Water Pipeline Failure Research Articles

Application of the count-circle method to forecast the potential for fault, hanging-wall deformation based on water-pipeline failures: An example from California, USA

The Mw 6.7, 1994 Northridge earthquake (NE) produced water-pipeline failures (WPF) in California's highly urbanized San Fernando Valley (SFV) and Sylmar Basin (SB). The causative fault was a south-dipping blind thrust, one of several that underlie the SFV in the Los Angeles Basin with little, if any, obvious geomorphic expression. Prior NE studies indicated WPF are a proxy for tectonic permanent ground deformations (PGD) and secondary shaking hazards including liquefaction, differential settlement, and slope failure (Johnson and Shlemon, 2013, 2015). To determine the specific causes for WPF, we employed the count-circle method (CCM). The CCM is a novel analysis used to calculate WPF densities with known x, y locations and z values that are equal to one. The CCM analysis involves constructing a study area grid of square cells. A circle is then placed over the center of four adjacent cells and WPF within the circle are counted. This is followed by shifting the circle west to east and south to north until all WPF are counted. Adjacent circles exceed the recommended minimum 50% overlap. The resulting CCM data are then machine contoured to construct the WPF isopleths. These isopleths also enclose several faults associated with ground rupture stemming from the nearby 1971 San Fernando, California earthquake and as well as from others that have no evidence of Holocene slip. The isopleth analysis shows that many regional pipeline failures were associated with PGD caused by multi-fault, hanging-wall triggered slip and epicentral area fracture zones unrelated to known geologic structures. From our WPF documentation in southern California, we suggest that CCM analysis of WPF is a useful tool to forecast potential infrastructure damage and lifeline failures stemming from future blind thrusts and associated hanging-wall deformation.

A study of the water supply system failure in terms of the seasonality: analysis by statistical approaches

Abstract Among the many factors affecting water supply system failures are the weather conditions that change over the year. Since this is an important research issue, as part of this study, investigation of water supply system failure seasonality by the selected statistical approaches was presented. The basis of the research was monthly number of the pipelines' failures from the multi-year period of 2007–2017 of the municipal water network located in southern Poland. Mann–Kendall test results proved decreasing seasonal trend of the failure rate indexes λ. In turn, the results of the Colwell indexes' calculations allowed it to be stated that seasonal course of the water pipelines' failure events can be relatively easy to predict. As it turned out, it is difficult to determine unambiguously the impact of a given period of the year on the water pipeline failure events' occurrence. However, greater failure-free operation of the water pipelines may be expected in spring and summer months than in autumn and winter months. Because using Colwell indexes for seasonality analysis has no limitations compared to other methods, Colwell indexes may be considered as reliable tools for the assessment of the seasonal course of the water pipelines' failure events.

Prediction of Breaks in Municipal Drinking Water Linear Assets

Improper asset management practices increase the probability of water main failures due to inactive intervention actions. The annual number of breaks of each pipe segment is known as one of the most important criteria for the condition assessment of water pipelines. This metric is also considered one of the major performance measures in levels of service (LoS) studies. In an effort to maximize the benefits of historical data, this research utilized the evolutionary polynomial regression (EPR) method in determining the best mathematical expression for predicting water pipeline failures. The prediction model was trained and tested on the city of Montreal water network. After determining the best independent variables through the best subset regression, pipelines were clustered based on their attributes (length, diameter, age, and material). The majority of the models provided high R2 values, but the highest performing model’s R2 was 89.35%. Further, a sensitivity analysis was also performed and showed that the most sensitive parameter was the diameter, and the most sensitive material type to age was ferrous material. The tools and stages performed in this research showed promising results in predicting the expected water main failures using four different asset attributes. Therefore, this research can be implemented in asset management best practices and in LoS performance measures to predict the number of water pipeline failures. To further improve the prediction model, additional explanatory variables could be considered along with leveraging multiple artificial intelligence tools.

