Data-driven sewer asset management uses digital sewer representations to store inspection data and to support predictive maintenance planning. This approach requires asset managers to determine what inspection data they need to collect for the assessment of the asset conditions. Existing studies review sewer inspection methods based on their technical working principles but do not explicitly address what data about condition cues these methods provide. Consequently, literature lacks structured insights that help sewer asset managers link their data-needs with appropriate condition assessment methods. To make this link, we propose a data-needs based categorization of sewer inspection methods. Specifically, we relate data output of inspection methods to condition cues using the classification of hydraulic, structural, and environmental inspection domains. This shows that few methods exist to collect data about cues in structural and environmental domains. Future research should develop methods to satisfy these needs, and eventually, contribute to holistic data-driven asset management.

数据驱动的下水道资产管理使用数字下水道表示来存储检查数据并支持预测性维护计划。这种方法要求资产管理人员确定他们需要收集哪些检查数据来评估资产状况。现有研究根据其技术工作原理审查下水道检查方法，但没有明确说明这些方法提供的有关状况线索的数据。因此，文献缺乏有助于下水道资产管理者将他们的数据需求与适当的条件评估方法联系起来的结构化见解。为了建立这种联系，我们提出了一种基于数据需求的下水道检查方法分类。具体来说，我们使用液压、结构、和环境检查领域。这表明很少有方法可以收集有关结构和环境领域线索的数据。未来的研究应该开发满足这些需求的方法，并最终为整体数据驱动的资产管理做出贡献。