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Developing a new methodology for wastewater pipes cleaning prioritization using integrating Induced Ordered Weighted Averaging and fuzzy logic methods
Abstract
This paper proposes a method for prioritizing sewer cleaning operations in the absence of historical data on failures. The method utilizes the Induced Ordered Weighted Averaging (IOWA) multi-criteria decision-making technique, incorporating experts' opinions to regulate subjective inferences and ascertain uncertainty. The study considers ten parameters categorized into three main focuses: structural, environmental, and operational to analyze sewer pipe conditions. As a case study, four small zones in Tehran city were prioritized using the proposed methodology. The findings reveal that (1) decision-makers' subjective priorities and evaluations do not significantly influence critical cleaning options, (2) pipe diameter is the most effective parameter for prioritizing since it allows pipes to be meaningfully categorized and compared, and (3) slope, age, depth, upstream manhole condition, and the number of connections and laterals are more important than other parameters. Overall, this study provides valuable insights into developing effective prioritization strategies for sewer cleaning operations.
... Fuzzy set theory can also represent complex relationships between water quality parameters and their impact on aquatic ecosystems or human health. Fuzzy set theory is particularly useful when data are limited or when there are multiple conflicting objectives in water quality management (Barzegar et al. 2023). ...
... The choice of the method relies on the specific app and the data available. Occasionally, a mixture of fuzzy and nonfuzzy methods may be most appropriate (Barzegar et al. 2023). ...
Fuzzy methods using linguistic expressions and fuzzy numbers can provide a more accurate examination of manufacturing systems where data is not clear. Researchers expanded fuzzy control charts (CCs) using fuzzy linguistic statements and investigated the current process efficiency index to evaluate the performance, precision, and accuracy of the production process in a fuzzy state. Compared to nonfuzzy data mode, fuzzy linguistic statements provided decision makers with more options and a more accurate assessment of the quality of products. The fuzzy index of the actual process efficiency analyzed the process by considering mean, target value, and variance of the process simultaneously. Inspection of household water meters in Ha'il, Saudi Arabia showed the actual process index values were less than 1, indicating unfavorable production conditions. Fuzzy methods enhance the accuracy and effectiveness of statistical quality control in real-world systems where precise information may not be readily available. In addition, to provide a new perspective on the comparison of urban water and sewage systems, the results obtained from fuzzy-CC were compared with various machine learning methods such as artificial neural network and M5 model tree, in order to identify and understand their respective advantages and limitations.
HIGHLIGHTS
This research shows that fuzzy methods, which use appropriate linguistic expressions and fuzzy numbers, can provide a more accurate examination of the state of the production process.;
This article evaluates the performance of the fuzzy-CC, which was developed by benefiting the fuzzy linguistic statements, the current procedure, and the actual process efficiency index (Cpm) in the production process.;
... Depending on the length of the pipe, water consumption (W cp ) is generally 50-200 m 3 per time [8]. The water-flushing backfill pipe has a poor remove paste effect and large water consumption, which increases the drain burden and the sedimentation tank construction [9]. To develop backfill pipe flushing more efficiently in CPB, a new gas-water full-face pipe flushing technology based on two-phase flow with simpler equipment and lower cost is innovatively proposed [10,11], as shown in Figure 1. ...
The clean filling pipe can ensure sufficient overflow section, reduce paste transport resistance, and avoid slurry deposition. The introduction of compressed gas into the pipe flushing can improve the flushing effect and efficiency of the pipe and reduce water consumption. However, the dominant factors influencing water consumption water consumption are not well understood. The purpose of this study is to determine the effect of flow velocity, backfill gradient (4–7), gas void fraction (0–1), and gas pressure (0–0.7 MPa) on water consumption. Water consumption was obtained experimentally by modeling the gas–water flushing pipe. The criterion for the completion of pipeline cleaning is to reduce the water solid content of the flushing pipeline from the initial value to 1%. A model was established to predict water consumption, which was validated by industrial tests. The results show that the water consumption of the flushing pipe increased with the power law as the backfill gradient increased and decreased with the increase of gas void fraction, gas pressure, and flow velocity. The water consumption is 0.8–1.1 times the volume of the pipe in water flushing. However, the water consumption of gas–water flushing is 0.60–0.75 times the volume of the pipe. Gas–water flushing can reduce water consumption by up to 30%. The findings are conducive to design pipe flushing parameters to avoid excessive water consumption.
