Wastewater Pipe Rating Model Using Natural Language Processing
- URL: http://arxiv.org/abs/2202.13871v1
- Date: Tue, 22 Feb 2022 18:03:24 GMT
- Title: Wastewater Pipe Rating Model Using Natural Language Processing
- Authors: Sai Nethra Betgeri, Shashank Reddy Vadyala, Dr. John C. Mattews, Dr.
Hongfang Lu
- Abstract summary: Closed-circuit video (CCTV) inspection has been the most popular technique for visually evaluating the interior status of pipelines in recent decades.
The traditional manual method of assessing sewage structural conditions from pipe repair documents takes a long time and is prone to human mistakes.
This study presents an effective technique to automate the identification of the pipe defect rating of the pipe repair documents.
- Score: 0.0
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: 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
Related papers
- Lazy Layers to Make Fine-Tuned Diffusion Models More Traceable [70.77600345240867]
A novel arbitrary-in-arbitrary-out (AIAO) strategy makes watermarks resilient to fine-tuning-based removal.
Unlike the existing methods of designing a backdoor for the input/output space of diffusion models, in our method, we propose to embed the backdoor into the feature space of sampled subpaths.
Our empirical studies on the MS-COCO, AFHQ, LSUN, CUB-200, and DreamBooth datasets confirm the robustness of AIAO.
arXiv Detail & Related papers (2024-05-01T12:03:39Z) - Automatic Defect Detection in Sewer Network Using Deep Learning Based Object Detector [0.0]
A dataset with 14.7 km of various sewer pipes was annotated.
An object detector (EfficientDet-D0) was trained for automatic defect detection.
It was able to detect 83% of defects in the test set; out of the missing 17%, only 0.77% are very severe defects.
arXiv Detail & Related papers (2024-04-09T11:13:36Z) - MFL Data Preprocessing and CNN-based Oil Pipeline Defects Detection [0.0]
Application of computer vision for anomaly detection has been under attention in several industrial fields.
This work focuses on the research of the Magnetic Flux Leakage data and the preprocessing techniques.
In doing so, we exploited the recent convolutional neural network structures and proposed robust approaches.
arXiv Detail & Related papers (2023-09-30T10:37:12Z) - U-PASS: an Uncertainty-guided deep learning Pipeline for Automated Sleep
Staging [61.6346401960268]
We propose a machine learning pipeline called U-PASS tailored for clinical applications that incorporates uncertainty estimation at every stage of the process.
We apply our uncertainty-guided deep learning pipeline to the challenging problem of sleep staging and demonstrate that it systematically improves performance at every stage.
arXiv Detail & Related papers (2023-06-07T08:27:36Z) - Detecting automatically the layout of clinical documents to enhance the
performances of downstream natural language processing [53.797797404164946]
We designed an algorithm to process clinical PDF documents and extract only clinically relevant text.
The algorithm consists of several steps: initial text extraction using a PDF, followed by classification into such categories as body text, left notes, and footers.
Medical performance was evaluated by examining the extraction of medical concepts of interest from the text in their respective sections.
arXiv Detail & Related papers (2023-05-23T08:38:33Z) - Recognition of Defective Mineral Wool Using Pruned ResNet Models [88.24021148516319]
We developed a visual quality control system for mineral wool.
X-ray images of wool specimens were collected to create a training set of defective and non-defective samples.
We obtained a model with more than 98% accuracy, which in comparison to the current procedure used at the company, it can recognize 20% more defective products.
arXiv Detail & Related papers (2022-11-01T13:58:02Z) - Wastewater Pipe Condition Rating Model Using K- Nearest Neighbors [0.0]
This paper's goal is to classify a comprehensive pipe rating model based on a series of pipe physical, external, and hydraulic characteristics.
The proposed model is built according to the industry-accepted and used guidelines to estimate the overall condition.
arXiv Detail & Related papers (2022-02-22T17:32:45Z) - Deep Learning Based Steel Pipe Weld Defect Detection [0.0]
State-of-the-art single-stage object detection algorithm YOLOv5 is proposed to be applied to the field of steel pipe weld defect detection.
The experimental results show that applying YOLOv5 to steel pipe weld defect detection can greatly improve the accuracy, complete the multi-classification task, and meet the criteria of real-time detection.
arXiv Detail & Related papers (2021-04-30T11:15:13Z) - Towards Automatic Digital Documentation and Progress Reporting of
Mechanical Construction Pipes using Smartphones [0.0]
This manuscript presents a new framework towards automated digital documentation and progress reporting of mechanical pipes in building construction projects.
New methods were proposed to optimize video frame rate to achieve a desired image overlap; define metric scale for 3D reconstruction; extract pipes from point clouds; and classify pipes according to their planned bill of quantity radii.
arXiv Detail & Related papers (2020-12-20T15:53:34Z) - Text Mining to Identify and Extract Novel Disease Treatments From
Unstructured Datasets [56.38623317907416]
We use Google Cloud to transcribe podcast episodes of an NPR radio show.
We then build a pipeline for systematically pre-processing the text.
Our model successfully identified that Omeprazole can help treat heartburn.
arXiv Detail & Related papers (2020-10-22T19:52:49Z) - Predictive Analytics for Water Asset Management: Machine Learning and
Survival Analysis [55.41644538483948]
We study a statistical and machine learning framework for the prediction of water pipe failures.
We use a dataset containing the failure records of all pipes within the water distribution network in Barcelona, Spain.
The results shed light on the effect of important risk factors, such as pipe geometry, age, material, and soil cover, among others.
arXiv Detail & Related papers (2020-07-02T19:08:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.