction industry progresses, engineering standards can no longer be adequately defined by outdated methodologies that fail to satisfy engineers' needs. This study presents a novel integration of Baidu's PaddlePaddle Natural Language Processing technology, utilizing the PaddleNLP engine, with AutoCAD software to provide a building engineering specification search engine with distinctive functionalities. It comprehends users' natural language input for searches, swiftly collects pertinent specifications from diverse sources, and accurately presents these specifications to meet the intended requirements. This investigation represents the inaugural research employing deep learning methodologies to categorize architectural needs and facilitate semantic search. Consequently, it facilitates substantial quickness and enhanced precision in the retrieval process. Creating a search engine focused on the investigation and testing of distributed computing frameworks, optimization of search algorithms, and adaptive user behavior models to tackle substantial data processing issues. The results indicate that the group utilizing the integrated technology solution exhibited a 60% increase in inquiry efficiency and improvements in engineering design quality and adherence to requirements. The investigations further enhanced the engine's interaction design, user feedback system, and algorithms to better accommodate the participants, necessitating a more intuitive and tailored user experience. This study offers clear and practical algorithmic explanations essential for addressing diverse building engineering specification challenges, while also advancing NLP technology through its integration into targeted applications.