Challenges of Intelligent Management Approaches in Construction Engineering

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 956

Special Issue Editors


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Guest Editor
Construction Management and Real Estate, Tongji University, Shanghai 200092, China
Interests: project management; intelligent operation and maintenance; building information modeling; knowledge management

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Guest Editor
College of Architectural Science and Engineering, Yangzhou University, Yangzhou 211189, China
Interests: intelligent management of construction safety; computer vision

Special Issue Information

Dear Colleagues,

Construction engineering often faces significant challenges related to multi-source data management, resulting from the diverse range of construction objects and processes involved. To improve management efficiency, various intelligent management tools, including building information modeling (BIM), construction process simulation, database construction and algorithm development have been implemented. Nevertheless, in practical engineering projects, intelligent management approaches still need to address several persistent issues, such as data fragmentation, model–practice discrepancy, complex management structure, inefficient communication, and suboptimal collaboration. Exploring and devising solutions to these problems is essential for bridging the gap between academic research and practical implementations of intelligent management approaches, ultimately contributing to their promotion. The primary objective of this Special Issue is to explore the contemporary challenges and advancements in intelligent management approaches within the field of construction engineering.

Prof. Dr. Guofeng Ma
Dr. Zhijiang Wu
Guest Editors

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Keywords

  • intelligent collaborative management
  • construction process simulation
  • multi-objective optimization algorithms
  • construction data integration and processing
  • smart construction platform
  • knowledge and communication management

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Published Papers (1 paper)

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Research

21 pages, 3456 KiB  
Article
A Semi-Automatic Ontology Development Framework for Knowledge Transformation of Construction Safety Requirements
by Zhijiang Wu, Mengyao Liu and Guofeng Ma
Buildings 2025, 15(4), 569; https://doi.org/10.3390/buildings15040569 - 13 Feb 2025
Viewed by 618
Abstract
Construction safety requirements (SRs), which serve as critical information encapsulating a wide range of safety-related issues, constitute a fundamental basis for effective construction safety management. The constraints of the complex information characteristics and uncertainty of knowledge migration, however, lead to the failure to [...] Read more.
Construction safety requirements (SRs), which serve as critical information encapsulating a wide range of safety-related issues, constitute a fundamental basis for effective construction safety management. The constraints of the complex information characteristics and uncertainty of knowledge migration, however, lead to the failure to transform most of the requirement information into effective knowledge. This study proposes a multi-stage knowledge transformation framework for realizing the transformation of SRs from abstract information to canonical knowledge, and it accurately completes the knowledge transformation through document matching, knowledge extraction, and knowledge representation. Meanwhile, a semi-automated model was introduced into this study to develop a domain ontology knowledge base for SRs and to represent each type of knowledge through class definitions. The proposed framework was validated by testing project documents collected from two types of building projects, and the results show that the RD-based association rules can accurately match documents associated with SRs and adapt to match different types of sentiment attribute documents. Moreover, the improved TF-IDF algorithm improved by 20% in precision and recall, showing that the algorithm can extract tacit knowledge by combining knowledge points. Further, the domain ontology knowledge base facilitates normative documentation and representation for each type of knowledge in SRs. Full article
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