This review paper presents the current state-of-the-art pertains to water pipe failure prediction and risk assessment, published in the last ten years (2009–2019). This paper has been motivated by ...the lack of comprehensive review articles that integrates water network failure and risk modeling. Some of the current practices reviewed the pipe condition and its failure. Others focused on the statistical prediction models, whereas the rest outlined failure prediction models of large diameter mains only. The mainstream of the current practice, highlighted in this paper characterizes the structural deterioration and failure rates using various statistical techniques, whereas the remainder of research covers a proliferation of machine learning and soft computing applications to forecast and model the pipeline risk of failure. The review offers descriptions of the models together with their proposed methodologies, algorithms and equations, contributions and drawbacks, comparisons and critiques, and types of data used to develop the models using the bibliographic review method. Finally, future work and research challenges are recommended to assist the civil engineering research community in setting a clear agenda for the upcoming research.
Effective functioning of water systems is critical to ensure the quality of human life. Therefore, failure prediction of water mains under climatic variations is necessary to avoid socio-economic and ...environmental losses. This paper aims to propose a hybrid model named STL-GC-LSTM for an accurate failure prediction of water mains under the impact of climatic variations. Firstly, the seasonal-trend decomposition based on Loess (STL) method is employed to decompose the failure time series. Next, significant climate variables are selected from the Granger causality (GC) test. Lastly, the final predicted failure of water mains is acquired by adding up the predictive results of the three components which are learned by Long Short-Term Memory (LSTM) models. Several evaluation metrics are used to assess the prediction performance. The results from a case study in Hong Kong imply that STL decomposition is promising for fully mining intrinsic properties of failure series. The developed hybrid models are effective in specifically identifying which component climatic variations exert influence on, and the final failure predictions show satisfactory agreement compared with peer models. This paper could provide an accurate estimation for failures of water mains ahead of time and be used as an essential complement to other numerical prediction models.
An accelerometer-based leak detection system El-Zahab, Samer; Mohammed Abdelkader, Eslam; Zayed, Tarek
Mechanical systems and signal processing,
08/2018, Volume:
108
Journal Article
Peer reviewed
Display omitted
•System for monitoring pressurized pipelines using vibration signals is proposed.•The wireless system relies on vibration signals measured by accelerometers.•Studied using three ...classification techniques that are: SVM, NB, and DT.•Established multiple levels of thresholds to differentiate between leak states.•The proposed system can predict the size of the leak.
Aging infrastructures, specifically pipelines, that were installed decades ago and currently operating under poor conditions are highly susceptible to the threat of leaks, which pose economic, health, and environmental risks. For example, in the year 2009, the state of Ontario lost 25% of its water supply solely due to leaks. Therefore, a need arises to develop an approach that allows condition monitoring and early intervention. This article proposes a model for a real-time monitoring system capable of identifying the existence of single event leaks in pressurized water pipelines. The model proposes that wireless accelerometers be placed within the network on the exterior of the valves connecting the pipelines. To test the viability of the proposal, experiments were performed on one-inch cast iron pipelines, one-inch and two-inch PVC pipelines using single event leaks and the results were displayed. The vibration signal derived from each accelerometer was assessed and analyzed to identify the Monitoring Index (MI) at each sensor. The data collected from experimentation were analyzed using support vector machines (SVM), Decision Tree (DT), and Naïve Bayes (NB). A leak threshold was determined such that if the signal increased above the threshold, a leak status is identified. The developed models showed promising results with 98.25% accuracy in distinguishing between leak states and non-leak states. The proposed model is aimed at presenting novel approaches to providing municipalities with an affordable real-time monitoring system capable of aiding them in early detection and facilitating the repair process of leaks.
The escalating water stress resulting from drought conditions in certain global regions underscores the imperative to minimize water losses, particularly within drinking water supply networks. One ...way to achieve this is by improving pipe monitoring systems to allow the early detection of possible structural collapse of the pipes. One type of pipe widely used in water mains is the prestressed concrete pipe, whose main cause of structural failure is the breakage of prestressing wires. This research paper analyses the ability of an easy-to-install distributed acoustic sensing (DAS) monitoring system using fibre optics to identify and locate the acoustic signal produced by the wire breaks in prestressed concrete pipes to make early detection of possible structural failures. For this purpose, a large experimental pipeline stretch was built (approximately 1 m in diameter and 40 m long) where wire breaks were simulated. Several variables were studied: the origin of the signal (to distinguish wire breaks from events of a similar nature), the location of the event in the pipe, the presence of background noise, the internal water pressure, the length of the prestressed wire not subject to bonding with the concrete and the presence of water in the pipe. The results showed that the DAS system could detect almost all events. In addition, two of the multiple parameters measured in the signals, the zero-crossing rate and the short-time energy, made it possible to precisely determine the signal’s origin and the event’s location. Another parameter measured, the duration of the signal in this case, made it possible to differentiate whether the events had occurred when the pipe was empty or full of water. These and other results in this paper present a highly promising perspective on using this DAS system in water main monitoring.