Efficient management of sewer blockages requires increased preventive maintenance planning. Conventional approaches to the management of blockages in sewer pipe networks constitute largely unplanned maintenance stemming from a lack of adequate information and diagnosis of blockage causative mechanisms. This study mainly investigated a spatial statistical approach to determine the influence of explanatory factors on increased blockage propensity in sewers based on spatial heterogeneity. The approach consisted of the network K-function analysis, which provided an understanding of the significance of the spatial variation of blockages. A geographically-weighted Poisson regression then showed the degree of influence that explanatory factors had on increased blockage propensity in differentiated segments of the sewer pipe network. Lastly, blockage recurrence predictions were carried out with Random Forest ensembles. This approach was applied to three municipalities. Explanatory factors such as material type, number of service connections, self-cleaning velocity, sagging pipes, root intrusion risk, closed-circuit television inspection grade and distance to restaurants showed significant spatial heterogeneity and varying impacts on blockage propensity. The Random Forest ensemble predicted blockage recurrence with 60–80% accuracy for data from two municipalities and below 50% for the last. This approach provides knowledge that supports proactive maintenance planning in the management of blockages in sewer pipe networks.
Wastewater infrastructure systems deteriorate over time due to a combination of physical and chemical factors. Failure of these critical structures could cause major social, environmental, and economic impacts. To avoid such problems, several researchers attempted to develop infrastructure condition assessment methodologies to maintain sewer pipe networks at desired condition. Sewer condition prediction models are developed to provide a framework to forecast future condition of pipes and to schedule inspection frequencies. Yet, utility managers and other authorities have challenges identifying the optimized timeline for inspection of sewer pipelines. Frequent inspection of sewer network is not cost-effective due to limited time and high cost of assessment technologies and large inventory of pipes. Therefore, the objective of this state-of-the art review is to study progress acquired over years in development of condition prediction models and investigate the potential factors affecting the condition of sewer pipes. Published papers for prediction models over a period from 2001 through 2019 were identified and analyzed. Also, a comparative analysis of most common condition prediction models such as artificial intelligence (AI) and statistical is performed in this study. The literature review suggests that out of twenty independent variables studied, pipe age, diameter, and length significantly contribute to the deterioration of sewer systems. In addition, it can be concluded that artificial intelligence models reduce uncertainty in current condition prediction models. Furthermore, prediction models which are capable to accurately find nonlinear and complex relationships among variables, are more appropriate for the model development. This study recommends the use of more environmental and operational factors such as soil type, bedding material, flow rate and soil corrosivity, and advanced data mining techniques to develop comprehensive and accurate condition prediction models. The findings of this study guides practitioners in the development of customized condition assessment models for their agencies and saves millions of dollars through optimized inspection timelines and less number of incidents. You can use your own wording as well.
Wastewater infrastructure systems deteriorate over time due to a combination of aging, physical, and chemical factors, among others. Failure of these critical structures cause social, environmental, and economic impacts. To avoid such problems, infrastructure condition assessment methodologies are developing to maintain sewer pipe network at desired condition. However, currently utility managers and other authorities have challenges when addressing appropriate intervals for inspection of sewer pipelines. Frequent inspection of sewer network is not cost-effective due to limited time and high cost of assessment technologies and large inventory of pipes. Therefore, it would be more beneficial to first predict critical sewers most likely to fail and then perform inspection to maximize rehabilitation or renewal projects. Sewer condition prediction models are developed to provide a framework to forecast future condition of pipes and to schedule inspection frequencies. The objective of this study is to present a state-of-the-art review on progress acquired over years in development of statistical condition prediction models for sewer pipes. Published papers for prediction models over a period from 2001 through 2019 are identified. The literature review suggests that deterioration models are capable to predict future condition of sewer pipes and they can be used in industry to improve the inspection timeline and maintenance planning. A comparison between logistic regression models, Markov Chain models, and linear regression models are provided in this paper. Artificial intelligence techniques can further improve higher accuracy and reduce uncertainty in current condition prediction models.
