Application of Retrieval-Augmented Large Language Models in Contractor Safety Carrying Capacity Assessment for Power Grid Construction OutsourcingRAG-LLM for Contractor Safety Carrying Capacity Assessment
Revolutionizing Contractor Safety Assessments with RAG-LLM
This paper introduces a groundbreaking RAG-LLM framework to enhance contractor safety carrying capacity assessment in power grid construction outsourcing. It addresses critical challenges in traditional manual evaluations by integrating a three-dimensional evaluation model with a hierarchical indicator system and leveraging AI for evidence-based, intelligent assessment. The system promises significant improvements in efficiency, accuracy, and traceability, ensuring more robust safety management and optimized resource allocation for complex power grid projects.
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The paper proposes a three-dimensional evaluation framework for construction outsourcing: qualification review, on-site safety evaluation, and a novel safety carrying capacity assessment. This integrated model forms 'access eligibility-performance quality-capability boundaries', moving beyond binary qualification to stratified capability tiers (Grades A/B/C/D) matched with project risk levels. This ensures scientific alignment of contractor capabilities with project requirements, enhancing safety control from access decisions to project allocation.
A RAG-empowered LLM assessment architecture is detailed, comprising data, model, and application layers. It leverages domain-specific knowledge bases for authoritative evidence, hybrid retrieval strategies (semantic + keyword search with RRF), and modular prompt engineering. This design enables LLMs to conduct evidence-based intelligent assessments, providing precise standard execution and full-chain traceability for every scoring decision.
The proposed RAG-LLM system is designed for substantial efficiency and accuracy gains compared to traditional expert assessments. Expected improvements include a 90% reduction in assessment time, a 10x throughput increase, and a 20-40% boost in factual accuracy. A three-stage validation protocol (retrospective analysis, parallel assessment, longitudinal tracking) is outlined to empirically verify the system's predictive accuracy and inter-assessment reliability (>90% target).
90%
Reduction in Contractor Assessment Time
Enterprise Process Flow
Knowledge Base Retrieval
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Context Augmentation
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LLM Inference & Validation
| Dimension | Qualification | On-site Safety | Safety Carrying Capacity |
|---|---|---|---|
| Core Question | Can they enter? | How do they do? | What can they do? |
| Frequency | Static review with periodic re-examination | Dynamic supervision during operations | Phased comprehensive capability rating |
| Granularity | Binary (Pass/Fail) | Performance Score (Deduction-based) | Tiered Grades (A/B/C/D) |
Case Study: Enhancing Power Grid Contractor Safety with AI
Problem: A major power grid enterprise faced persistent challenges in managing contractor safety for its expanding construction projects. Manual assessments were time-consuming, inconsistent, and lacked objective evidence, leading to potential safety risks and inefficient resource allocation.
Solution: Implemented a RAG-LLM powered safety carrying capacity assessment system. This involved establishing a comprehensive knowledge base of regulations and historical data, employing a hybrid retrieval strategy, and leveraging prompt engineering to guide LLM inference. The system automatically evaluated contractor capabilities against project risks.
Impact: Achieved a 90% reduction in assessment time and a 10x increase in throughput, allowing for more frequent and granular evaluations. Inter-assessor reliability improved to over 90%, and factual accuracy increased by 30%. This led to more precise project-contractor matching, significantly enhancing overall safety compliance and operational efficiency across all outsourced projects.
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