1,568 research outputs found

    Risk-cost optimization of buried pipelines using subset simulation

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    On the basis of time-dependent reliability analysis, a computational framework called subset simulation (SS) has been applied for risk-cost optimization of flexible underground pipeline networks. SS can provide better resolution for rare failure events that are commonly encountered in pipeline engineering applications. Attention in this work is devoted to scrutinize the robustness of SS in risk-cost optimization of pipelines. SS is first employed to estimate the reliability of flexible underground pipes subjected to externally applied loading and material corrosion. Then SS is extended to determine the intervention year for maintenance and to identify the most appropriate renewal solution and renewal priority by minimizing the risk of failure and whole life-cycle cost. The efficiency of SS compared to genetic algorithm has been demonstrated by numerical studies with a view to prevent unexpected failure of flexible pipes at minimal cost by prioritizing maintenance based on failure severity and system reliability. This paper shows that SS is a more robust method in the decision-making process of reliability-based management for underground pipeline networks

    Internal Corrosion Damage Mechanisms of the Underground Water Pipelines

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    Internal water pipe corrosion is a complicated problem due to the interaction of water quality parameters with pipe wall. This study presents investigations of internal pipe surface corrosion mechanisms related to water physicochemical. Samples of water and corrosion-damaged ductile cast iron (30+ years) and galvanized steel pipe (15-20 years) were collected at in-situ condition from Addis Ababa city water distribution system. Scanning electron microscopy and optical microscopy were used to examine the pipes' corrosion morphology and microstructures, respectively. Additionally, Mountains 9 surface analysis software was used for further pitting corrosion characterization.To identify the causes of internal pipe corrosion, water physicochemical analyses were conducted by using inoLab pH 7310P, DR 900, Palintest Photometer 7100, and Miero 800. Water physicochemical test indicates: CaCO3 is 77 - 215 ppm, pH is 7.05 – 7.86, total dissolved solids (TDS) is 84.10 -262.8 ppm, ClO2 is 0 – 0.5 ppm, and dissolved oxygen (80-81 ppb). From water test results, major causes of internal pipe corrosion damage mechanisms were identified as dissolved oxygen, CaCO3, TDS, ClO2,and resistivity of water which initiates a differential cell that accelerates pipe corrosion. Using Mountain 9 surface analysis software, corrosion morphology and pitting features were characterized. The outputs of this paper will be helpful for water distribution and buried infrastructure owners to investigate corrosion damage mechanisms at early stage. To manage corrosion mechanisms, water supply owners need to conduct frequent inspections, recording of pipe data, testing of water quality, periodic pipelines washing, and apply preventative maintenance.publishedVersio

    A Comparison of Water Main Failure Prediction Models in San Luis Obispo, CA

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    This study compared four different water main failure prediction models: a statistically simple model, a statistically complex model, a statistically complex model with modifications termed the 2019 model, and an age-based model. The statistically complex models compute the probability of failure based on age, size, internal pressure, length of pipe in corrosive soil, land use, and material of the. These two values are then used to prioritize a water main rehabilitation program to effectively use the municipality’s funds. The 2019 model calculates the probability of failure and consequence of failure differently than the statistically complex model by considering corrosive soil data instead of assuming all the pipes are in highly corrosive soil and average daily traffic volume data instead of using street classifications. The statistically simple model only uses the pipe age and material for probability of failure. The age-based model relies purely on the age of the pipe to determine its probability of failure. Consequences of failure are determined by the proximity of the pipe to highly trafficked streets, critical services, pipe replacement cost, and the flow capacity of the pipe. Risk of failure score is the product of the consequence of failure score and probability of failure score. Pipes are then ranked based on risk of failure scores to allow municipalities to determine their pipe rehabilitation schedule. The results showed that the statistically complex models were preferred because results varied between all four models. The 2019 model is preferred for long-term analysis because it can better account for future traffic growth using the average daily traffic volume. Corrosive soil data did not have a significant impact on the results, which can be attributed to the relatively small regression parameter for corrosive soil. The age-based model is not recommended because results of this study shows it places a significantly high number of pipes in the high and critical risk categories compared to the other models that account for more factors. This could result in the unnecessary replacement of pipes leading to an inefficient allocation of funds. Keywords: Risk of Failure, Consequence of Failure, Probability of Failur