Proper management of sewer pipeline networks involves fulfillment of many technical requirements under economic constraints. Therefore, cost effective solutions are required to assist municipal engineers in prioritizing maintenance and rehabilitation needs. This demands a systematic approach to condition assessment of rapidly deteriorating sewers. Performance evaluation of sewers through random inspections is expensive. Therefore, there is an urgent need to develop a proactive sewer pipeline condition prediction methodology. This paper presents a method for assessing a sewer's structural condition by utilizing general pipeline inventory data. Based on historic condition assessment data, condition prediction models for sewers are developed using multiple regression technique. The final outcome of these models produces most likely condition rating of pipes, which will assist municipal agencies in prioritizing pipe inspection and rehabilitation to critical sewers.
The Federation of Canadian Municipalities reported that approximately 55% of sewer infrastructure in Canada did not meet current standards. Therefore, the burden on Canadian municipalities to maintain and prioritize sewers is increasing. One of the major challenges is to develop a framework to standardize the condition assessment procedures for sewer pipelines. Lack of detailed knowledge on the condition of sewer networks escalates vulnerability to catastrophic failures. This research presents a proactive methodology of assessing the existing condition of sewers by considering various physical, environmental, and operational influence factors. Based on historic data collected from two municipalities in Canada, structural and operational condition assessment models for sewers are developed using the multiple regression technique. The developed regression models show 82 to 86% accuracy when they are applied to the validation data set. These models are utilized to generate deterioration curves for concrete, asbestos cement, and polyvinyl chloride sewers in relation to traffic loads, bedding materials, and other pipe characteristics. The developed models are expected to assist municipal engineers in identifying critical sewers, prioritizing sewer inspections, and rehabilitation requirements.
An accurate description of aging and deterioration of urban drainage systems is necessary for optimal investment and rehabilitation planning. Due to a general lack of suitable datasets, network condition models are rarely validated, and if so with varying levels of success. We therefore propose a novel network condition simulator (NetCoS) that produces a synthetic population of sewer sections with a given condition-class distribution. NetCoS can be used to benchmark deterioration models and guide utilities in the selection of appropriate models and data management strategies. The underlying probabilistic model considers three main processes: a) deterioration, b) replacement policy, and c) expansions of the sewer network. The deterioration model features a semi-Markov chain that uses transition probabilities based on user-defined survival functions. The replacement policy is approximated with a condition-class dependent probability of replacing a sewer pipe. The model then simulates the course of the sewer sections from the installation of the first line to the present, adding new pipes based on the defined replacement and expansion program. We demonstrate the usefulness of NetCoS in two examples where we quantify the influence of incomplete data and inspection frequency on the parameter estimation of a cohort survival model and a Markov deterioration model. Our results show that typical available sewer inventory data with discarded historical data overestimate the average life expectancy by up to 200 years. Although NetCoS cannot prove the validity of a particular deterioration model, it is useful to reveal its possible limitations and shortcomings and quantifies the effects of missing or uncertain data. Future developments should include additional processes, for example to investigate the long-term effect of pipe rehabilitation measures, such as inliners.
Oslo Vann og Avløpsetaten (Oslo VAV)—the water/wastewater utility in the Norwegian capital city of Oslo–is assessing future strategies for selection of most reliable materials for wastewater networks, taking into account not only material technical performance but also material performance, regarding operational condition of the system.
The research project undertaken by SINTEF Group, the largest research organisation in Scandinavia, NTNU (Norges Teknisk-Naturvitenskapelige Universitet) and Oslo VAV adopts several approaches to understand reasons for failures that may impact flow capacity, by analysing historical data for blockages in Oslo.
