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Automatic logical inconsistency detection in the National Bridge Inventory

机译:国家桥梁库存中自动逻辑不一致检测

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Studies about the data quality of National Bridge Inventory (NBI) reveal missing, erroneous, and logically conflicting data Existing data quality programs lack a focus on detecting the logical inconsistencies within NBI and between NBI and external data sources. For example, within NBI, the structural condition ratings of some bridges improve over a period while having no improvement activity or maintenance funds recorded in relevant attributes documented in NBI. An example of logical inconsistencies between NBI and external data sources is that some bridges are not located within 100 meters of any roads extracted from Google Map. Manual detection of such logical errors is tedious and error-prone. This paper proposes a systematical 'hypothesis testing' approach for automatically detecting logical inconsistencies within NBI and between NBI and external data sources. Using this framework, the authors detected logical inconsistencies in the NBI data of two sample states for revealing suspicious data items in NBI. The results showed that about 1% of bridges were not located within 100 meters of any actual roads, and few bridges showed improvements in the structural evaluation without any reported maintenance records.
机译:关于国家桥梁库存(NBI)的数据质量的研究显示缺失,错误和逻辑冲突的数据现有数据质量计划缺乏对NBI和NBI和外部数据源之间的逻辑不一致的关注。例如,在NBI内,某些桥梁的结构条件率超过一段时间,同时没有在NBI中记录的相关属性中记录的改进活动或维护资金。 NBI和外部数据源之间的逻辑不一致的示例是一些桥梁不在从Google地图中提取的任何道路范围内的100米范围内。手动检测此类逻辑错误是乏味和错误的。本文提出了一种系统的“假设检测”方法,用于自动检测NBI内的逻辑不一致以及NBI和外部数据源之间的逻辑不一致。使用此框架,作者检测到两个示例状态的NBI数据中的逻辑不一致,以显示NBI中的可疑数据项。结果表明,大约1%的桥梁不在任何实际道路范围内的100米范围内,很少有桥梁在没有任何报告的维护记录的情况下显示结构评估的改进。

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