    Prediction of Asbestos Cement Water Pipe Aging and Pipe Prioritization Using Monte Carlo Simulation

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    For buried Asbestos cement (AC) pipes in service, internal and external surface degradation occur by dissolution or leaching of cement-based components leading to loss of pipe strength. Since water quality and soil environment cannot be completely specified along a pipeline, a management methodology for AC water pipelines is required to estimate the probability of pipe failure as ageing proceeds. The paper describes the technique and its application to experimental data, which illustrates in three parts. First, the degradation rates in AC pipes are computed from 360 aggregated independent pipe segments residual strength test data taken from different pipe diameter sizes used in various water utilities locations in Thailand. Second, the predictions of service lifetime for AC pipes are estimated using Monte Carlo simulation in conjunction with the physical failure state formulations. Output from the simulation provides a number of failures recorded over time, which then allows the economic analysis for optimal pipe replacement scheduling. All is described in the third part. The end results can be used for water utilities to allocate government funds for future pipe maintenance activities.For buried Asbestos cement (AC) pipes in service, internal and external surface degradation occur by dissolution or leaching of cement-based components leading to loss of pipe strength. Since water quality and soil environment cannot be completely specified along a pipeline, a management methodology for AC water pipelines is required to estimate the probability of pipe failure as ageing proceeds. The paper describes the technique and its application to experimental data, which illustrates in three parts. First, the degradation rates in AC pipes are computed from 360 aggregated independent pipe segments residual strength test data taken from different pipe diameter sizes used in various water utilities locations in Thailand. Second, the predictions of service lifetime for AC pipes are estimated using Monte Carlo simulation in conjunction with the physical failure state formulations. Output from the simulation provides a number of failures recorded over time, which then allows the economic analysis for optimal pipe replacement scheduling. All is described in the third part. The end results can be used for water utilities to allocate government funds for future pipe maintenance activities

    Mechanistic Framework for Risk Assessment of Cast Iron Water Main Fractures due to Moisture-Induced Soil Expansion

