A Rule-Based BIM Framework for Construction Fall Hazard Detection and Prevention in Bangladesh:A BNBC 2020-Grounded Approach.

 Introduction: In Bangladesh, falls from height cause the most fatal injuries within the construction industry accounting 13.7% of all fatal occupational injuries. And no significant improvement was observed. Despite the requirement for edge protection stipulated in the Bangladesh National Building Code (BNBC) 2020, no BIM safety framework was proposed in the literature to date, which would prevent falls from height during design phase considering BNBC 2020 requirements or consider number of incidents. The paper presents a rule-based BIM framework for fall hazard detection and prevention at the design stage. Methods: Following a Design Science Research Methodology (DSRM), and based on a PRISMA guided systematic literature review, a descriptive and statistical analysis was performed on a dataset comprising 419 fatal incidents reported over a ten-year period in the Safety and Rights Society (SRS) database resulting in the formulation of ten design requirements traceable to the Python-based ifcopenshell implementation used to test them against a sample building model. Results: Edge protection was absent from all 419 fatal worksites; general laborers comprised 85.9% of victims, with Dhaka district accounting for 51.3%. There was no difference in fall height between building types or worker categories; no annual trends.The rule-checker correctly identified all 6 seeded violations in the test model without any false positives. When applied on a real-world building model, the rule-checker identified 274 slab-edge violations as compared to 1 edge protection rule pass, highlighting a significant deficiency in the design does not present in the test scenario. Conclusions: These findings prove the framework's ability to identify design-stage hazards consistently, although its application across a greater range of cases is required to confirm the practical value of its findings and ensure that it is an affordable method of large-scale adoption in the context of limited resources.