A Front-Line and Cost-Effective Model for the Assessment of Service Life of Network Pipes

[EN] In any water utility, a reliable assessment of the service life of the network pipes is a key piece within the big puzzle of assets management. This paper presents a new statistical model (basic pipes life assessment, BPLA) to assess the service life of pipes, to locate the pipes on the failures bath curve and to forecast the expected failures in future years. Its main novelties are the processing of pipe information (is that information what is adapted to the classical maintenance engineering and not the other way back) and the definition of two different time variables that can be analyzed in parallel. The first novelty makes the model less demanding in terms of data and software tools than others currently available, and the second one allows to get all the results after one single stage of calculation. To show its usability, the BPLA has been applied to a pipe network that supplies water to 500,000 citizens for which two years of failure records are available. Procedures and results have been compared to the well-known Weibull proportional hazard model (WPHM), with final relative errors lower than 10% and 15% on each particular result.The authors would like to thank Global Omnium for the support provided, both directly and through the Catedra Aguas de Valencia of the UPV, for the development of the works presented in this paper.Ramírez-Aguilar, RX.; López Jiménez, PA.; Torres Toro, D.; Cobacho Jordán, R. (2020). A Front-Line and Cost-Effective Model for the Assessment of Service Life of Network Pipes. Water. 12(3):1-23. https://doi.org/10.3390/w12030667S123123Shamir, U., & Howard, C. D. D. (1979). An Analytic Approach to Scheduling Pipe Replacement. Journal - American Water Works Association, 71(5), 248-258. doi:10.1002/j.1551-8833.1979.tb04345.xKleiner, Y., Nafi, A., & Rajani, B. (2010). Planning renewal of water mains while considering deterioration, economies of scale and adjacent infrastructure. Water Supply, 10(6), 897-906. doi:10.2166/ws.2010.571Christodoulou, S., & Deligianni, A. (2009). A Neurofuzzy Decision Framework for the Management of Water Distribution Networks. Water Resources Management, 24(1), 139-156. doi:10.1007/s11269-009-9441-2Kutyłowska, M. (2015). Neural network approach for failure rate prediction. Engineering Failure Analysis, 47, 41-48. doi:10.1016/j.engfailanal.2014.10.007Motiee, H., & Ghasemnejad, S. (2018). Prediction of pipe failure rate in Tehran water distribution networks by applying regression models. Water Supply, 19(3), 695-702. doi:10.2166/ws.2018.137Di Nardo, A., Di Natale, M., Giudicianni, C., Greco, R., & Santonastaso, G. F. (2017). Complex network and fractal theory for the assessment of water distribution network resilience to pipe failures. Water Supply, 18(3), 767-777. doi:10.2166/ws.2017.124Kutyłowska, M. (2018). Forecasting failure rate of water pipes. Water Supply, 19(1), 264-273. doi:10.2166/ws.2018.078Le Gat, Y., & Eisenbeis, P. (2000). Using maintenance records to forecast failures in water networks. Urban Water, 2(3), 173-181. doi:10.1016/s1462-0758(00)00057-1Alvisi, S., & Franchini, M. (2010). Comparative analysis of two probabilistic pipe breakage models applied to a real water distribution system. Civil Engineering and Environmental Systems, 27(1), 1-22. doi:10.1080/10286600802224064Kimutai, E., Betrie, G., Brander, R., Sadiq, R., & Tesfamariam, S. (2015). Comparison of Statistical Models for Predicting Pipe Failures: Illustrative Example with the City of Calgary Water Main Failure. Journal of Pipeline Systems Engineering and Practice, 6(4), 04015005. doi:10.1061/(asce)ps.1949-1204.0000196Santos, P., Amado, C., Coelho, S. T., & Leitão, J. P. (2016). Stochastic data mining tools for pipe blockage failure prediction. Urban Water Journal, 14(4), 343-353. doi:10.1080/1573062x.2016.1148178Debón, A., Carrión, A., Cabrera, E., & Solano, H. (2010). Comparing risk of failure models in water supply networks using ROC curves. Reliability Engineering & System Safety, 95(1), 43-48. doi:10.1016/j.ress.2009.07.004Davis, P., Silva, D. D., Marlow, D., Moglia, M., Gould, S., & Burn, S. (2008). Failure prediction and optimal scheduling of replacements in asbestos cement water pipes. Journal of Water Supply: Research and Technology-Aqua, 57(4), 239-252. doi:10.2166/aqua.2008.035Punurai, W., & Davis, P. (2017). Prediction of Asbestos Cement Water Pipe Aging and Pipe Prioritization Using Monte Carlo Simulation. Engineering Journal, 21(2), 1-13. doi:10.4186/ej.2017.21.2.1Yoo, D., Kang, D., Jun, H., & Kim, J. (2014). Rehabilitation Priority Determination of Water Pipes Based on Hydraulic Importance. Water, 6(12), 3864-3887. doi:10.3390/w6123864D’Ercole, M., Righetti, M., Raspati, G., Bertola, P., & Maria Ugarelli, R. (2018). Rehabilitation Planning of Water Distribution Network through a Reliability—Based Risk Assessment. 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Intelligent urban water infrastructure management

