Risk-Based Decision-Making Modeling for Wastewater Pipes

The dissertation research work described here has three primary objectives under risk-based decision making. (1) The development of a comprehensive sewer pipe condition rating model that incorporates many environmental, structural, and hydraulic parameters. (2) The development of a sewer pipe deterioration model used to predict future overall condition states of the pipe, as well as determining the probability of failure at any given age of the pipe. (3) The development of a comprehensive consequence of failure model that assesses the consequence of sewer pipe failure using economic, social, and environmental cost factors. The Pipeline Assessment and Certification Program (PACP) was developed by the National Association of Sewer Service Companies, the industry-accepted protocol for condition rating sewer pipes in the US. The PACP method relies exclusively on visual inspections performed using Closed-Circuit Television (CCTV), where existing structural and operation and maintenance (O&M) defects are observed by certified operators. A limitation of the PACP method is that it does not use pipe characteristics, depth, soil type, surface conditions, pipe criticality, capacity, the distribution of structural defects, or history of preventative maintenance to determine the condition rating of the sewer pipe segment. Therefore, a comprehensive rating model with pipe characteristics, external characteristics, and hydraulic characteristics was developed. The calculating of a comprehensive rating is an entirely manual process. Therefore, this research work addresses this limitation of Analytical Hierarchy Process (AHP) and suggests AHP is not a suitable method to calculate comprehensive rating. Develops a faster calculation of a comprehensive rating model using and K-NN that incorporates pipe characteristics, environmental characteristics, and information about PACP structural score and PACP O&M score in hydraulic factors. Factors such as pipe age, pipe material, diameter, shape, depth, soil type, loading, carried waste, seismic zone, PACP structural score, and PACP O&M score are used. Our proposed model is applied to the data received from the City of Shreveport, LA, which is currently under a Federal Consent Decree. The results of a comprehensive rating model showed a below-average validity percentage because linear regression assumes a linear relationship between the input and output variables. Still, the relationship between response and the predictor is not linear for AHP to prove AHP is not a suitable method and satisfactory results for K-NN. As part of decision-making, for capital improvement planning and budgeting, the capacity to predict future sewer pipe conditions and potential breakdowns is essential. In contrast to the often-used Discrete Time Markov Chain approaches in the literature, the deterioration model created here uses a Continuous Time Markov Chain method to calculate the likelihood that a pipe will change from a better to a worse condition at given age. The consequence of the pipe\u27s failure is established to ascertain the risk of failure and to create a comprehensive framework for risk-based decision-making. To estimate the impact of the asset\u27s failure, the established consequence of failure model considers a significant number of economic, social, and environmental cost elements. For budgeting future capital projects and improvements, the CTMC model and failure consequences for sewers are useful

Wastewater pipe defect rating model for pipe maintenance using natural language processing

IntroductionClosed-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 structural wastewater 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. Computer Vision based Machine Learning models failed to estimate structural damage because they are not entirely understood and have difficulty providing high data needs. Hence, they have problems providing physically consistent findings due to their high data needs. Currently, a very small curated annotated image and video data set with well-defined, precisely labeled categories to test Computer Vision based Machine Learning models.MethodsThis study provides a valuable method to determine the pipe defect rating of the pipe repair documents by developing an automated framework using Natural Language Processing (NLP) on very small, curated annotated images, video data, and more text data. The text used in this study is broken into grammatical units using NLP technologies. The next step in the analysis entails using words to find the frequency of pipe defects and then classify them into respective defect ratings for pipe maintenance.Results and discussionsThe proposed model achieved 95.0% accuracy, 94.9% recall, 95% 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

Analytic Hierarchy Process is not a Suitable method for the Comprehensive Rating

The Pipeline Assessment and Certification Program (PACP) was developed by the National Association of Sewer Service Companies, the industry-accepted protocol for condition rating of sewer pipes in the US. The PACP method relies exclusively on visual inspections performed using Closed-Circuit Television (CCTV), where existing structural and operation and maintenance (O&M) defects are observed by certified operators. A limitation of the PACP method is that it does not use pipe characteristics, depth, soil type, surface conditions, pipe criticality, and capacity, nor the distribution of structural defects or history of preventative maintenance to determine the condition rating of the sewer pipe segment. Therefore, this research work addresses this limitation and develops a comprehensive rating model using Analytic Hierarchy Process(AHP) that incorporates pipe characteristics, environmental characteristics, and information about PACP structural score and PACP O&M score in hydraulic factors. Factors such as pipe age, pipe material, diameter, shape, depth, soil type, loading, type of carried waste, seismic zone, PACP structural score, and PACP O&M score are used. The results showed a below-average validity percentage because linear regression assumes a linear relationship between the input and output variables. Still, the actual relation between response and the predictor is not linear. Our proposed model is applied to the data received from the City of Shreveport, LA, which is currently under a Federal Consent Decree

General implementation of quantum physics-informed neural networks

Recently, a novel type of Neural Network (NNs): the Physics-Informed Neural Networks (PINNs), was discovered to have many applications in computational physics. By integrating knowledge of physical laws and processes in Partial Differential Equations (PDEs), fast convergence and effective solutions are obtained. Since training modern Machine Learning (ML) systems is a computationally intensive endeavour, using Quantum Computing (QC) in the ML pipeline attracts increasing interest. Indeed, since several Quantum Machine Learning (QML) algorithms have already been implemented on present-day noisy intermediate-scale quantum devices, experts expect that ML on reliable, large-scale quantum computers will soon become a reality. However, after potential benefits from quantum speedup, QML may also entail reliability, trustworthiness, safety, and security risks. To solve the challenges of QML, we combine classical information processing, quantum manipulation, and processing with PINNs to accomplish a hybrid QML model named quantum based PINNs

Wastewater Pipe Rating Model Using Natural Language Processing

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 recognitio