A Fuzzy Rule-based Neuro-Symbolic Approach for Pipe Severity Prediction in Sewer Networks· 下水道管道严重程度预测的模糊规则神经符号方法
Standard automated sewer pipe severity assessment relies on direct image classification, creating a "black box" where the link between visual defects and final severity scores remains implicit. This study introduces a modular, fuzzy rule-based neuro-symbolic framework that bridges this gap by decoupling neural perception from symbolic reasoning. The perception module utilizes a Swin Transformer to predict 14 multilabel inspection CODE degrees directly from images. For reasoning, a DT, specifically Weka's J48, algorithm is trained on ground-truth CODEs and severity labels, and its paths are converted into 19 fixed IF--THEN rules. Inference operates via fuzzy logic: t-norm activations from CODE conditions are weighted by rule confidence and combined with corresponding s-norms to produce inte
通过模糊规则神经符号方法预测下水道管道严重程度。
- 核心方法
- 提出了一种模块化的模糊规则神经符号框架，将神经感知与符号推理解耦。感知模块使用Swin Transformer从图像中预测14个多标签检查CODE等级，推理模块使用Weka的J48算法训练并转换成19条固定的IF-THEN规则，通过模糊逻辑进行推理。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 传统的下水道管道严重程度评估依赖于直接图像分类，导致视觉缺陷与最终严重程度评分之间的关系不明确。
- 关键实验
- 未提供
- 主要贡献
- 该方法弥补了视觉缺陷和严重程度评分之间的联系，提高了模型的可解释性和透明度。
- 意义与局限
- 意义在于提供了一种更加透明和可靠的管道严重程度预测方法，但实验部分较为薄弱，需要更多实际数据验证。