Copyright © 2013 Indian Institute of ScienceUrban population growth together with other pressures, such as climate change, create enormous challenges to provision of urban infrastructure services, including gas, electricity, transport, water, etc. Smartgrid technology is viewed as the way forward to ensure that infrastructure networks are fl exible, accessible, reliable and economical. “Intelligent water networks” take advantage of the latest information and communication technologies to gather and act on information to minimise waste and deliver more sustainable water services. The effective management of water distribution, urban drainage and sewerage infrastructure is likely to require increasingly sophisticated computational techniques to keep pace with the level of data that is collected from measurement instruments in the field. This paper describes two examples of intelligent systems developed to utilise this increasingly available real-time sensed information in the urban water environment. The first deals with the failure-management decision-support system for water distribution networks, NEPTUNE, that takes advantage of intelligent computational methods and tools applied to near real-time logger data providing pressures, flows and tank levels at selected points throughout the system. The second, called RAPIDS, deals with urban drainage systems and the utilisation of rainfall data to predict flooding of urban areas in near real-time. The two systems have the potential to provide early warning and scenario testing for decision makers within reasonable time, this being a key requirement of such systems. Computational methods that require hours or days to run will not be able to keep pace with fast-changing situations such as pipe bursts or manhole flooding and thus the systems developed are able to react in close to real time.Engineering and Physical Sciences Research CouncilUK Water Industry ResearchYorkshire Wate

Pipe failure prediction and impacts assessment in a water distribution network

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

Condition Assessment Models for Sewer Pipelines

Underground pipeline system is a complex infrastructure system that has significant impact on social, environmental and economic aspects. Sewer pipeline networks are considered to be an extremely expensive asset. This study aims to develop condition assessment models for sewer pipeline networks. Seventeen factors affecting the condition of sewer network were considered for gravity pipelines in addition to the operating pressure for pressurized pipelines. Two different methodologies were adopted for models’ development. The first method by using an integrated Fuzzy Analytic Network Process (FANP) and Monte-Carlo simulation and the second method by using FANP, fuzzy set theory (FST) and Evidential Reasoning (ER). The models’ output is the assessed pipeline condition. In order to collect the necessary data for developing the models, questionnaires were distributed among experts in sewer pipelines in the state of Qatar. In addition, actual data for an existing sewage network in the state of Qatar was used to validate the models’ outputs. The “Ground Disturbance” factor was found to be the most influential factor followed by the “Location” factor with a weight of 10.6% and 9.3% for pipelines under gravity and 8.8% and 8.6% for pipelines under pressure, respectively. On the other hand, the least affecting factor was the “Length” followed by “Diameter” with weights of 2.2% and 2.5% for pipelines under gravity and 2.5% and 2.6% for pipelines under pressure. The developed models were able to satisfactorily assess the conditions of deteriorating sewer pipelines with an average validity of approximately 85% for the first approach and 86% for the second approach. The developed models are expected to be a useful tool for decision makers to properly plan for their inspections and provide effective rehabilitation of sewer networks.1)- NPRP grant # (NPRP6-357-2-150) from the QatarNational Research Fund (Member of Qatar Foundation) 2)-Tarek Zayed, Professor of Civil Engineeringat Concordia University for his support in the analysis part, the Public Works 3)-Authority of Qatar (ASHGAL) for their support in the data collection