The aim of the study was to understand whether there is a relationship between the performance of the pipeline and a number of specific attributes such as age, material, diameter, to name a few. This paper presents the characteristics of the data set available and discusses the results obtained by performing two different approaches: a traditional statistical analysis by segregating the pipes into classes, each of which with the same explanatory variables, and a Evolutionary Polynomial Regression model (EPR), developed by Technical University of Bari and University of Exeter, to identify possible influence of pipe's attributes on the total amount of predicted blockages in a period of time.
Starting from a detailed analysis of the available data for the blockage events, the most important variables are identified and a classification scheme is adopted.
From the statistical analysis, it can be stated that age, size and function do seem to have a marked influence on the proneness of a pipeline to blockages, but, for the reduced sample available, it is difficult to say which variable it is more influencing. If we look at total number of blockages the oldest class seems to be the most prone to blockages, but looking at blockage rates (number of blockages per km per year), then it is the youngest class showing the highest blockage rate.
EPR allowed identifying the relation between attitude to block and pipe's attributes in order to understand what affects the possibility to have a blockage in the pipe. EPR provides formulae to compute the accumulated number of blockages for a pipe class at the end of a given period of time. Those formulae do not represent simply regression models but highlight those variables which affect the physical phenomenon in question.
This paper deals with the characterization of two classes of
monotonic and neutral (MN) aggregation operators. The first class
corresponds to (MN) aggregators which are stable for the same positive
linear transformations and presents the ordered linkage property. The
second class deals with (MN)-idempotent aggregators which are stable for
positive linear transformations with the same unit, independent zeroes
and ordered values. These two classes correspond to the weighted ordered
averaging operator (OWA) introduced by Yager in 1988. It is also shown
that the OWA aggregator can be expressed as a Choquet integral
Certain sewer deterioration modeling approaches, represent the condition of a pipe as the sum of individual defect severities, leading to the loss of spatial information about defects in the pipe. This paper proposes a hybrid framework for incorporating spatial information of defects into the analysis of sewer pipe condition. The first component of the framework is called Defect Cluster Analysis (DCA) and it seeks to identify defect clusters (i.e. areas with multiple defects in proximity) and quantify their severity. The second component of the framework is called Defect Co-Occurrence Mining and it attempts to identify groups of defects, which occur simultaneously in pipes. The framework was evaluated on data from 7193 inspections of sewers in the US. Validation of the results by subject matter experts indicates that the proposed framework enables a fine-grained analysis of pipe condition and could be instrumental in rehabilitation decision-making. ARTICLE HISTORY
To inspect every pipe in a city annually would be a costly and labor-intensive endeavor due to the significant number of sewer pipes underground. In practice, sewer pipes in worse (better) conditions should be assigned a higher (lower) priority for maintenance. A deterioration model is an effective tool for prioritizing sewer pipe maintenance since it is able to predict the current and future conditions of these pipes. As such, this paper proposes a bi-level deterioration model to predict the condition of sewer pipes at a neighborhood level and an individual level. The neighborhood-level prediction is used to facilitate the maintenance schedule at the neighborhood scale, while the individual-level prediction is used to identify the sewer pipes with the highest risk of failure so that maintenance operations can be scheduled to keep them operating at an acceptable level of service. A linear regression model is employed in the development of the neighborhood-level prediction model, and the results are visualized by a geographic information system (GIS). The model for predicting the neighborhood level deterioration is first proposed in this research. A neural network (NN) model with a backward variable elimination process is proposed to predict the individual sewer pipe condition. Deterioration curves are derived from the individual prediction model, which facilitates better decision making with respect to sewer pipe maintenance. The contributions of the research not only lie in developed a bi-level deterioration model for the targeted city but also proposed a framework that can be generalized for municipal departments located in other cities that aim to develop their own deterioration model for sewer pipe systems. Optimized procedures, including results visualization geographically, input variable selection, etc., are adopted in the model development process to get a more accurate and efficient deterioration model for the sewer pipe system.
