An Inverse Transient Nonmetallic Pipeline Leakage Diagnosis Method Based on Markov Quantitative Judgment

Hao, Yongmei; Ma, Yan; Jiang, Juncheng; Zhang, Xing; Ni, Lei; Yang, Jian Guo

doi:10.1155/2020/9527836

Cited by 7 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2001, Wirahadikusumah et al [56] combined nonlinear optimization and Markov chains to establish a drainage pipe deterioration and breakage prediction model and proved the sensitivity to pipe age of Markov chains. Since then, many studies have gradually improved the application form and interpretation of Markov chains [85,92], and some researchers have established Markov chain engineering application models [90,[92][93][94], which have gradually established a mature and applicable drainage pipe health state evaluation O&M system. The cohort survival method is sensitive to pipe-age analysis and can be used to deduce the most likely time in the future for the target pipeline to enter a poorer health state using existing data, and can also provide an accurate schedule for pipeline maintenance and rehabilitation [84][85][86][87].…”

Section: Markov Chainsmentioning

confidence: 99%

Progress in Drainage Pipeline Condition Assessment and Deterioration Prediction Models

Zeng

Wang

et al. 2023

Sustainability

View full text Add to dashboard Cite

The condition of drainage pipes greatly affects the urban environment and human health. However, it is difficult to carry out economical and efficient pipeline investigation and evaluation due to the location and structure of drainage pipes. Herein, the four most-commonly used drainage pipeline evaluation standards have been synthesized and analyzed to summarize the deterioration and breakage patterns of drainage pipes. The common pipe breakage patterns are also summarized by integrating the literature and engineering experience. To systematically describe the condition of drainage pipes, a system of influencing factors for the condition of pipes, including physical, environmental, and operational factors, has been established, and the mechanism of action of each influencing factor has been summarized. Physical, statistical, and AI models and their corresponding representative models have been categorized, and the research progress of current mainstream drainage-pipe deterioration and breakage prediction models are reviewed in terms of their principles and progress in their application.

show abstract

Section: Markov Chainsmentioning

confidence: 99%

Progress in Drainage Pipeline Condition Assessment and Deterioration Prediction Models

Zeng

Wang

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…This will surely save time and difficulty when performing outdoor tests [25]. A mathematical model for forecasting the losses in pipes was created based on transient calculations [26][27][28]. As the flow is turbulent in most industrial applications, the current study seeks to provide a mathematical model that allows us to find the site of the leak in pipes that are transporting incompressible liquids, or moving in laminar systems or turbulent regimes.…”

Section: Mathematical Modelling Of Water Losses In Pipesmentioning

confidence: 99%

Evaluating the Pressure and Loss Behavior in Water Pipes Using Smart Mathematical Modelling

Altowayti

Othman

Tajarudin

et al. 2021

Water

View full text Add to dashboard Cite

Due to the constant need to enhance water supply sources, water operators are searching for solutions to maintain water quality through leakage protection. The capability to monitor the day-to-day water supply management is one of the most significant operational challenges for water companies. These companies are looking for ways to predict how to improve their supply operations in order to remain competitive, given the rising demand. This work focuses on the mathematical modeling of water flow and losses through leak openings in the smart pipe system. The research introduces smart mathematical models that water companies may use to predict water flow, losses, and performance, thereby allowing issues and challenges to be effectively managed. So far, most of the modeling work in water operations has been based on empirical data rather than mathematically described process relationships, which is addressed in this study. Moreover, partial submersion had a power relationship, but a total immersion was more likely to have a linear power relationship. It was discovered in the experiment that the laminar flows had Reynolds numbers smaller than 2000. However, when testing with transitional flows, Reynolds numbers were in the range of 2000 to 4000. Furthermore, tests with turbulent flow revealed that the Reynolds number was more than 4000. Consequently, the main loss in a 30 mm diameter pipe was 0.25 m, whereas it was 0.01 m in a 20 mm diameter pipe. However, the fitting pipe had a minor loss of 0.005 m, whereas the bending pipe had a loss of 0.015 m. Consequently, mathematical models are required to describe, forecast, and regulate the complex relationships between water flow and losses, which is a concept that water supply companies are familiar with. Therefore, these models can assist in designing and operating water processes, allowing for improved day-to-day performance management.

show abstract

“…16 Hence, it is important to apply optimization methods, especially those related to the classification of problems' complexity, 17 such as metaheuristics or bio-inspired techniques in order to provide high efficiency. [18][19][20] In this context, we aim to perform an adequate remote water pipeline monitoring system used to transport water from εAin Sebsebε wells to the Mdhilla2 Tunisian Chemical Industrial Group. We apply a model-based technique in which we make use of our WDS hydraulic model to detect and localize leakages under generated transient scenarios.…”

Section: Statement Of the Problemmentioning

confidence: 99%

A genetic algorithm‐based intelligent solution for water pipeline monitoring system in a transient state

Abdelhafidh

Fourati

2020

Concurrency and Computation

View full text Add to dashboard Cite

Water pipeline monitoring system becomes a relevant solution to cope with various pipeline hydraulic failures in order to save the environment from water losses. In this respect, cognitive water distribution system (WDS) combines Internet of Things (IoT) technology with Big Data generated by various connected objects and devices for reliable structural health monitoring of pipelines. Accordingly, designing a scalable WDS with smart leak detection and localization requires a serious study and an adequate planning. In this paper, we suggest a cognitive IoT-based architecture with adequate data collection based on the Apache Spark framework. Furthermore, our objective is to elaborate a hybrid mechanism that defines and performs accurate leakage identification and localization by taking into account both the steady-state and transient behavior of water. The bio-inspired sensitivity analysis of the water pressure profile based on the genetic algorithm ensures the effectiveness and the accuracy of the proposed monitoring solution.

show abstract

An Inverse Transient Nonmetallic Pipeline Leakage Diagnosis Method Based on Markov Quantitative Judgment

Cited by 7 publications

References 14 publications

Progress in Drainage Pipeline Condition Assessment and Deterioration Prediction Models

Progress in Drainage Pipeline Condition Assessment and Deterioration Prediction Models

Evaluating the Pressure and Loss Behavior in Water Pipes Using Smart Mathematical Modelling

A genetic algorithm‐based intelligent solution for water pipeline monitoring system in a transient state

Contact Info

Product

Resources

About