用机器学习预测污水管状态，神经网络比传统方法更准。
Machine Learning Models for Reinforced Concrete Pipes Condition Prediction: The State-of-the-Art Using Artificial Neural Networks and Multiple Linear Regression in a Wisconsin Case Study
- 用神经网络和线性回归融合管道年龄、直径等多因素建模。
- 神经网络R²达0.9066，显著优于线性回归的0.8474。
- 适合城市基建管理者和做模型可解释性研究的人。
美国老化污水管网总长210万公里，每年约发生7.5万次污水溢流，带来严重经济、环境与健康风险。传统检测方法与确定性模型无法应对管网退化随机性，而概率方法又依赖大量历史数据，常不完整。本研究通过人工神经网络（ANN）和多元线性回归（MLR）模型，整合管龄、材质、直径、环境因素及PACP评级等变量，提升管道状态预测精度。ANN采用ReLU激活函数和Adam优化，MLR使用正则化处理共线性问题，评估指标包括RMSE、MAE和R²。结果表明，ANN表现更优，R²达0.9066，有效捕捉非线性关系且保持泛化能力；而MLR具备更好可解释性，识别出残留物堆积为关键影响因子。分析显示，管长、管龄和管径是主要退化驱动因素，埋深、土质和管段影响较小。未来研究应探索混合模型，结合ANN精度与MLR可解释性，引入SHAP分析与迁移学习，提升基础设施管理效率与环境可持续性。
原文摘要 · Abstract (English)
The aging sewer infrastructure in the U.S., covering 2.1 million kilometers, encounters increasing structural issues, resulting in around 75,000 yearly sanitary sewer overflows that present serious economic, environmental, and public health hazards. Conventional inspection techniques and deterministic models do not account for the unpredictable nature of sewer decline, whereas probabilistic methods depend on extensive historical data, which is frequently lacking or incomplete. This research intends to enhance predictive accuracy for the condition of sewer pipelines through machine learning models artificial neural networks (ANNs) and multiple linear regression (MLR) by integrating factors such as pipe age, material, diameter, environmental influences, and PACP ratings. ANNs utilized ReLU activation functions and Adam optimization, whereas MLR applied regularization to address multicollinearity, with both models assessed through metrics like RMSE, MAE, and R2. The findings indicated that ANNs surpassed MLR, attaining an R2 of 0.9066 compared to MLRs 0.8474, successfully modeling nonlinear relationships while preserving generalization. MLR, on the other hand, offered enhanced interpretability by pinpointing significant predictors such as residual buildup. As a result, pipeline degradation is driven by pipe length, age, and pipe diameter as key predictors, while depth, soil type, and segment show minimal influence in this analysis. Future studies ought to prioritize hybrid models that merge the accuracy of ANNs with the interpretability of MLR, incorporating advanced methods such as SHAP analysis and transfer learning to improve scalability in managing infrastructure and promoting environmental sustainability.
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