Pipe Deterioration Research Articles

Consistent Classification System for Sewer Pipe Deterioration and Asset Management

Consistent Classification System for Sewer Pipe Deterioration and Asset Management

The monitoring approaches and non-destructive testing technologies for sewer pipelines.

Sewage pipe deterioration has been one of the factors causing huge asset losses and urban hazards worldwide. For more efficient and reliable management, a variety of monitoring and non-destructive testing techniques has been developed for defect inspection and condition assessment of sewer pipes. In the present paper, the monitoring approaches of sewage pipes in the form of operational monitoring, structural monitoring, and durability monitoring are outlined. The fundamentals and features of various non-destructive testing (NDT) techniques for different target detect locations are presented. The stereo vision, light detection and ranging (LiDAR), and laser 3D scanning technologies that might serve to architect the digital twin of the pipeline are also described. What's more, the capabilities and limitations of these technologies are discussed and summarized in tables. Some possible visions for the development of inspection and quantitative evaluation of sewage pipes are also discussed. In practice, it is suggested that visual inspection techniques are the most feasible for the evaluation of underground pipes. In terms of quantitative and automated evaluation, visual inspection robots equipped with stereo vision or laser 3D scanning technology are the most promising.

Application of the Harmony Search Algorithm for Optimization of WDN and Assessment of Pipe Deterioration

The relevance of the optimal design of water distribution networks lies in its applicability. Thus, the design must be as efficient and affordable as possible, meaning that it achieves a minimum level of serviceability using an appropriate amount of resources. In this context, an ideal water network requires the use of minimum-size elements to reach the minimum head pressure required for each node of the system with the lowest energy consumption. For this purpose, a relatively new meta-heuristic algorithm, called Harmony Search, was used in this study to optimize the water distribution network of Cheongna International City, Korea, in terms of the pipe diameter. Furthermore, using the same algorithm under a different approach, an assessment of the deterioration of conduits over time was conducted by analyzing the optimal roughness coefficient for the original pipe material, which provides a useful tool for decision making. Other heuristic and meta-heuristic algorithms have been applied to this type of problem; however, Harmony Search provides a convenient implementation at a reasonable computational cost. In this study, Harmony Search is demonstrated to be a valuable tool for water distribution network optimization as well as for pipe aging assessment.

Monitoring of Pressure Transients in Great Lakes Water Authority Water Transmission System

Great Lakes Water Authority (GLWA) operates one of the largest water systems in the United States and, like most other water utilities, is facing the problem of aging water infrastructure. Internal pressure transient events can be a major contributing factor in the deterioration and failure of aging water pipes. To evaluate the impact of pressure transients on water main deterioration, for over three years GLWA has maintained a real-time pressure transient monitoring program within its water transmission system. The Trimble Unity Remote Monitoring suite is used; it includes high speed pressure sensors and data loggers. Approximately 6000 transient events have been recorded by the 30 transient monitoring sensors installed within the transmission system. A quantitative approach to evaluating the relative impact of pressure transients on the deterioration of water pipes has been used in analyzing the pressure transient events. The approach is based on the frequencies and pressure ranges of transient events. This paper presents the development of the transient monitoring program and analytical results of the pressure transient data. These analytical results, plus the ongoing transient monitoring data, are being used in updating GLWA’s system risk assessment.

Robust Sewer Defect Detection With Text Analysis Based on Deep Learning

Sewerage systems play a vital role in building modern cities, providing appropriate ways to release liquid wastes. Due to the rapid expansion of cities, the deterioration of sewage pipes are increasing. Hence, systematic maintenance methods are require to overcome this problem. In most cases, sewer inspection is done by human inspectors, which is error-prone, time-consuming, costly, and lacking appropriate survey evaluations. In this paper, we introduce a new automated framework for detecting sewage pipe defects based on the attention mechanism, improved YOLOv5 architecture, and location information recognition from CCTV videos. The main contributions include (1) the addition of a micro-scale detection feature in the layers to improve the defect detection mechanism; (2) the application of a convolutional block attention module for better channel/spatial features; (3) construction of a larger defect-detection dataset for the 12 most common defect types; and (4) implementation of the TPS-ResNet-BiLSTM-Attn (TRBA) model for the text-information recognition mechanism from CCTV videos. The experimental results show that the proposed real-time sewer defect detection model achieved the mean average precision (mAP) of 75.9% on the proposed dataset, outperforming other standard models, such as YOLO and SSD.

