Regulations play an important role in assuring the quality of a building’s construction and minimizing its adverse environmental impacts. Engineers and the like need to retrieve regulatory information to ensure a building conforms to specified standards. Despite the availability of search engines and digital databases that can be used to store regulations, engineers, for example, are unable to retrieve information for domain-specific needs in a timely manner. As a consequence, users often have to deal with the burden of browsing and filtering information, which can be a time-consuming process. This research develops a robust end-to-end methodology to improve the efficiency and effectiveness of retrieving queries pertaining to building regulations. The developed methodology integrates information retrieval with a deep learning model of Natural Language Processing (NLP) to provide precise and rapid answers to user’s questions from a collection of building regulations. The methodology is evaluated and a prototype system to retrieve queries is developed. The paper’s contribution is therefore twofold as it develops a: (1) methodology that combines NLP and deep learning to be able to address queries raised about the building regulations; and (2) chatbot of question answering system, which we refer to as QAS4CQAR. Our proposed methodology has powerful feature representation and learning capability and therefore can potentially be adopted to building regulations in other jurisdictions.

法规在确保建筑物的质量并最大程度地减少其不利的环境影响方面发挥着重要作用。工程师等需要检索法规信息，以确保建筑物符合指定的标准。尽管可以使用用于存储法规的搜索引擎和数字数据库，但是工程师无法及时检索满足特定领域需求的信息。结果，用户常常不得不处理浏览和过滤信息的负担，这可能是一个耗时的过程。这项研究开发了一种健壮的端到端方法，以提高检索与建筑法规有关的查询的效率和有效性。所开发的方法将信息检索与自然语言处理（NLP）的深度学习模型集成在一起，以从一系列建筑法规中为用户的问题提供准确，快速的答案。对方法进行了评估，并开发了用于检索查询的原型系统。因此，本文的贡献是双重的，因为它开发了：（1）结合了NLP和深度学习的方法，能够解决有关建筑法规的疑问；（2）问答系统的聊天机器人，我们称为QAS4CQAR。我们提出的方法具有强大的功能表示和学习能力，因此有可能被其他司法管辖区采用以建立法规。