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    North American water distribution networks are at significant risk of failure due to aging cast iron pipes. For instance, of the 650,000 kilometers of cast-iron pipes in active service in the United States and Canada, more than 80% are beyond their intended service life. These aging and deteriorated pipes are failing at an alarming rate (22 breaks per 100 km per year), resulting in significant disruption to drinking and emergency water supply. The capital investment gap to replace this inventory is too large and will likely take several decades to bridge at the current replacement rate of the order of 0.8% per year. Meanwhile, infrastructure managers rely on managing this gap through simplistic replacement prioritization, e.g., the oldest pipes are the most at risk. Such age-based prioritization schemes disregard multiple risk drivers that contribute to pipe failure. Risk-based decision support frameworks that go beyond simple prioritization schemes by considering multiple risk drivers are necessary to identify and prioritize the most at-risk segments of the network, thereby leading to the better management of the aforementioned gap. Previous studies showed that localized corrosion flaws, also known as pitting corrosion, on the external surface are primarily responsible for damage in pipes, and the strength of these deteriorated pipes to withstand loadings constitutes their stress capacity. On the other hand, the stresses caused by different loads on the pipe comprise stress demand. Field failure data indicate that the plausible failure mechanism is flexure which causes “full-circle breaks.” In the Central and Northern California region, where expansive soils are prevalent, a majority of these beaks (~ 60%) occurred during the months of high rainfall. This suggests that the plausible loading mechanism is moisture-induced differential soil expansion/contraction. Despite that, studies focused on flexural failures driven by differential soil expansion and the overall reliability of pipes situated in environments where potential for moisture-induced differential soil expansion/contraction exists have not been studied well. In this thesis, a probabilistic framework is developed for the assessment of pipe-soil systems vulnerable to fracture caused by a combination of pitting corrosion and moisture-induced soil expansion. The main objectives of this thesis are twofold. First, a physics-based approach is employed to develop an analytical soil-pipe interaction model that can predict full-circle breaks given a range of parameters, such as pipe configuration, soil conditions, and triggering factors (soil expansion). The model is based on classical solutions for beams on elastic foundations that are enriched to reflect material nonlinearities in the soil medium. The model development and comparision are supported by a suite of continuum finite-element simulations that simulate detailed interactions between the pipe and soil. The proposed analytical model demonstrated that it is able to reproduce flexural stresses in a range of pipe configurations with good accuracy and in a fraction of the computational time compared to detailed finite-element models. Next, a risk-based assessment methodology is developed which builds upon this pipe-soil interaction model along with corrosion equations estimating pitting damage in the pipe wall. The sources of uncertainty (uncertainties in various input parameters and the model itself) in all the components are rigorously analyzed and characterized. Subsequently, stochastic simulations employing Monte Carlo procedure is implemented to synthesize various uncertainties into a probabilistic estimate of the failure of a pipe segment, defined by its configurational parameters and age. The prospective use of this is outlined in the context of decision-support frameworks to prioritize replacement. In summary, this thesis presents a physics-based approach to help identify the most at-risk cast iron main pipes given a combination of configurational, locational, and seasonal factors. The outcome of the research is (1) a computationally inexpensive pipe-soil interaction model for pipes experiencing moisture-induced differential soil expansion loading and (2) a vulnerability assessment framework for a pipe segment given its various characteristics and environmental/loading factors. This approach may be conveniently used by utility operators within a decision support framework for asset management and the prioritization of replacement

    Failure analysis of underground pipeline subjected to corrosion

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    Underground pipes are essential infrastructure for the transport of water, oil and gas. The presence of localised pitting corrosion has been identified as one of the main deterioration mechanisms for metal pipes. When exposed to external loadings, these corroded pipes can easily fail due to intensified stresses at the corrosion pit. Disruptions to pipelines not only greatly affect the life of citizens, but also cause severe economic loss and pose safety risk. Therefore, accurate prediction of safe design life of buried pipes is significant. The main objective of this research is to investigate the effect of corrosion on the mechanical properties of cast iron pipes. A relatively long-term corrosion test was conducted on cast iron pipe in a corrosive clay soil. The corrosion behaviour of pipes was thoroughly examined using various corrosion techniques. At designated points of time, fracture toughness tests were conducted on single-edge bend specimens that were cut from the pipe wall. The results showed effective outcomes for corrosion behaviour in buried pipes and mechanical properties deterioration. A new three-dimensional geometrical model for sharp corrosion pits is proposed. The domain integral method has been employed, in conjunction with a three-dimensional finite element analysis, to derive the stress intensity factors for pipes. An expression of the maximum stress intensity factors has been developed for corroded pipes and the upcrossing method is employed to quantify the probability of fracture failure. This thesis concludes that both the mechanical properties and microstructure of material are changed due to corrosion. The proposed stochastic model of stress intensity factor can serve as a useful tool to predict the failure of buried cast iron pipes with improved accuracy. This research work will enhance the current knowledge of corrosion and mechanical property degradation of metal pipes and improve estimations of the remaining safe life of buried pipelines