Integrated approach for pipe failure prediction and condition scoring in water infrastructure systems

Pipe failures in water distribution infrastructure have significant economic, environmental, and public health impacts. To alleviate these impacts, pipe deterioration modeling has been increasingly implemented to characterize and predict pipe failure patterns with the aim of prioritizing repair and replacement decisions. Logistic regression has been recognized in recent literature as a strong candidate for failure prediction modeling. However, previous studies have often been limited to demonstrating the application of logistic regression for estimating failure probabilities. This study builds on previous efforts by proposing an approach for implementing logistic regression into a holistic framework for asset management decision-making. This framework incorporates logistic regression modeling, with a flexible time-interval, into a practical condition scoring methodology that accounts for the attitude of water utilities towards risk. The developed framework is demonstrated and tested on a 20-year pipe failure dataset of a large metropolitan US city. The logistic regression model displayed high accuracy in estimating the probability of failure within different time intervals, and the scoring method showed a reasonable ability to predict the criticality of repair decisions for pipes based on their condition.

Machine learning based water pipe failure prediction: The effects of engineering, geology, climate and socio-economic factors

Underground water pipes deteriorate under the influence of various physical, mechanical, environmental, and social factors. Reliable pipe failure prediction is essential for a proactive management strategy of the water supply network (WSN), which is challenging for the conventional physics-based model. This study applied data-driven machine learning (ML) models to predict water pipe failures by leveraging the historical maintenance data heritage of a large water supply network. A multi-source data-aggregation framework was firstly established to integrate various contributing factors to underground pipe deterioration. The framework defined criteria for the integration of various data sources including the historical pipe break dataset, soil type dataset, topographical dataset, census dataset, and climate dataset. Based on the data, five ML algorithms, including LightGBM, Artificial Neural Network, Logistic Regression, K-Nearest Neighbors, and Support Vector Classification are developed for pipe failure prediction. LightGBM was found to achieve the best performance. The relative importance of major contributing factors on the water pipe failures was analyzed. Interestingly, the socioeconomic factors of a community are found to affect the probability of pipe failures. This study indicates that data-driven analysis that integrates the Machine Learning (ML) techniques and the proposed data integration framework has the potential to support reliable decision-making in WSN management.

Degradation of asbestos – Reinforced water supply cement pipes after a long-term operation

Potable water supply system in major countries still uses a large proportion of asbestos-cement (AC) pipes for fresh drinking water delivery. Generally, after installation and initial purging, the AC tubes are believed to self-passivate by calcite scale and bio-film, especially when conveying hard water. However, the overall performance of AC tubes after decades of operation is significantly reduced and is still mainly unknown. In the current research, we investigated the AC water supply tube after 56 years of operation with high-hardness conveyed water. Our results show that asbestos fibres are emitted from degraded AC pipes as a result of wall softening due to calcium leaching from hydrated cementitious materials, resulting in the loss of mechanical stability. Although the water pumped into the system is not considered aggressive, the seasonal variations of water temperature and chemistry results in an interplay of calcite scaling and Ca leaching, the latter being the dominating process. By comparing the experimental observations with the long-term chemistry reports of the water supplied through the pipes, a positive relationship was established between the temperature and quality of the conveyed water with the corrosion and the calcite scale formation, which are dictating the emission of the fibres into the drinking water. In addition to the health risks posed by asbestos, these processes have many adverse effects on drinking water supply, such as pipe malfunction and destruction resulting in water loss, reduction of hydraulic capacity, microbial proliferation, and water quality deterioration, a topic of interest for global water industries process.

