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Surveying near highways, rough terrain, or power lines involves significant risks. Working along highways is particularly dangerous and requires the use of warning signs and flagmen. It is safest to avoid working directly on roads and use offsets whenever possible. When highway work is unavoidable, it must follow all safety guidelines. Surveyors should wear bright clothing, such as orange reflective vests, to ensure visibility to motorists, coworkers, and hunters. In construction zones, wearing...
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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
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Data-Driven Construction Safety Information Sharing System Based on Linked Data, Ontologies, and Knowledge Graph

Akeem Pedro1, Anh-Tuan Pham-Hang2, Phong Thanh Nguyen3

  • 1Center for Systems Engineering and Innovation, Imperial College London, London SW7 2BX, UK.

International Journal of Environmental Research and Public Health
|January 21, 2022
PubMed
Summary
This summary is machine-generated.

Construction safety data is often inaccessible, hindering accident prevention. A new system uses linked data and ontologies to improve sharing and reuse of accident information for better safety outcomes.

Keywords:
accident preventionconstruction safetydata-driveninformation sharingknowledge engineeringknowledge graphknowledge managementlinked dataontologysemantic web

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Area of Science:

  • Construction Safety Management
  • Data Science Applications
  • Industry 4.0

Background:

  • High accident, injury, and fatality rates persist in the construction industry.
  • Existing accident data is often inconsistently formatted, non-machine-readable, and inaccessible, limiting learning opportunities.
  • Advances in data science and Industry 4.0 offer potential for improved safety information sharing.

Purpose of the Study:

  • To address challenges in construction safety data accessibility and reusability.
  • To propose a novel information sharing system leveraging linked data, ontologies, and knowledge graphs.
  • To enable proactive accident prevention through enhanced data utilization.

Main Methods:

  • Developed an ontological approach to semantically model safety information and formalize accident case knowledge.
  • Implemented a multi-algorithmic approach for automatic conversion of accident data to Resource Description Framework (RDF).
  • Utilized the SPARQL protocol for enabling efficient query functionalities on the structured data.

Main Results:

  • Confirmed the effectiveness and efficiency of the proposed system through trials on 200 real accident cases.
  • Demonstrated significant improvements in information access, retrieval, and reusability.
  • Validated the system's capability to capture and disseminate insights from past mishaps.

Conclusions:

  • The developed system facilitates a new "open" information sharing paradigm for construction safety.
  • This approach has major implications for Industry 4.0 and data-driven safety management applications.
  • The system enhances the potential for learning from past accidents to proactively prevent future incidents.