Enhancing sewer asset management using machine learning algorithms
department or school: Applied Science, Faculty of
department or school: Civil Engineering, Department of
data provider: DSpace
degree campus: UBCV
creator: Seng, Vannary
issued: 2024-04-10T21:34:00Z
issued: 2024
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: Closed-circuit television (CCTV) is widely employed for assessing defects in sewers. This thesis: i) develops a number of deep convolutional neural network (CNN)-based image classification models for identifying sewer defects in CCTV videos, ii) develops a number of Machine Learning (ML)-based models for predicting the likely occurrence of specific defects in sewer networks, as well as identifying the factors that contribute to these defects, and iii) analyses the model performances with data from multiple utilities. The image classification models are trained and tested using images extracted from CCTV inspection videos for three utilities. They are analysed for serving two purposes: conducting a quality assurance and quality control (QA/QC) assessment of manual inspection reports of previously inspected videos, and identifying defects in images from videos that have not yet been manually inspected. Models are developed based on three pre-trained CNN backbone architectures: MobileNet V3 Large, ResNet50, and ResNet101; two model configurations: one- and two-stage models; and grouped classes. Two-stage models result in higher performance compared with alternative approaches. For QA/QC, on average, the ResNet models outperform MobileNet V3 Large where model accuracies for Utilities A, B, and C, are 0.95, 0.70, and 0.98, respectively. For recognizing defects in videos not previously viewed, the optimal model for each of the three utilities does not perform well. However, all models possess the ability to distinguish between non-defective and defective images with accuracies higher than 0.50, where the Utility A model achieves an accuracy of 0.80. Multiple decision tree-based ML models that predict the likely occurrence of infiltration and structural defects are developed using data from CCTV inspections coupled with additional pipe information and Geographical Information System files for two utilities. The performance of the models is assessed using the area under the…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/87751
note: ENHANCING SEWER ASSET MANAGEMENT USING MACHINE LEARNING ALGORITHMS by Vannary Seng B.Eng., McGill University, 2019 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Civil Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2024 © Vannary Seng, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Enhancing Sewer Asset Management Using Machine Learning Algorithms submitted by Vannary Seng in partial fulfilment of the requirements for the degree of Master of Applied Science in Civil Engineering Examining Committee: Dr. Barbara J. Lence, Professor, Department of Civil Engineering, UBC Supervisor Dr. Steven Weijs, Associate Professor, Department of Civil Engineering, UBC Supervisory Committee Member iii Abstract Closed-circuit television (CCTV) is widely employed for assessing defects in sewers. This thesis: i) develops a number of deep convolutional neural network (CNN)-based image classification models for identifying sewer defects in CCTV videos, ii) develops a number of Machine Learning (ML)-based models for predicting the likely occurrence of specific defects in sewer networks, as well as identifying the factors that contribute to these defects, and iii) analyses the model performances with data from multiple utilities. The image classification models are trained and tested using images extracted from CCTV inspection videos for three utilities. They are analysed for serving two purposes: conducting a quality assurance and quality control (QA/QC) assessment of manual inspection reports of previously inspected videos, and identifying defects in images from videos that have not yet been manually inspected. Models are developed based on three pre-trained CNN backbone architectures: MobileNet V3 Large, ResNet50, and ResNet101; two model co…
has type: Thesis/Dissertation
date issued: 2024-05
is shown at: 10.14288/1.0441279
language: eng
degree discipline: Civil Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Lence, Barbara J.
type: Text
identifier URI: http://hdl.handle.net/2429/87751