Several deterioration models have been developed for prediction of the actual and future condition states of individual sewer pipes. However, most tools that have been developed assume a linear dependency between the predictor and the structural condition response. Moreover, unobserved variables are not included in the models. Physical processes, such as the deterioration of pipes, are complex, and a nonlinear dependency between the covariates and the condition of the pipes is more realistic. This study applied a Bayesian geoadditive regression model to predict sewer pipe deterioration scores from a set of predictors categorized as physical, maintenance, and environmental data. The first and second groups of covariates were allowed to affect the response variables linearly and nonlinearly. However, the third group of data was represented by a surrogate variable to account for unobserved covariates and their interactions. Data uncertainty was captured by the Bayesian representation of the P-splines smooth functions. Additionally, the effects of unobserved covariates are analyzed at two levels including the structured level that globally considers a possible dependency between the deterioration pattern of pipes in the neighborhood and the unstructured level that account for local heterogeneities. The model formulation is general and is applicable to both inspected and uninspected pipes. The tool developed is an important decision support tool for urban water utility managers in their prioritization of inspection, maintenance, and replacement.
A condition assessment model for gravity and pressurised sewer pipelines using Fuzzy Set Theory (FST), and Evidential Reasoning (ER) with the aid of Fuzzy Analytical Network Process (FANP) integrated with Monte-Carlo Simulation is presented in this paper. Seventeen factors were considered for gravity pipelines in addition to the operating pressure for pressurised pipelines. The model was developed using relative weights for the different factors affecting pipelines condition which were obtained using FANP integrated with Monte-Carlo Simulation based on the results of a questionnaire that was distributed to experts working in the field of infrastructures. FST was used to set thresholds for the different effect values of factors on the pipelines’ condition, whereas ER was used to determine the final condition assessment index for the pipeline by aggregating both the relative weights and effect values for the different affecting factors.
The main objective of this work is to develop a comprehensive criteria-based multi-attribute decision-making model to plan the rehabilitation of water networks. Among the main features of this model is the capability to simultaneously analyse comprehensive criteria for the rehabilitation of water networks; where there is a significant uncertainty within the operational data. In this model, which is called as WDSR model, using a fuzzy TOPSIS technique, it is possible to adjust the criteria based on the local conditions, and to weight them based on the group decision-making of experts. In this paper, using the WDSR model, the rehabilitation plan of the ‘Anytown’ water network is prioritised through two methods. The first method uses a predefined template of WDSR, and the second uses a local template based on the conditions within the study area. The obtained results show the potential of the WDSR in the rehabilitation planning of water networks, where is encountered with numerous criteria, a significant uncertainty in the recorded data and hesitation in group decision-making of experts. It was found that, for rehabilitation of network zones, predefined template of WDSR is preferred, while for pipe rehabilitation, it is essential to use local template layer.
In wastewater systems as one of the most important urban infrastructures, the adverse consequences and effects of unsuitable performance and failure event can sometimes lead to disrupt part of a city functioning. By identifying high failure risk areas, inspections can be implemented based on the system status and thus can significantly increase the sewer network performance. In this study, a new risk assessment model is developed to prioritize sewer pipes inspection using Bayesian Networks (BNs) as a probabilistic approach for computing probability of failure and weighted average method to calculate the consequences of failure values. Finally to consider uncertainties, risk of a sewer pipe is obtained from integration of probability and consequences of failure values using a fuzzy inference system (FIS). As a case study, sewer pipes of a local wastewater collection network in Iran are prioritized to inspect based on their criticality. Results show that majority of sewers (about 62%) has moderate risk, but 12%of sewers are in a critical situation. Regarding the budgetary constraints, the proposed model and resultant risk values are expected to assist wastewater agencies to repair or replace risky sewer pipelines especially in dealing with incomplete and uncertain datasets.