Calibration of Design Models for Leakage Management of Water Distribution Networks

Water losses in urban water distribution networks (WDN) accelerate the deterioration of such infrastructures. The enhanced hydraulic modelling provides a phenomenological representation of WDN hydraulics, including the modelling of leakages as function of pipe average pressure and deterioration. The methodological use of such models on real WDN was demonstrated to support the planning of leakage management actions. Nonetheless, many water utilities are still in the process of designing flow/pressure monitoring, thus data available are not enough to perform detailed calibration of such models.This work presents a physically based approach for the calibration of WDN hydraulic models aimed at supporting leakage management plans since early stages. The proposed procedure leverages the key role of mass balance in enhanced hydraulic models and the technical insight on pipe deterioration mechanisms for various quantity and quality of available data. Two calibration studies of real WDNs demonstrate the feasibility of the approach and show that the distribution of leakages in the WDN does not much influence the pressure values, which confirms the need for flow measurements at monitoring districts for leakage and asset management.

Determining physical and operational factors influencing pipeline leakage location pattern in water distribution networks using spatial poisson point process

There are four pillars in leakage management strategy for water distribution networks, consist of pressure management, speed of repairs, active leakage control, and asset management. Leakage can be managed by applying the right combination of those factors in the right leakage management strategy. This study conducted in Malang City, East Java, Indonesia. The factors that influence physical water loss in the water distribution system are analyzed by focusing on the physical and operational parameters. The data in this study consist of spatial data and quantitative data. Poisson distribution in the spatial point process is used as the leakage point distribution approximation. From the analysis, it is found that the distribution pattern of pipeline leakage location is categorized as inhomogeneous poisson process, with two types of intensity area. Based on the results of parameter estimation, it is found that the high-intensity area is affected by time of repairs, pipe diameter, pipe length ratio, water supply ratio, and pipe deterioration ratio but not affected by the hydraulic energy demand. While the low-intensity area is influenced by pipe diameter, pipe length ratio, water supply ratio, pipe deterioration ratio, and the hydraulic energy demand but not affected by the time of repairs.

Pipe breaks and estimating the impact of pressure control in water supply networks

The deterioration and fracture of water supply pipes present a major threat for the continuous provision of drinking water. The hydraulic pressure in pipes is an influential factor for the occurrence of pipe breaks. However, little evidence has been provided so far for the quantitative assessment of the impact of pressure control on reducing the number of pipe breaks. In this paper, we applied logistic regression with polynomial terms, and a sensitivity analysis to assess the potential impact of pressure control on reducing pipe breaks. A large dataset of historic pipe breaks was used to develop and validate the presented method. Cast iron and asbestos cement pipes were examined in detail. Results showed that pipe breaks could be decreased by 18% to 30% by reducing the mean pressure for the investigated cohorts of asbestos cement and cast iron pipes. Pressure range reduction could provide larger impacts on both pipe materials. These results indicate that proactively controlling the hydraulic pressure may have a potentially significant impact on the reliability and sustainability of water supply networks.

Assessment of Pipe Deterioration in Water Distribution Network of Incheon City Using Principal Component Analysis and Artificial Neural Network

Assessment of Pipe Deterioration in Water Distribution Network of Incheon City Using Principal Component Analysis and Artificial Neural Network

