| Trial Application and Expected Outcome |
- Large-small language model co-adapter frameworks, with a coefficient variation-based difficulty grading mechanism to stratify training datasets by the samples’ degree of complexity, and with multi-stage curriculum learning strategies to progressively optimize the AI models toward advanced loss functions tailor-made for construction safety applications;
- Hybrid edge-cloud computing paradigms with staged analyses based on criticality-latency trade-off, for cost-effective resource utilization and timely incident reporting to safety managers;
- Integration of chain-of-thought prompting with agentic retrieval-augmented generation frameworks, for transforming risk identification into root cause reasoning and actionable mitigation planning, dynamically augmented by site-specific metadata (e.g. historical incidents, evolving project requirements or site conditions);
- Multi-stage time-series analytical frameworks for automated video-based anomaly detection for high-confidence tasks, integrated with continual feedback-and-correction mechanisms upon low-confidence edge cases, to refine the AI models’ site-specific knowledge for enhanced scene-adaptive generalization and contextual awareness of the AI models.
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