Failure prediction plays an important role in the management of urban water systems infrastructures. An accurate description of the deterioration of urban drainage systems is essential for optimal investment and rehabilitation planning. In the study presented in this paper, a new method to predict sewer pipe failure based on robust decision trees is proposed. Five other different stochastic failure prediction models – the non-homogeneous Poisson process, the zero-inflated non-homogeneous Poisson process, classical decision tress (CART and Random Forest algorithms), the Weibull accelerated lifetime model and the linear extended Yule process – are also implemented and explored in order to identify models that combine good failure prediction results with robustness. The six models were tested on the asset register and pipe failure register of a large US wastewater utility; only pipe blockage failures were considered in this study. The linear extended Yule process and the zero-inflated non-homogeneous Poisson process presented the overall best results throughout the models’ comparison, showing a good ability to detect pipes with high likelihood of blockage failure. Decision trees based on robust random forests only detected pipes with high likelihood of failure when considering a short-term prediction window; the accuracy of the predictions was one of the best when using the robust decision tree model. The Weibull accelerated lifetime model provided some of the best medium-term predictions but performed less well for shorter prediction windows.
This report is an output of the fourth research track (Track 4) of WERF's strategic asset management research program ‘Asset Management Communication and Implementation’ (SAM1R06). Track 4 addressed ‘remaining asset life’, and had the overall objective of contributing to the development of techniques, tools and methods for estimating residual life of wastewater assets. Track 4 research was planned to be undertaken in a staged manner, so as to provide a stepwise development of concepts and protocols. To this end, the research team has produced a synthesis of knowledge in relation to “end of life’ and “remaining asset life”, which is the subject of this report.
Drawing on the literature and the knowledge-base of the research team and industry partners, information is presented on the range of factors that influence the life of the different asset classes involved in the provision of wastewater services. A taxonomy of asset life is also given, along with a critical review of the conceptual linkages between risk, asset management and remaining asset life. A review of techniques used to assess remaining asset life is also included, as well as a detailed 'state of the art' review of modeling tools and approaches.
One of the key questions to be addressed in this initial stage of the research was the state of knowledge with respect to the estimation and prediction of remaining asset life, and if there is the capacity to translate between condition and performance data (e.g. the presence of significant defects) and the residual life of an asset. In this regard, this report builds on previous work undertaken by the research team into protocols for condition and performance assessments, as detailed in WERF (2007).
This title belongs to WERF Research Report Series .
ISBN: 9781843393306 (Print)
ISBN: 9781780403427 (eBook)
Because of deteriorating condition levels of wastewater networks and financial constraints, sewer agencies are seeking methods to prioritize inspection of sewer pipes on the basis of risk of failure. Risk assessment of sewer pipes requires integration of the probability and consequences-of-failure values in a way that reflects the decision maker's perception of risk. This paper presents the use of a weighted scoring method to determine criticality of sewer pipelines. The procedure involves identifying important factors, determining the relative importance of the selected factors, and summarizing the overall performance of sewer pipes in terms of these factors. Determination of risk of failure by combining the resultant consequence-of-failure values with the probability-of-failure values using simple multiplication, risk matrices, and fuzzy inference systems is presented. Application of these methods is illustrated by using the sewer network information obtained from a local wastewater agency. The resultant risk maps are expected to assist agency officials in identifying sewer-pipe sections that require immediate attention or close monitoring, along with sewer-pipe sections with lower risk of failure.
This paper describes the development of the knowledge base expert system denoted as Sewer Cataloging, Retrieval and Prioritization System. This computer support system prioritizes sewer pipeline inspections used to target critical areas within a sewer drainage system. This system addresses a growing need of municipalities. The sewer infrastructure of many cities is in a state of disrepair due to budgetary constraints, a history of neglect and, often most importantly, a lack of critical information about the aging and complex system of sewers that convey wastewater for 75% of the population. The knowledge base was assembled with input from a national group of experts from both the public and private sectors. Input from the experts assesses the overall need to inspect based on both the line's consequence and likelihood of failure. In turn, consequence and likelihood of failure are based on six mechanisms describing failure and two mechanisms predicting the impact of failure. Prioritization is accomplished using a Bayesian belief network that allows the uncertainty of the experts' beliefs to be propagated through the decision process. The knowledge base is evaluated with a series of case studies and is shown to be effective at mimicking the knowledge of experts.