The resilience of water main networks highly depends on the capacity for identifying and fixing structural failures in the system as fast as possible. Given the buried nature of such systems, this ...will be hard and costly through manual or semi-automated inspections. In this paper, a data-driven method is applied to predict the failure of water mains in the City of Kitchener. Six machine learning prediction models were developed under two scenarios: global models, which consider the three dominant material types in the network; and the homogenous model, which considers only cast-iron pipes. The water main’s condition score, length, and criticality score were the most influential factors on the pipe failure. The random forest models outperformed the other machine learning models with an accuracy of 97.3% and an F1-score of 80.4%; the homogenous modeling showed superior performance than the global one with an F1-score of 86.0%. The results showed that more than 72% of breaks could have been potentially prevented by monitoring and upgrading only 8% of the network. The superiority of the developed models lies in their ability to predict pipe failures with the least number of false alarms.
Lead Water Service Lines LEWIS, CARRIE M.; COUILLARD, LON A.; KLAPPA, PATRICIA J. ...
Journal - American Water Works Association,
01/2017, Volume:
109, Issue:
1
Journal Article
Peer reviewed
A pilot study in Milwaukee, Wis., assessed lead levels before and after water main replacement and led to a new sampling and communication protocol for addressing possible increased lead at the taps ...of customers with lead service lines.
Deterioration modelling has been a bottlenecking step towards risk-informed asset management of municipal water distribution networks. To close the gap, we proposed a two-time-scale (TTS) point ...process model on a pipe level for modelling and prediction of water main breaks. This paper presents the characterization, statistical parameter estimation, probabilistic features, and application of the model. Combining Poisson and renewal models into one, the proposed TTS process is characterized by a conditional intensity function of two time variables—one in a pipe clock for overall pipe aging and the other in a repair clock for local renewal. As a result, different aging patterns including the complicated bathtub-type behaviour can be modelled. A novel statistical method that combines data augmentation and Markov Chain Monte Carlo was developed for model estimation to deal with partially missing event histories. A case study using real-life data collected from a regional municipality in Canada was presented to illustrate the application of the proposed model. The modelling process ranging from model estimation, verification, validation, and updating to application in asset management was thoroughly demonstrated. This study also demonstrated that one must use the full distributions of the parameters to obtain an unbiased prediction of mean number of water main breaks. The proposed model was also compared with the Poisson process model in terms of break intensity, survival probability, mean cumulative number of breaks, and mean annual number of breaks. The implications of the different results to asset management were carefully discussed as well. Last, the ability of the proposed model to capture the maintenance effectiveness of pipe repair was proven. This work represents a solid advancement towards holistic assessment of the aging risk of a municipal water distribution network.
•A two-time-scale point process model was proposed for modelling of water main breaks.•The model captures various aging patterns including bathtub behaviour.•A MCMC/DC algorithm considering partial missing break histories was proposed for parameter estimation.•A real life case study was presented to demonstrate the whole modelling process.
To develop an effective preventive or proactive repair and replacement action plan, water utilities often rely on water main failure prediction models. However, in predicting the failure of water ...mains, uncertainty is inherent regardless of the quality and quantity of data used in the model. To improve the understanding of water main failure, a Bayesian framework is developed for predicting the failure of water mains considering uncertainties. In this study, Bayesian model averaging method (BMA) is presented to identify the influential pipe-dependent and time-dependent covariates considering model uncertainties whereas Bayesian Weibull Proportional Hazard Model (BWPHM) is applied to develop the survival curves and to predict the failure rates of water mains. To accredit the proposed framework, it is implemented to predict the failure of cast iron (CI) and ductile iron (DI) pipes of the water distribution network of the City of Calgary, Alberta, Canada. Results indicate that the predicted 95% uncertainty bounds of the proposed BWPHMs capture effectively the observed breaks for both CI and DI water mains. Moreover, the performance of the proposed BWPHMs are better compare to the Cox-Proportional Hazard Model (Cox-PHM) for considering Weibull distribution for the baseline hazard function and model uncertainties.
•Prioritize rehabilitation and replacements (R/R) strategies of water mains.•Consider the uncertainties for the failure prediction.•Improve the prediction capability of the water mains failure models.•Identify the influential and appropriate covariates for different models.•Determine the effects of the covariates on failure.
Responding to Hazardous Material Spills Lawson, Raven
Journal - American Water Works Association,
July/August 2022, 2022-07-00, 20220701, Volume:
114, Issue:
6
Journal Article
