On-site text classification and knowledge mining for large-scale projects construction by integrated intelligent approach |
| |
| Affiliation: | 1. State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300350, China;2. College of Engineering, Louisiana State University, Baton Rouge, LA 70803, USA;3. China Three Gorges Corporation, Beijing 100038, China;1. State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China;2. College of Mechanical Engineering, Chongqing University, Chongqing 400044, China;3. School of Intelligent Manufacturing Engineering, Chongqing University of Arts and Sciences, Chongqing 402160, China;1. Engineering of Systems and Environment, University of Virginia, Charlottesville, VA 22903, United States;2. University of California Los Angeles, United States;1. Beijing Institute of Technology, No. 5 South Street, Zhongguancun, Haidian District, Beijing, China;2. The University of Oklahoma, 60 Parrington Oval, Norman, OK, USA |
| |
| Abstract: | 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. |
| |
| Keywords: | Large-scale projects construction Text classification Knowledge mining CNN Mutual information TF-IDF |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
