Wastewater Pipe Rating Model Using Natural Language Processing
This addresses inefficiencies and errors in pipeline maintenance for utility managers, though it is incremental as it applies existing NLP methods to a new domain.
The study tackled the problem of manually assessing sewage pipe conditions from CCTV inspection documents by developing an automated NLP framework to identify pipe defect ratings, achieving over 94% accuracy and F1 score.
Closed-circuit video (CCTV) inspection has been the most popular technique for visually evaluating the interior status of pipelines in recent decades. Certified inspectors prepare the pipe repair document based on the CCTV inspection. The traditional manual method of assessing sewage structural conditions from pipe repair documents takes a long time and is prone to human mistakes. The automatic identification of necessary texts has received little attention. By building an automated framework employing Natural Language Processing (NLP), this study presents an effective technique to automate the identification of the pipe defect rating of the pipe repair documents. NLP technologies are employed to break down textual material into grammatical units in this research. Further analysis entails using words to discover pipe defect symptoms and their frequency and then combining that information into a single score. 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 the proposed model to be used in large-scale pipe repair documents for accurate and efficient pipeline failure detection to improve the quality of the pipeline. Keywords: Sewer pipe inspection, Defect detection, Natural language processing, Text recognition