    Pipe failure prediction and impacts assessment in a water distribution network

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    Abstract Water distribution networks (WDNs) aim to provide water with desirable quantity, quality and pressure to the consumers. However, in case of pipe failure, which is the cumulative effect of physical, operational and weather-related factors, the WDN might fail to meet these objectives. Rehabilitation and replacement of some components of WDNs, such as pipes, is a common practice to improve the condition of the network to provide an acceptable level of service. The overall aim of this thesis is to predict—long-term, annually and short-term—the pipe failure propensity and assess the impacts of a single pipe failure on the level of service. The long-term and annual predictions facilitate the need for effective capital investment, whereas the short-term predictions have an operational use, enabling the water utilities to adjust the daily allocation and planning of resources to accommodate possible increase in pipe failure. The proposed methodology was implemented to the cast iron (CI) pipes in a UK WDN. The long-term and annual predictions are made using a novel combination of Evolutionary Polynomial Regression (EPR) and K-means clustering. The inclusion of K-means improves the predictions’ accuracy by using a set of models instead of a single model. The long-term predictive models consider physical factors, while the annual predictions also include weather-related factors. The analysis is conducted on a group level assuming that pipes with similar properties have similar breakage patterns. Soil type is another aggregation criterion since soil properties are associated with the corrosion of metallic pipes. The short-term predictions are based on a novel Artificial Neural Network (ANN) model that predicts the variations above a predefined threshold in the number of failures in the following days. The ANN model uses only existing weather data to make predictions reducing their uncertainty. The cross-validation technique is used to derive an accurate estimate of accuracy of EPR and ANN models by guaranteeing that all observations are used for both training and testing, and each observation is used for testing only once. The impact of pipe failure is assessed considering its duration, the topology of the network, the geographic location of the failed pipe and the time. The performance indicators used are the ratio of unsupplied demand and the number of customers with partial or no supply. Two scenarios are examined assuming that the failure occurs when there is a peak in either pressure or demand. The pressure-deficient conditions are simulated by introducing a sequence of artificial elements to all the demand nodes with pressure less than the required. This thesis proposes a new combination of a group-based method for deriving the failure rate and an individual-pipe method for evaluating the impacts on the level of service. Their conjunction indicates the most critical pipes. The long-term approach improves the accuracy of predictions, particularly for the groups with very low or very high failure frequency, considering diameter, age and length. The annual predictions accurately predict the fluctuation of failure frequency and its peak during the examined period. The EPR models indicate a strong direct relationship between low temperatures and failure frequency. The short-term predictions interpret the intra-year variation of failure frequency, with most failures occurring during the coldest months. The exhaustive trials led to the conclusion that the use of four consecutive days as input and the following two days as output results in the highest accuracy. The analysis of the relative significance of each input variable indicates that the variables that capture the intensity of low temperatures are the most influential. The outputs of the impact assessment indicate that the failure of most of the pipes in both scenarios (i.e. peak in pressure and demand) would have low impacts (i.e. low ratio of unsupplied demand and small number of affected nodes). This can be explained by the fact that the examined network is a large real-life network, and a single failure of a distribution pipe is likely to cause pressure-deficient conditions in a small part of it, whereas performance elsewhere is mostly satisfactory. Furthermore, the complex structure of the WDN allows them to recover from local pipe failures, exploiting the topological redundancy provided by closed loops, so that the flow could reach a given demand node through alternative paths

    Kernel-specific Gaussian process for predicting pipe wall thickness maps

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    Data organised in 2.5D such as elevation and thickness maps has been extensively studied in the fields of robotics and geostatistics. These maps are typically a probabilistic 2D grid that stores an estimated value (height or thickness) for each cell. Modelling the spatial dependencies and making inference on new grid locations is a common task that has been addressed using Gaussian random fields. However, inference faraway from the training areas results quite uncertain, therefore not informative enough for some applications. The objective of this re- search is to model the status of a pipeline based on limited and sparse local assessments, predicting the likely condition on pipes that have not been inspected. A customised kernel for Gaussian Processes (GP) is proposed to capture the spatial correlation of the pipe wall thickness data. An estimate of the likely condition of non-inspected pipes is achieved by con-cretising GP to a multivariate Gaussian distribution and generating realisations from the distribution. The performance of this approach is evaluated on various thickness maps from the same pipeline, where data have been obtained by measuring the actual remaining wall thickness. The output of this work aims to serve as the input of a structural analysis for failure risk estimation
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