Operational Risk-Based Decision Making for Wastewater Pipe Management

Recent advances in the development of wastewater pipe deterioration models allow a better quantification of the uncertainty in the likelihood of failure of pipes. However, the consequence of failure is subjected to many uncertainties that are not easy to be captured and support effective risk-based decision making. One component of the consequence of failure is the effect of the failed components on the network-level performance. An operational risk metric index is defined as a combination of the probability of failure and the effect (consequence) of the failed component at the network level. Three topological graph metrics (betweenness, eigenvector centrality, and principal component centrality) are used to identify the criticality of components in large wastewater systems. Their impact on the network performance is quantified in terms of graph connectivity. Both metrics lead to different rankings of components, making hard the decision-making process. The ordered weighted averaging (OWA) technique is used, as a data fusion technique, to determine a new consequence metric that is an aggregation of the information obtained from individual graph theoretical metrics. This metric is coupled with the likelihood of failure to quantify the operational risk of failure of the components. The uncertainty is propagated through the orness function, which is dependent of the decision-maker’s attitude toward risk. The results show that the OWA aggregation reasonably captured the critical areas in the city. Different risk levels are identified and projected onto GIS maps for visualization of the high-risk pipes in the network. The proposed approach is applied on the wastewater system of the city of Calgary.

Assessment of external corrosion deterioration of large diameter metallic water pipes buried in reclaimed land

Assessment of external corrosion deterioration of large diameter metallic water pipes buried in reclaimed land

Geoadditive Quantile Regression Model for Sewer Pipes Deterioration Using Boosting Optimization Algorithm

Proactive management of wastewater pipes requires the development of deterioration models that support maintenance and inspection prioritization. The complexity and the lack of understanding of the deterioration process make this task difficult. A semiparametric Bayesian geoadditive quantile regression approach is applied to estimate the deterioration of wastewater pipe from a set of covariates that are allowed to affect linearly and nonlinearly the response variable. Categorical covariates only affect linearly the response variable. In addition, geospatial information embedding the unknown and unobserved influential covariates is introduced as a surrogate covariate that capture global autocorrelations and local heterogeneities. Boosting optimization algorithm is formulated for variable selection and parameter estimation in the model. Three geoadditive quantile regression models (5%, 50% and 95%) are developed to evaluate the band of uncertainty in the prediction of the pipes scores. The proposed model is applied to the wastewater system of the city of Calgary. The results show that an optimal selection of covariates coupled with appropriate representation of the dependence between the covariates and the response increases the accuracy in the estimation of the uncertainty band of the response variable. The proposed modeling approach is useful for the prioritization of inspections and provides knowledge for future installations. In addition, decision makers will be informed of the probability of occurrence of extreme deterioration events when the identified causal factors, in the 5% and 95% quantiles, are observed on the field.

Sanitation Network Sulfide Modeling as a Tool for Asset Management. The Case of the City of Murcia (Spain)

Hydrogen sulfide (H2S) generated in sewer networks induces corrosion which today constitutes the main cause of deterioration of concrete pipes. Information regarding the H2S concentration inside sewer networks, as well as its control and reduction, has become one of the most important concerns in sanitation systems management nowadays. Modeling sulfide dynamics is the key to understanding corrosion processes. A dynamic model (called EMU-SANETSUL) was developed to calculate the H2S concentration in both the water and the gas phases of the main sewers of the city of Murcia (Spain). The model was calibrated with data from field measurements taken from both the gas phase and wastewater. Samples were taken in the network from 2016 to 2019. The model achieved an accuracy index and error index in the ranges of 57% and 15%, respectively. Empirical equations of reaction are used, and calibration parameters are detailed. The code uses an explicit discretization technique, named the Discrete Volume Element Method (DVEM). A map with the annual average concentration of H2S at the gas phase inside each pipe of the simulated network is presented. Values are compared with the mechanical deterioration inventory from closed-circuit television (CCTV) inspections performed by the Municipal Sanitation Company of the city of Murcia (EMUASA). Mechanical deterioration of pipes has diverse causes, including corrosion of pipes by H2S. Sections with high H2S concentrations that match with mechanical wear can be considered susceptible to being prioritized when rehabilitation works are being planned. Therefore, H2S concentration modeling provides valuable information for asset management of the sewer network.