Models and explanatory variables in modelling failure for drinking water pipes to support asset management: a mixed literature review

There is an increasing demand to enhance infrastructure asset management within the drinking water sector. A key factor for achieving this is improving the accuracy of pipe failure prediction models. Machine learning-based models have emerged as a powerful tool in enhancing the predictive capabilities of water distribution network models. Extensive research has been conducted to explore the role of explanatory variables in optimizing model outputs. However, the underlying mechanisms of incorporating explanatory variable data into the models still need to be better understood. This review aims to expand our understanding of explanatory variables and their relationship with existing models through a comprehensive investigation of the explanatory variables employed in models over the past 15 years. The review underscores the importance of obtaining a substantial and reliable dataset directly from Water Utilities databases. Only with a sizeable dataset containing high-quality data can we better understand how all the variables interact, a crucial prerequisite before assessing the performance of pipe failure rate prediction models.EF-O acknowledges the financial support provided by the “Agencia de Gestió d’Ajust Universitaris I de Recerca” (https:// agaur. gencat. cat/ en/) through the Industrial Doctorate Plan of the Secretariat for Universities and Research of the Department of Business and Knowledge of the Government of Catalonia, under the Grant DI 093-2021. Additionally, EF-O appreciates the economic support received from the Water Utility Aigües de Barcelona, Empresa Metropolitana de Gestió del Cicle Integral de l'Aigua.Peer ReviewedPostprint (published version

Systematic Review for Water Network Failure Models and Cases

As estimated in the American Society of Civil Engineers 2017 report, in the United States, there are approximately 240,000 water main pipe breaks each year. To help estimate pipe breaks and maintenance frequency, a number of physically-based and statistically-based water main failure prediction models have been developed in the last 30 years. Precious review papers focused more on the evolution of failure models rather than modeling results. However, the modeling results of different models applied in case studies are worth reviewing as well. In this review, we focus on research papers after Year 2008 and collect latest cases without repetition. A total of 64 papers are qualified following the selection criteria. Detailed information on models and cases are summarized and compared. Chapter 2 provides a summary and review of failure models and discusses the limitation of current models. Chapter 3 provides a comprehensive review of collected cases, which include network characteristics and factors. Chapter 4 focuses on the main findings from collected papers. We conclude with insights and suggestions for future model selection for pipe failure analysis

Application of artificial neural networks and colored petri nets on earthquake resilient water distribution systems

Water distribution systems are important lifelines and a critical and complex infrastructure of a country. The performance of this system during unexpected rare events is important as it is one of the lifelines that people directly depend on and other factors indirectly impact the economy of a nation. In this thesis a couple of methods that can be used to predict damage and simulate the restoration process of a water distribution system are presented. Contributing to the effort of applying computational tools to infrastructure systems, Artificial Neural Network (ANN) is used to predict the rate of damage in the pipe network during seismic events. Prediction done in this thesis is based on earthquake intensity, peak ground velocity, and pipe size and material type. Further, restoration process of water distribution network in a seismic event is modeled and restoration curves are simulated using colored Petri nets. This dynamic simulation will aid decision makers to adopt the best strategies during disaster management. Prediction of damages, modeling and simulation in conjunction with other disaster reduction methodologies and strategies is expected to be helpful to be more resilient and better prepared for disasters --Abstract, page iv

Multi-Objective Optimization for Urban Drainage or Sewer Networks Rehabilitation through Pipes Substitution and Storage Tanks Installation

