RDI workflow
AI-Powered Safety Intelligence & SIF Detection
the evidence system's AI-powered video analytics layer autonomously analyses site footage in real time to detect Serious Injury and Fatality (SIF) precursors and dispatches automated alerts to the safety manager.
- Category
- Safety Monitoring & Incident Management
- Frequency
- Occasional
- Confidence
- Medium
- Evidence records
- 8
- Cost model
- Qualitative
Trigger, activity, conclusion
A safety violation or SIF precursor condition is detected by the AI system on a live camera feed.
Automated AI analysis of live the evidence system camera feeds to detect defined safety risk conditions and dispatch real-time alerts to designated safety personnel.
Alert received, condition investigated, ground operative intervenes, near-miss logged. Detection event recorded as a leading indicator.
Workflow steps
- Step 01
AI safety analytics module configured for the project — defining detection classes (PPE, zone intrusion, plant-person proximity, fall protection) and cameras.
Inferred - Step 02
Detection thresholds and alert rules set — specifying conditions that trigger immediate alert vs. observation, and which personnel receive notifications.
Inferred - Step 03
Live camera feeds continuously processed by the AI analytics engine during working hours.
Evidenced - Step 04
When a trigger condition detected, system generates a real-time alert including a screenshot or clip, camera location, and violation type — dispatched to safety manager.
Evidenced - Step 05
Safety manager receives the alert and reviews the attached footage clip to assess severity.
Evidenced - Step 06
If condition confirmed as a genuine violation, safety manager contacts site supervisor or nearest competent person by radio to intervene immediately.
Evidenced - Step 07
Ground operative locates the worker or activity in question and corrects the unsafe condition.
Inferred - Step 08
Incident logged as a near-miss in the safety management system, with the AI detection clip attached.
Inferred - Step 09
Safety performance data from the AI system aggregated into weekly/monthly intelligence report identifying systemic risk patterns.
Inferred - Step 10
Repeat offenders or persistent high-risk zones escalated to the project safety plan for structural intervention.
Inferred
Evidence records
AI-automated analysis of site footage to detect serious safety risks in real time — describing an autonomous detection system for SIF precursors rather than a human-reviewed monitoring workflow.
Real-time AI detection of high-risk safety conditions on site — confirming the distinct character of this workflow as an automated intelligence layer over live camera feeds.
The ability to remotely check work zones helped avoid multiple daily trips across the site.
ROI model
Qualitative workflow
This workflow is currently represented as a qualitative benefit. A parametric cost model should be added only when the assumption set is credible.