The analysis of financially sustainable management strategies of urban water distribution network under increasing block tariff structure: A system dynamics approach

Financially self-sustainable management of urban water infrastructure assets in IBTs structure is a significant challenge faced by policy makers. In this paper, a novel causal loop diagram is developed to model dynamics of feedback loops and interactions between physical, financial, and consumers’ sectors in order to assess financially sustainable management strategies of water distribution networks in IBTs structure. Developing a system dynamics (SD) model for policy analysis of urban water management according to IBTs structure, is the main contribution of this research. In the presented model, results of different financial and rehabilitation strategies are compared by means of financial, environmental, social, and service performance indicators. The proposed model is implemented for Isfahan province in Iran, demonstrating that prices set to finance full cost of the service through higher blocks of consumption decrease annual fee hike rate of lower blocks of consumption. However, penalizing higher blocks has no considerable impact on the amount of total water volume purchased. Households outflow to the lower blocks slows down the growth rate of utility’s revenue that consequently results in deterioration of network pipes and leakage. Larger price differentials between consumption blocks causes reduction in the utility’s revenue and underlines the need for debt-based financing.

Factors Affecting Steel Water-Transmission Pipe Failure and Pipe-Failure Mechanisms

Water-transmission main (WTM) failures transporting a much greater volume of water cause flooding roads, the inconvenience of daily activities, and economic loss. However, few studies have investigated factors affecting failure in steel WTMs and failure mechanisms based on field data. This study aimed to (1) evaluate the primary factors affecting a break rate of steel WTMs by statistical correlation analysis, (2) establish failure mechanisms of the influential factors with the steel WTM, and (3) quantify the failure causes based on the historical break data. It was found that water alkalinity, along with pipe age, significantly affected steel pipe deterioration, while other factors such as external load contributed to a lesser extent, which differs from previous studies based on small iron or plastic pipes. Additionally, this study proposed the holistic model for chemistry-based mechanisms between the key factors and steel pipe deterioration. The failure cause analysis also determined corrosion as the highest failure cause (52%). These findings will contribute to understanding steel pipe-failure mechanisms and extending the service life of steel WTMs.

Shear strength and interface friction characteristics of expandable foam grout

The deterioration of buried pipes in urban areas leads to the formation of underground cavities, which may induce road subsidence. To fill these cavities, this study investigates the shear strength characteristics of expandable foam grout (EFG) and the interface between EFG and two types of sands. A series of direct shear tests is conducted using EFG specimens prepared under different conditions of curing time and unit weights of sand. After three days of curing, the shear stress curves of EFG and EFG–sand interface obtained at three normal stresses indicate brittle behavior and ductile behavior, respectively. Friction angles of the EFG increase with curing time, while those of the EFG–sand interface remain almost constant. The interface friction angles between the EFG and sands are smaller than or equal to the internal friction angles of the sands. Therefore, the shear strength of the EFG–surrounding soil interface should be given more attention than the high shear strength of the EFG, to fill underground cavities.

Lifecycle operational resilience assessment of urban water distribution networks

This paper proposes a lifecycle operational resilience assessment framework of urban water distribution networks (WDNs), taking accidents, pipe deterioration, and restoration into account. First, the accident occurrence rates for three common types of pipes, namely, cast iron pipes (CIPs), ductile iron pipes (DIPs), and steel pipes (SPs), are fitted using the maintenance data provided by a water administrative sector. Second, for the two most common accidents, i.e., leakages and bursts, the accident pipes in the former case are simulated by reduced flow, while the accident pipes as well as the influenced ones in the latter case are isolated completely by closing valves, which are determined by a depth-first search method. Third, two restoration strategies are considered, including plugging and replacement, and the corresponding recovery time by days are given based on the maintenance data. Meanwhile, the aging induced pipe deterioration is modeled in the operation process. Finally, the operational resilience of the WDN in Mianzhu city is evaluated by above framework and six resilience strategies are investigated and compared. Results show that replacing accident pipes with new DIPs once burst occurs improve the operational resilience of WDNs most obviously. In addition, replacing CIPs with DIPs ahead of the design working life or adding valves also help to improve the resilience level. Based on the findings, some feasible and practicable resilience enhancement suggestions are concluded to provide guidance for local decision makers.