[EN] Drainage networks are civil constructions which do not generally attract the attention of decision-makers. However, they are of crucial importance for cities; this can be seen when a city faces floods resulting in extensive and expensive damage. The increase of rain intensity due to climate change may cause deficiencies in drainage networks built for certain defined flows which are incapable of coping with sudden increases, leading to floods. This problem can be solved using different strategies; one is the adaptation of the network through rehabilitation. A way to adapt the traditional network approach consists of substituting some pipes for others with greater diameters. More recently, the installation of storm tanks makes it possible to temporarily store excess water. Either of these solutions can be expensive, and an economic analysis must be done. Recent studies have related flooding with damage costs. In this work, a novel solution combining both approaches (pipes and tanks) is studied. A multi-objective optimization algorithm based on the NSGA-II is proposed for the rehabilitation of urban drainage networks through the substitution of pipes and the installation of storage tanks. Installation costs will be o set by damage costs associated with flooding. As a result, a set of optimal solutions that can be implemented based on the objectives to be achieved by municipalities or decisions makers. The methodology is finally applied to a real network located in the city of Bogotá, Colombia.This work was supported by the Program Fondecyt Regular (Project 1180660) of the Comision Nacional de Investigacion Cientifica y Tecnologica (Conicyt), Chile.Ngamalieu-Nengoue, UA.; Martínez-Solano, FJ.; Iglesias Rey, PL.; Mora-Meliá, D. (2019). Multi-Objective Optimization for Urban Drainage or Sewer Networks Rehabilitation through Pipes Substitution and Storage Tanks Installation. 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Comparative analysis of the ability of a set of CMIP3 and CMIP5 global climate models to represent precipitation in South America. International Journal of Climatology, 35(4), 583-595. doi:10.1002/joc.4005Ma, M., He, B., Wan, J., Jia, P., Guo, X., Gao, L., … Hong, Y. (2018). Characterizing the Flash Flooding Risks from 2011 to 2016 over China. Water, 10(6), 704. doi:10.3390/w10060704Kirshen, P., Caputo, L., Vogel, R. M., Mathisen, P., Rosner, A., & Renaud, T. (2015). Adapting Urban Infrastructure to Climate Change: A Drainage Case Study. Journal of Water Resources Planning and Management, 141(4), 04014064. doi:10.1061/(asce)wr.1943-5452.0000443Moselhi, O., & Shehab-Eldeen, T. (2000). Classification of Defects in Sewer Pipes Using Neural Networks. Journal of Infrastructure Systems, 6(3), 97-104. doi:10.1061/(asce)1076-0342(2000)6:3(97)Driessen, P., Hegger, D., Kundzewicz, Z., van Rijswick, H., Crabbé, A., Larrue, C., … Wiering, M. (2018). 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Low-Impact Development Practices to Mitigate Climate Change Effects on Urban Stormwater Runoff: Case Study of New York City. Journal of Irrigation and Drainage Engineering, 141(1), 04014043. doi:10.1061/(asce)ir.1943-4774.0000770Mora-Melià, D., López-Aburto, C., Ballesteros-Pérez, P., & Muñoz-Velasco, P. (2018). Viability of Green Roofs as a Flood Mitigation Element in the Central Region of Chile. Sustainability, 10(4), 1130. doi:10.3390/su10041130Ugarelli, R., & Di Federico, V. (2010). Optimal Scheduling of Replacement and Rehabilitation in Wastewater Pipeline Networks. Journal of Water Resources Planning and Management, 136(3), 348-356. doi:10.1061/(asce)wr.1943-5452.0000038Ngamalieu-Nengoue, U., Iglesias-Rey, P., Martínez-Solano, F., Mora-Meliá, D., & Saldarriaga Valderrama, J. (2019). Urban Drainage Network Rehabilitation Considering Storm Tank Installation and Pipe Substitution. Water, 11(3), 