Water pipe failure prediction using AutoML

PurposeOwing to the consumption of considerable resources in developing physical pipe prediction models and the fact that the statistical models cannot fit the failure records perfectly, the purpose of this paper is to use data mining method to analyze and predict the risks of water pipe failure via considering attributes and location of pipes in historical failure records. One of the Automatized Machine Learning (AutoML) methods, tree-based pipeline optimization technique (TPOT) was used as the key data mining technique in this research.Design/methodology/approachBy considering pipeline attributes, environmental factors and historical pipeline broke/breaks records, a water pipeline failure prediction method is proposed in this research. Regression analysis, genetic algorithm, machine learning, data mining approaches are used to analyze and predict the probability of pipeline failure. TPOT was used as the key data mining technique. A case study was carried out in a specific area in China to investigate the relationships between pipeline broke/breaks and relevant parameters, such as pipeline age, materials, diameter, pipeline density and so on.FindingsBy integrating the prediction models for individual pipelines and small research regions, a prediction model is developed to describe the probability of water pipe failures and validated by real data. A high fitting degree is achieved, which means a good potential of using the proposed method in reality as a guideline for identifying areas with high risks and taking proactive measures and optimizing the resources allocation for water supply companies.Originality/valueDifferent models are developed to have better prediction on regional or individual pipeline. A comparison between the predicted values with real records has shown that a preliminary model has a good potential in predicting the future failure risks.

An innovative monitoring and maintenance model for the INFN CNAF Tier-1 data center infrastructure

During the last years we have carried out a renewal of the Building Management System (BMS) software of our data center with the aim of improving the data collection capability. Considering the complex physical distribution of the technical plants and the limits of the actual building hosting our center, a system that simply monitors and collects all the necessary information and provides alarms only in case of major failures has proven to be unsatisfactory. In 2017 we suffered a major flood due to one main water pipeline failure in the public street. After this disastrous event, clearly far beyond our control, we were however forced to reconsider completely the physical site robustness of our building in addition to the current monitoring and alarm system capabilities. It was clear that in some specific cases, alerts should be triggered hours or days before the actual main problem arises in order to allow efficient human intervention and proper escalation process. This paradigm could be easily applied to almost all the infrastructure components in our site, mainly the electric power distribution and continuity systems as well as the whole cooling devices. For this reason, in parallel to a consistent increase in the sensor widespread distribution of our BMS data collector system, a study of a predictive maintenance approach applicability to our site has been started. Predictive maintenance techniques aims at prevent unexpected infrastructure components failures or major events with the study of the whole monitoring data collection and the creation of appropriate statistical models with the help of big data analysis and machine learning techniques. An improvement in the Power Distribution Units (PDUs) monitoring in our site and the introduction of a dedicated network of water leak sensors were the first steps for increasing the data collection information at our disposal. With sufficient monitoring statistical information stored in our BMS system a preliminary and exploratory predictive data analysis proof of concept could be constructed. This could lead to the model building phase and the creation of a prototype with the aim of forecasting future infrastructure main failure events and forthcoming error conditions. The general idea is, conceivably, an approach to the predictive maintenance model where it would be possible to introduce scheduled corrective actions for the purpose of preventing potential failures in the next future and increasing the site overall reliability.

Method of improvement of operational and technical conditions of a large cooling water system

