Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system

The reliability of the water distribution system is critical to maintaining a secure supply for the population, industry and agriculture, so there is a need for proactive maintenance to help reduce water loss and down times. Bayesian networks are one approach to modelling the complexity of water mains, to assist water utility companies in planning maintenance. This paper compares and analyses how accurately the Bayesian network structure can be derived given a large and highly variable dataset. Method one involved using automated learning algorithms to build the Bayesian network, while method two involved a guided method using a combination of historic failure data, prior knowledge and pre-modelling data exploration of the water mains. By understanding common failure types (circumferential, longitudinal, pinhole and joint), the guided learning Bayesian Network was able to capture the interactions of the surrounding soil environment with the physical properties of pipes. The Bayesian network built using data exploration and literature was able to achieve an overall accuracy of 81.2% when predicting the specific type of water mains failure compared to the 84.4% for the automated method. The slightly greater accuracy from the automated method was traded for a sparser Bayes net where the interpretation of the interactions between the variables was clearer and more meaningful

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

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

Pipe burst diagnostics using evidence theory

Copyright © IWA Publishing 2011.The definitive peer-reviewed and edited version of this article is published in Journal of Hydroinformatics Volume 13 Issue 4, pp. 596–608 (2011), DOI: 10.2166/hydro.2010.201 and is available at www.iwapublishing.com.This paper presents a decision support methodology aimed at assisting Water Distribution System (WDS) operators in the timely location of pipe bursts. This will enable them to react more systematically and promptly. The information gathered from various data sources to help locate where a pipe burst might have occurred is frequently conflicting and imperfect. The methodology developed in this paper deals effectively with such information sources. The raw data collected in the field is first processed by means of several models, namely the pipe burst prediction model, the hydraulic model and the customer contacts model. The Dempster–Shafer Theory of Evidence is then used to combine the outputs of these models with the aim of increasing the certainty of determining the location of a pipe burst within a WDS. This new methodology has been applied to several semi-real case studies. The results obtained demonstrate that the method shows potential for locating the area of a pipe burst by capturing the varying credibility of the individual models based on their historical performance

Applications of Bayesian Networks as Decision Support Tools for Water Resource Management under Climate Change and Socio-Economic Stressors: A Critical Appraisal

Bayesian networks (BNs) are widely implemented as graphical decision support tools which use probability inferences to generate “what if?” and “which is best?” analyses of potential management options for water resource management, under climate change and socio-economic stressors. This paper presents a systematic quantitative literature review of applications of BNs for decision support in water resource management. The review quantifies to what extent different types of data (quantitative and/or qualitative) are used, to what extent optimization-based and/or scenario-based approaches are adopted for decision support, and to what extent different categories of adaptation measures are evaluated. Most reviewed publications applied scenario-based approaches (68%) to evaluate the performance of management measures, whilst relatively few studies (18%) applied optimization-based approaches to optimize management measures. Institutional and social measures (62%) were mostly applied to the management of water-related concerns, followed by technological and engineered measures (47%), and ecosystem-based measures (37%). There was no significant difference in the use of quantitative and/or qualitative data across different decision support approaches (p = 0.54), or in the evaluation of different categories of management measures (p = 0.25). However, there was significant dependence (p = 0.076) between the types of management measure(s) evaluated, and the decision support approaches used for that evaluation. The potential and limitations of BN applications as decision support systems are discussed along with solutions and recommendations, thereby further facilitating the application of this promising decision support tool for future research priorities and challenges surrounding uncertain and complex water resource systems driven by multiple interactions amongst climatic and non-climatic changes. View Full-Tex

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

A comprehensive framework to efficiently plan short and long-term investments in water supply and sewer networks

Water supply and sewer networks are critical infrastructures that provide a basic service to society. However, these systems constantly age and degrade over time. In addition, since network infrastructures are so extensive in length, they require a significant investment in maintenance tasks. Hence, within the context of infrastructure asset management (IAM), accurately defining the most efficient investment planning possible is essential to ensure their long-term sustainability. This paper presents an original five-step comprehensive framework to successfully implement an infrastructure asset management strategy and plan long-term investments. Moreover, this methodology integrates innovative and relevant operational and convenience factors that, while provide the problem both with realism and practicality, have not been addressed so far. To illustrate the usefulness and applicability of this methodology, the case study of a large water company in Spain is presented

Evaluating the uncertainty in the performance of small scale renewables

The successful delivery of low-carbon housing (both new and retrofitted) is a key aspect of the UK s commitment to an 80% reduction in carbon emissions by 2050. In this context, the inclusion of small-scale building-integrated renewable energy technologies is an important component of low carbon design strategies, and is subject to numerous regulation and incentive schemes (including the Renewable Heat Incentive (RHI)) set up by government to encourage uptake and set minimum performance benchmarks. Unfortunately there are numerous examples of in-use energy and carbon performance shortfalls for new and retrofitted buildings this is termed the performance gap . Technical and human factors associated with building subsystem performance, which are often not considered in design tools used to predict performance, are the root cause of performance uncertainty. The research presented in this doctoral thesis aims to develop and apply a novel probabilistic method of evaluating the performance uncertainty of solar thermal systems installed in the UK. Analysis of measured data from a group of low carbon retrofitted dwellings revealed that the majority of buildings failed to meet the designed-for carbon emissions target with an average percentage difference of 60%. An in-depth case study technical evaluation of one of these dwellings showed significant dysfunction associated with the combined ASHP/solar thermal heating system, resulting in a performance gap of 94%, illustrating that the performance gap can be regarded as a whole-system problem, comprising a number of subsystem causal factors. Using a detailed dataset obtained from the UK s largest field trial of domestic solar thermal systems, a cross-cutting evaluation of predicted vs. measured performance similarly revealed a discrepancy with a mean percentage difference in predicted and measured annual yield of -24%. Having defined the nature and extent of underperformance for solar thermal technology in the UK, causal factors influencing performance were mapped and the associated uncertainty quantified using a novel knowledge-based Bayesian network (BN). In addition, the BN approach along with Monte Carlo sampling was applied to the well-established BREDEM model in order to quantify performance uncertainty of solar thermal systems by producing distributions of annual yield. As such, the modified BN-based BREDEM model represents a significant improvement in the prediction of performance of small-scale renewable energy technologies. Finally, financial analysis applied to the probabilistic predictions of annual yield revealed that the current UK RHI scheme is unlikely to result in positive returns on investment for solar thermal systems unless the duration of the payments is extended or electricity is the primary source of heating