Related Experiment Video
Updated: Mar 17, 2026

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
Published on: February 1, 2020
Mixed-language expressions in construction safety violation and warning reports: A domain-specific framework for
Kyung-Su Kang1, Sang-Min Lee2, Han-Guk Ryu3
1Construction Engineering and Management Institute, Sahmyook University, Seoul, Republic of Korea.
None:
BackgroundConstruction sites generate large volumes of textual safety data, yet inconsistent terminology and mixed-language expressions (MLEs) reduce the reliability of analysis. Korean safety violation and warning reports (SVWRs), a localized form of safety observation reports, are often written with irregular spacing, abbreviations, and hybrid vocabulary, hindering systematic utilization for data-driven safety management.ObjectiveThis study aims to develop and validate a domain-specific text normalization framework to improve the linguistic consistency and analytical reliability of SVWRs.MethodsA dataset of 64,999 SVWRs collected from 39 construction sites in South Korea was analyzed. A rule- and dictionary-based normalization pipeline was designed to unify fragmented terms and standardize MLEs. Topic modeling was conducted using topic modeling with symmetric priors and eight topics aligned with national safety categories.ResultsNormalization increased topic-model coherence from 0.412 to 0.497 (20.6% improvement), clarifying risk structures across categories such as falls, electrical hazards, and fire prevention. It revealed co-occurring risk patterns previously obscured by inconsistent language use, demonstrating that linguistic preprocessing is crucial for reliable text-based safety analytics.ConclusionsThe proposed framework enhances both methodological reliability and practical applicability by converting fragmented field reports into standardized, analyzable data. Its dictionary-based architecture can be extended to other agglutinative or multilingual languages, supporting scalable and data-driven safety management in the construction industry.
Related Concept Videos
Survey Safety
Types of Errors: Detection and Minimization
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
Types of Reports II: Incident or Occurrence Report
Purposes:
In the healthcare industry, reports play a crucial role in documenting incidents within an agency. The primary objective of these reports is to ensure patient safety, uphold the...
Detection of Gross Error: The Q Test
Improving Translational Accuracy
Contaminants and Errors
Another key consideration is determining the appropriate number of samples required to...