Increasing the energy efficiency of industrial installations is one of the European Union’s priorities for achieving energy policy goals. These goals can be achieved, among others, by applying the appropriate methodology for modernization of cooling water distribution pipelines and improving their operation. Water distribution in cooling systems of large industrial installations is associated with significant hydraulic losses due to large flows and spatial spread of these systems. The losses are unavoidable and have a decisive impact on the energy consumption for pumping. Thanks to optimal design solutions, implementation of the repair program and proper operation of cooling water transmission pipelines, it is possible to significantly reduce hydraulic losses and water leakage. This will translate into reduced energy consumption for pumping and, as a result, improved energy efficiency. Abovementioned goals can be achieved by replacing or renovating pipelines. This paper deals with determination of a method and schedule of modernization of cooling water piping systems on the basis of a case study – a large industrial plant. Firstly, evaluation of the existing condition is carried out. Data on flow rate and cooling water pressure in the system are collected and analyzed. A graphical and numerical database of the cooling water system is made, which maps the system in terms of system geometry (lengths, pipe diameters, ordinates) and flow and pressure streams. The hydraulic losses of the cooling water system are simulated. The results of simulation calculations of pressure losses in water distribution system are presented in the form of maps of water pressure distribution in pipelines. Calculations for the pipeline network are performed in the current state for two hydraulic load cases: maximum and average. An assessment of the failure rate is made on the basis of information about the place, time, cause and type of damage. Wall thickness of pipelines in selected locations is measured and samples are taken from pipes in places of failure. The reasons for water pipeline failures are diagnosed. On the basis of pre-modernization simulation, information on failure rate and forecasts of future water demand are obtained, it is proposed which pipeline sections and in what order should be modernized. Depending on the technical condition, pipeline diameter and location in the field, pipe replacement or renovation is recommended. For pipes to be replaced, new diameters, adjusted to the forecasted demand are calculated. For pipes qualified for renovation, different site hardened liners or full wall pipes are recommended depending on pipe condition. Renovation methods, despite the reduction of the internal cross-section, provide similar or lower hydraulic resistance values. After selecting the variants of modernization of distribution pipelines, hydraulic simulations are carried out in the post-modernization condition, taking into account the future demand for cooling water. The presented method can be applied to cooling water systems as well as other industrial water piping systems.

Water pipeline failure detection using distributed relative pressure and temperature measurements and anomaly detection algorithms

ABSTRACTThis paper presents the validation of a novel leak detection method for water distribution pipelines, although it could be applied to any buried pressurized fluid flow pipe. The detection method is based on a relative pressure sensor attached non-invasively to the outside of the pipe combined with temperature difference measurements between the pipe wall and the soil. Moreover, this paper proposes an anomaly detection algorithm, originally developed for monitoring website traffic data, which differentiates a ‘leak’ event from ‘normal’ pressure change events. It is compared to two more commonly used methods based on a fixed threshold and a moving average. The validation of the new system in a field trial over a 6-month period showed that all the known leaks were identified with 98.45% accuracy, with the anomaly detection algorithm performing best, making this system a real contender for leak detection in pipes.

A resilience-based prioritization scheme for water main rehabilitation

Water supply infrastructure in the United States is one lifeline system that is in dire need of huge financial investments to counter pipeline deterioration while keeping up with increasing demands and reliability goals. With decreasing financial resources available to state and local governments, effective decision-making tools for pipeline prioritization are becoming an increasingly integral part of the water utility industry. A majority of existing prioritization frameworks are merely based on the likelihood of the failure of pipelines and the resulting consequences, with little consideration given to the utility's response time to a water pipeline failure. This paper presents a novel resilience-based framework for effective prioritization of water distribution pipelines. The novelty lies in estimating the utility's response time to a pipeline failure. The proposed framework is demonstrated on a section of a real water distribution network in a coastal city of the United States. The pipeline priority results obtained are also compared with those from a more traditional risk-based prioritization scheme, and a reasonably significant difference has been observed. While availability of quality data is a challenge, this study brings to attention the importance of response time to water pipeline failures and demonstrates the merits of incorporating it in a prioritization scheme.

Water pipeline failure due to water hammer effects

ABSTRACTA numerical model has been established in order to simulate the propagation of pressure waves in water networks. The present model formulation is based on a system of partial hyperbolic differential equations. This system has been solved via the characteristics method. The current model provides the necessary data and the necessary damping of water hammer waves, taking into account the structure of the pipe network and the pressure loss. The numerical algorithm estimates the maximum pressure values resulting from the water hammer when closing valves in the network and consequently, the maximum stresses in the pipes have been calculated. In the case of simultaneous closing of several valves, the over pressure can exceed the admissible pressure. In this case, the severity of a defect such as a corrosion crater (pit) has been estimated by computing a safety factor for the stress distribution at the defect tip. This allows the applied notch stress intensity factor to be obtained. To investigate the defect geometry effects, semi‐spherical and semi‐elliptical defects are deemed to exist in up to one‐half of the thickness of the pipe wall. The outcomes have been introduced into the structural integrity assessment procedure (SINTAP) failure diagram assessment (FAD) in order to obtain the safety factor value. Conventionally, it is considered that a failure hazard exists if this safety factor is less than two.