A large-scale project produces a lot of text data during construction commonly achieved as various management reports. Having the right information at the right time can help the project team understand the project status and manage the construction process more efficiently. However, text information is presented in unstructured or semi-structured formats. Extracting useful information from such a large text warehouse is a challenge. A manual process is costly and often times cannot deliver the right information to the right person at the right time. This research proposes an integrated intelligent approach based on natural language processing technology (NLP), which mainly involves three stages. First, a text classification model based on Convolution Neural Network (CNN) is developed to classify the construction on-site reports by analyzing and extracting report text features. At the second stage, the classified construction report texts are analyzed with improved frequency-inverse document frequency (TF-IDF) by mutual information to identify and mine construction knowledge. At the third stage, a relation network based on the co-occurrence matrix of the knowledge is presented for visualization and better understanding of the construction on-site information. Actual construction reports are used to verify the feasibility of this approach. The study provides a new approach for handling construction on-site text data which can lead to enhancing management efficiency and practical knowledge discovery for project management.

一个大型项目在施工过程中会产生大量的文本数据，通常作为各种管理报告来实现。在正确的时间拥有正确的信息可以帮助项目团队了解项目状态并更有效地管理施工过程。但是，文本信息以非结构化或半结构化格式呈现。从如此庞大的文本仓库中提取有用的信息是一项挑战。手动流程成本高昂，而且通常无法在正确的时间将正确的信息交付给正确的人。本研究提出了一种基于自然语言处理技术（NLP）的集成智能方法，主要涉及三个阶段。第一的，开发了基于卷积神经网络（CNN）的文本分类模型，通过分析和提取报告文本特征，对施工现场报告进行分类。第二阶段，通过互信息对分类后的施工报告文本进行改进的频率-逆文档频率（TF-IDF）分析，以识别和挖掘施工知识。第三阶段，基于知识共现矩阵的关系网络被呈现出来，用于可视化和更好地理解施工现场信息。实际施工报告用于验证这种方法的可行性。该研究为处理施工现场文本数据提供了一种新方法，可以提高项目管理的管理效率和实践知识发现。