515. doi:10.3390/w11030515Lee, E., & Kim, J. (2017). Development of Resilience Index Based on Flooding Damage in Urban Areas. Water, 9(6), 428. doi:10.3390/w9060428Iglesias-Rey, P. L., Martínez-Solano, F. J., Saldarriaga, J. G., & Navarro-Planas, V. R. (2017). Pseudo-genetic Model Optimization for Rehabilitation of Urban Storm-water Drainage Networks. Procedia Engineering, 186, 617-625. doi:10.1016/j.proeng.2017.03.278Fadel, A. W., Marques, G. F., Goldenfum, J. A., Medellín-Azuara, J., & Tilmant, A. (2018). Full Flood Cost: Insights from a Risk Analysis Perspective. Journal of Environmental Engineering, 144(9), 04018071. doi:10.1061/(asce)ee.1943-7870.0001414Duan, H.-F., Li, F., & Yan, H. (2016). Multi-Objective Optimal Design of Detention Tanks in the Urban Stormwater Drainage System: LID Implementation and Analysis. Water Resources Management, 30(13), 4635-4648. doi:10.1007/s11269-016-1444-1Starzec, M. (2018). A critical evaluation of the methods for the determination of required volumes for detention tank. E3S Web of Conferences, 45, 00088. doi:10.1051/e3sconf/20184500088Pochwat, K. B., & Słyś, D. (2018). Application of Artificial Neural Networks in the Dimensioning of Retention Reservoirs. Ecological Chemistry and Engineering S, 25(4), 605-617. doi:10.1515/eces-2018-0040Cunha, M. C., Zeferino, J. A., Simões, N. E., & Saldarriaga, J. G. (2016). Optimal location and sizing of storage units in a drainage system. Environmental Modelling & Software, 83, 155-166. doi:10.1016/j.envsoft.2016.05.015Martino, G. D., De Paola, F., Fontana, N., Marini, G., & Ranucci, A. (2011). Pollution Reduction in Receivers: Storm-Water Tanks. Journal of Urban Planning and Development, 137(1), 29-38. doi:10.1061/(asce)up.1943-5444.0000037Andrés-Doménech, I., Montanari, A., & Marco, J. B. (2012). Efficiency of Storm Detention Tanks for Urban Drainage Systems under Climate Variability. Journal of Water Resources Planning and Management, 138(1), 36-46. doi:10.1061/(asce)wr.1943-5452.0000144Wang, M., Sun, Y., & Sweetapple, C. (2017). Optimization of storage tank locations in an urban stormwater drainage system using a two-stage approach. Journal of Environmental Management, 204, 31-38. doi:10.1016/j.jenvman.2017.08.024Cunha, M. C., Zeferino, J. A., Simões, N. E., Santos, G. L., & Saldarriaga, J. G. (2017). A decision support model for the optimal siting and sizing of storage units in stormwater drainage systems. International Journal of Sustainable Development and Planning, 12(01), 122-132. doi:10.2495/sdp-v12-n1-122-132Dziopak, J. (2018). A wastewater retention canal as a sewage network and accumulation reservoir. E3S Web of Conferences, 45, 00016. doi:10.1051/e3sconf/20184500016Słyś, D. (2018). An innovative retention canal – a case study. E3S Web of Conferences, 45, 00084. doi:10.1051/e3sconf/20184500084Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197. doi:10.1109/4235.996017Martínez-Solano, F., Iglesias-Rey, P., Saldarriaga, J., & Vallejo, D. (2016). Creation of an SWMM Toolkit for Its Application in Urban Drainage Networks Optimization. Water, 8(6), 259. doi:10.3390/w8060259Wang, Q., Zhou, Q., Lei, X., & Savić, D. A. (2018). Comparison of Multiobjective Optimization Methods Applied to Urban Drainage Adaptation Problems. Journal of Water Resources Planning and Management, 144(11), 04018070. doi:10.1061/(asce)wr.1943-5452.0000996Mora-Melia, D., Iglesias-Rey, P. L., Martinez-Solano, F. J., & Ballesteros-Pérez, P. (2015). Efficiency of Evolutionary Algorithms in Water Network Pipe Sizing. Water Resources Management, 29(13), 4817-4831. doi:10.1007/s11269-015-1092-xMora-Melià, D., Martínez-Solano, F. J., Iglesias-Rey, P. L., & Gutiérrez-Bahamondes, J. H. (2017). Population Size Influence on the Efficiency of Evolutionary Algorithms to Design Water Networks. Procedia Engineering, 186, 341-348. doi:10.1016/j.proeng.2017.03.20