Natural language processing for smart construction: Current status and future directions

Chengke Wu, Xiao Li, Yuanjun Guo, Jun Wang, Zengle Ren, Meng Wang, Zhile Yang

Research output: Journal article publicationReview articleAcademic researchpeer-review

81 Citations (Scopus)


Unstructured texts dominate data in construction projects. With the achievements of natural language processing (NLP) techniques, mining unstructured text data for smart construction has become increasingly significant. To understand state-of-the-art NLP for smart construction, uncover related issues, and propose potential improvements, this paper presents a comprehensive review of bottom-level techniques and mainstream applications of NLP in the industry. In total, 124 journal articles published in the last two decades are reviewed. NLP involves five core steps supported by various techniques, e.g., syntactic parsing, heuristic rules, machine learning, and deep learning. NLP has been applied for information extraction and exchanging and many downstream applications to facilitate management and decision-making. The role of NLP in smart construction and current challenges for fully reaping its benefits are discussed, and four research directions are identified, i.e., improving relation extraction, realising knowledge base auto-development, integrating multi-modal information, and achieving an accuracy-efficiency trade-off by developing an NLP application framework. It is envisioned that outcomes of this paper can assist both researchers and industrial practitioners with appreciating the research and practice frontier of NLP for smart construction and soliciting the latest NLP techniques.

Original languageEnglish
Article number104059
JournalAutomation in Construction
Publication statusPublished - Feb 2022


  • Artificial intelligence
  • Construction 4.0
  • Construction management
  • Data mining
  • Natural language processing
  • Project management
  • Review
  • Smart construction
  • Text mining

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Civil and Structural Engineering
  • Building and Construction


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