Neuro-symbolic systems can improve both accuracy and interpretability in infrastructure assessment by separating perception (what defects are present) from reasoning (how defects determine severity), making decisions traceable to specific visual evidence.
This paper presents a neuro-symbolic system for assessing sewer pipe damage severity by combining a vision model (Swin Transformer) that detects visual defects with fuzzy logic rules derived from decision trees.