Use of various deterioration models in the area of infrastructure management has provided decision makers with a vehicle for predicting future deterioration. This paper presents a methodology for predicting the likelihood that a particular infrastructure system is in a deficient state, using logistic regression models, a special case of linear regression. What distinguishes these two models is that the outcome variable in the logistic regression model is binary or dichotomous and assumes a Bernoulli distribution. The methodology is illustrated in a case study involving the evaluation of the local sewer system of Edmonton, Alta. Canada. Variables of age, diameter, material, waste type, and average depth of cover are modeled, using historical data, as factors contributing to deterioration of the sewer network. The outcome of this model does not produce a prediction of condition rating but rather uses historical inspection records to provide decision makers with a means of evaluating sewer sections for the planning of future scheduled inspection, based on the deficiency probability.
Due to increasing customer and political pressures, and more stringent environmental regulations, sediment and other blockage issues are now a high priority when assessing sewer system operational performance. Blockages caused by sediment deposits reduce sewer system reliability and demand remedial action at considerable operational cost. Consequently, procedures are required for identifying which parts of the sewer system are in most need of proactive removal of sediments. This paper presents an exceptionally long (7.5 years) and spatially detailed (9658 grid squares--0.03 km² each--covering a population of nearly 7.5 million) data set obtained from a customer complaints database in Bogotá (Colombia). The sediment-related blockage data are modelled using homogeneous and non-homogeneous Poisson process models. In most of the analysed areas the inter-arrival time between blockages can be represented by the homogeneous process, but there are a considerable number of areas (up to 34%) for which there is strong evidence of non-stationarity. In most of these cases, the mean blockage rate increases over time, signifying a continual deterioration of the system despite repairs, this being particularly marked for pipe and gully pot related blockages. The physical properties of the system (mean pipe slope, diameter and pipe length) have a clear but weak influence on observed blockage rates. The Bogotá case study illustrates the potential value of customer complaints databases and formal analysis frameworks for proactive sewerage maintenance scheduling in large cities.
Sewer maintenance and rehabilitation strategies developed in a number of countries are reviewed. Comparisons are made between those approaches that focus on a predefined subset of strategic sewers and those that consider proactive maintenance of the whole system to address the wider consequences of failure such as customer satisfaction, social disruption and environmental damage. A number of diverse methods are described which can be used to optimise and prioritise proactive maintenance by analysing sewer performance, and lessons are drawn from maintenance strategies developed for other buried infrastructure assets. Limitations in existing sewer databases are discussed and new methods of obtaining sewer condition information are described. The paper concludes that to be cost effective, proactive maintenance involving inspection and repair must be focussed on those pipes which can be shown to have an early predisposition to failure.
One important issue in the theory of ordered weighted averaging (OWA) operators is the determination of the associated weights. One of the first approaches, suggested by O'Hagan, determines a special class of OWA operators having maximal entropy of the OWA weights for a given level of orness; algorithmically it is based on the solution of a constrained optimization problem. Another consideration that may be of interest to a decision maker involves the variability associated with a weighting vector. In particular, a decision maker may desire low variability associated with a chosen weighting vector. In this paper, using the Kuhn–Tucker second-order sufficiency conditions for optimality, we shall analytically derive the minimal variability weighting vector for any level of orness.
We briefly describe the Ordered Weighted Averaging (OWA) operator and discuss a methodology for learning the associated weighting vector from observational data. We then introduce a more general type of OWA operator called the Induced Ordered Weighted Averaging (IOWA) Operator. These operators take as their argument pairs, called OWA pairs, in which one component is used to induce an ordering over the second components which are then aggregated. A number of different aggregation situations have been shown to be representable in this framework. We then show how this tool can be used to represent different types of aggregation models.
We are primarily concerned with the problem of aggregating multicriteria to form an overall decision function. We introduce a new type of operator for aggregation called an ordered weighted aggregation (OWA) operator. We investigate the properties of this operator. We particularly see that it has the property of lying between the “and,” requiring all the criteria to be satisfied, and the “or,” requiring at least one of the criteria to be satisfied. We see these new OWA operators as some new family of mean operators.
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Infrastructure Management and Deterioration Risk Assessment of Wastewater Collection Systems
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Ranking the Inter-Basin Water Transfers Using Induced Ordered Weighted Averaging Operator
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