Abstract This paper presents a methodology based on Bayesian Networks (BN) to prioritise and select the minimal number of variables that allows predicting the structural condition of sewer assets to support the strategies in proactive management. The integration of BN models, statistical measures of agreement (Cohen's Kappa coefficient) and a statistical test (Wilcoxon test) were useful for a robust and straightforward selection of a minimum number of variables (qualitative and quantitative) that ensure a suitable prediction level of the structural conditions of sewer pipes. According to the application of the methodology to a specific case study (Bogotás sewer network, Colombia), it found that with only two variables (age and diameter) the model could achieve the same capacity of prediction (Cohen's Kappa coefficient = 0.43) as a model considering several variables. Furthermore, the methodology allows finding the calibration and validation percentage subsets that best fit (80% for calibration and 20% for validation data in the case study) in the model to increase the capacity of prediction with low variations. Furthermore, it found that a model, considering only pipes in critical and excellent conditions, increases the capacity of successful predictions (Cohen's Kappa coefficient from 0.2 to 0.43) for the proposed case study.

中文翻译：

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基于贝叶斯网络的方法，用于选择具有成本效益的下水道资产管理模型

摘要本文提出了一种基于贝叶斯网络 (BN) 的方法，用于优先考虑并选择最少数量的变量，从而预测下水道资产的结构状况，以支持主动管理策略。BN 模型、统计一致性度量（Cohen 的 Kappa 系数）和统计检验（Wilcoxon 检验）的集成对于稳健且直接地选择最少数量的变量（定性和定量）非常有用，可确保适当的预测水平污水管道的结构状况。根据该方法在具体案例研究（哥伦比亚波哥大下水道网络）中的应用，发现仅使用两个变量（年龄和直径），该模型就可以达到与考虑多个变量的模型。此外，该方法允许在模型中找到最适合的校准和验证百分比子集（案例研究中的校准数据为 80%，验证数据为 20%），以提高低变化预测的能力。此外，研究还发现，仅考虑处于关键和良好条件下的管道的模型可以提高所提出的案例研究的成功预测能力（Cohen 的 Kappa 系数从 0.2 到 0.43）。