AI 中文总结
研究针对桥梁损伤原因难以肉眼察觉及专家诊断依赖隐性知识的问题，提出基于QLoRA的三重引导检索增强微调方法（含知识三元组提取等组件），能在消费级硬件上实现内存高效、高精度的桥梁诊断代理。
AI 中文摘要
桥梁基础设施会逐渐恶化，但其根本原因，如盐分侵入、冻结、疲劳开裂等，肉眼难以察觉。专家诊断依赖多年实践积累的隐性知识。我们提出一种损伤原因编码器，通过可见损伤描述\(S_i\)对10类损伤原因进行分类，以应对自动进行潜在因果推理的挑战。我们的方法包含三个组件：知识三元组提取，从15 - 35本诊断PDF手册中提取因果三元组并索引；检索增强上下文，在训练和推理时检索相关三元组并与\(S_i\)连接；系统微调比较，在固定黄金测试集上比较LoRA、QLoRA和QA - LoRA，结果表明QLoRA实现了最佳权衡。还引入了一个可重复使用的基准黄金测试集。这些发现有助于在消费级硬件上实现内存高效、高精度的边缘部署诊断代理。
英文摘要
Bridge infrastructure deteriorates gradually, yet its root causes---salt intrusion, freezing, fatigue cracking, and others---remain invisible to the naked eye. Expert diagnosis relies on tacit knowledge built over years of practice. We address the challenge of automating this latent causal reasoning by proposing a Damage Cause Encoder that classifies 10-class damage causes from visible damage descriptions $S_i$ for use in autonomous bridge diagnostic agents. Our approach chains three components: (i)Knowledge Triple Extraction---a large language model extracts causal triples of the form (damage $\xrightarrow{\mathtt{caused\_by}}$ cause) from 15--35 diagnostic PDF manuals and indexes them in a FAISS vector store; (ii)Retrieval-Augmented Context---at training and inference time, relevant causal triples $\mathcal{C}_i$ are retrieved and concatenated with $S_i$, converting implicit domain knowledge into explicit Encoder context; (iii)Systematic Fine-tuning Comparison---we conduct a rigorous comparison of LoRA, QLoRA, and QA-LoRA on a fixed Golden Testset (116 stratified samples), demonstrating that QLoRA achieves the optimal trade-off: identical test accuracy (87.07%) to full-precision LoRA, 11% faster inference, 72% lower GPU memory, and superior generalization across diverse unseen inputs. A controlled Golden Testset---stratified, deduplicated, and difficulty-tagged---is introduced as a reusable benchmark contribution. QLoRA further outperforms LoRA by 13 percentage points on a 100-sample diverse evaluation spanning all 10 damage cause classes.These findings enable memory-efficient, high-accuracy diagnostic agents on consumer-grade hardware for edge deployment.
Comments13 pages, 7 figures, 6 tables
