BIM-Integrated Semantic Risk Intelligence for Construction
Safety Severity Prediction Using Incident Narratives and 4D
Work-Zone Attributes
Esam El-Mekawy1,*
1School of Science, Engineering and Environment, University of Salford, UK
Emails: esam.elmekawy@gmail.com
Received: December 06, 2025 Revised: January 10, 2026 Accepted: February 16, 2026 ⋆ Corresponding author
ABSTRACT
Construction safety management increasingly depends on the ability to connect static building information models
with dynamic evidence from site operations. This paper proposes a BIM-integrated semantic risk intelligence model
that translates accident narratives into work-zone risk indicators and uses them to infer safety severity. The model
links textual incident evidence with BIM-relevant descriptors, including construction phase, spatial zone, temporary
protection status, energy isolation, and proximity to safety constraints. A formal risk-scoring layer is combined
with supervised severity learning to provide interpretable decision support for safety planning and 4D coordination.
The study contributes a reproducible methodology for converting unstructured safety reports into BIM-actionable
risk representations, supporting early hazard prioritisation, design-for-safety review, and site-control planning. The
findings indicate that semantic evidence becomes more useful when it is explicitly fused with BIM phase and spatial
context, rather than being treated as disconnected textual data.
Keywords: Building Information Modeling Construction safety Semantic risk intelligence 4D BIM Injury severity
prediction Machine learning Safety analytics