Encoding Invisible Causation for Bridge Diagnostic Agents: Triple-Guided Retrieval-Augmented Fine-Tuning with QLoRA

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 $\stackrel{\mathtt{c}\mathtt{a}\mathtt{u}\mathtt{s}\mathtt{e}\mathtt{d}\mathtt{\_}\mathtt{b}\mathtt{y}}{\to }$ 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.