This website requires JavaScript.

Wastewater Pipe Rating Model Using Natural Language Processing

Sai Nethra BetgeriShashank Reddy VadyalaDr. John C. MattewsDr. Hongfang Lu
摘要
Closed-circuit video (CCTV) inspection has been the most popular techniquefor visually evaluating the interior status of pipelines in recent decades.Certified inspectors prepare the pipe repair document based on the CCTVinspection. The traditional manual method of assessing sewage structuralconditions from pipe repair documents takes a long time and is prone to humanmistakes. The automatic identification of necessary texts has received littleattention. By building an automated framework employing Natural LanguageProcessing (NLP), this study presents an effective technique to automate theidentification of the pipe defect rating of the pipe repair documents. NLPtechnologies are employed to break down textual material into grammatical unitsin this research. Further analysis entails using words to discover pipe defectsymptoms and their frequency and then combining that information into a singlescore. Our model achieves 95.0% accuracy,94.9% sensitivity, 94.4% specificity,95.9% precision score, and 95.7% F1 score, showing the potential of theproposed model to be used in large-scale pipe repair documents for accurate andefficient pipeline failure detection to improve the quality of the pipeline.Keywords: Sewer pipe inspection, Defect detection, Natural language processing,Text recognition
展开全部
图表提取

暂无人提供速读十问回答

论文十问由沈向洋博士提出,鼓励大家带着这十个问题去阅读论文,用有用的信息构建认知模型。写出自己的十问回答,还有机会在当前页面展示哦。

Q1论文试图解决什么问题?
Q2这是否是一个新的问题?
Q3这篇文章要验证一个什么科学假设?
0
被引用
笔记
问答