61 research outputs found

    Application of neural networks in modelling serviceability deterioration of concrete stormwater pipes

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    Stormwater pipe systems in Australia are designed to convey water from rainfall and surface runoff only and do not transport sewage. Any blockage can cause flooding events with the probability of subsequent property damage. Proactive maintenance plans that can enhance their serviceability need to be developed based on a sound deterioration model. This paper uses a neural network (NN) approach to model deterioration in serviceability of concrete stormwater pipes, which make up the bulk of the stormwater network in Australia. System condition data was collected using CCTV images. The outcomes of model are the identification of the significant factors influencing the serviceability deterioration and the forecasting of the change of serviceability condition over time for individual pipes based on the pipe attributes. The proposed method is validated and compared with multiple discriminant analysis, a traditionally statistical method. The results show that the NN model can be applied to forecasting serviceability deterioration. However, further improvements in data collection and condition grading schemes should be carried out to increase the prediction accuracy of the NN model.<br /

    Markov and Neural Network Models for Prediction of Structural Deterioration of Stormwater Pipe Assets

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    Storm-water pipe networks in Australia are designed to convey water from rainfall and surface runoff. They do not transport sewerage. Their structural deterioration is progressive with aging and will eventually cause pipe collapse with consequences of service interruption. Predicting structural condition of pipes provides vital information for asset management to prevent unexpected failures and to extend service life. This study focused on predicting the structural condition of storm-water pipes with two objectives. The first objective is the prediction of structural condition changes of the whole network of storm-water pipes by a Markov model at different times during their service life. This information can be used for planning annual budget and estimating the useful life of pipe assets. The second objective is the prediction of structural condition of any particular pipe by a neural network model. This knowledge is valuable in identifying pipes that are in poor condition for repair actions. A case study with closed circuit television inspection snapshot data was used to demonstrate the applicability of these two models

    Predicting deterioration rate of culvert structures utilizing a Markov model

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    A culvert is typically a hydraulic passage, normally placed perpendicular to the road alignment, which connects the upstream and downstream sections underneath an embankment, while also providing structural support for earth and traffic loads. The structural condition of culverts continues to deteriorate due to aging, limited maintenance budgets, and increased traffic loads. Maintaining the performance of culverts at acceptable levels is a priority for the U.S. Department of Transportation (DOT), and an effective maintenance of culvert structures can be greatly improved by introducing asset management practices. A priority list generated by traditional condition assessment might not provide optimum solutions, and benefits of culvert asset management practices can be maximized by incorporating prediction of deterioration trends. This dissertation includes the development of a decision making chart for culvert inspection, the development of a culvert rating methodology using the Analytic Hierarchy Process (AFIP) based on an expert opinion survey and the development of a Markovian model to predict the deterioration rate of culvert structures at the network level. The literature review is presented in three parts: culvert asset management systems in the U.S.; Non-destructive Technologies (NDT) for culvert inspection (concrete, metal, and thermoplastic culvert structures); and statistical approaches for estimating the deterioration rate for infrastructure. A review of available NDT methods was performed to identify methods applicable for culvert inspection. To identify practices currently used for culvert asset management, culvert inventory data requests were sent to 34 DOTs. The responses revealed that a relatively small number of DOTs manage their culvert assets using formal asset management systems and, while a number of DOTs have inventory databases, many do not have a methodology in place to convert them to priority lists. In addition, when making decisions, DOTs do not incorporate future deterioration rate information into the decision making process. The objective of this work was to narrow the gap between research and application. The culvert inventory database provides basic information support for culvert asset management. Preliminary data analysis of datasets provided by selected DOTs was performed to demonstrate the differences among them. An expert opinion survey using AHP was performed to confirm the weight of 23 factors, which was believed to contribute to the hydraulic & structural performance of culvert structures, so as to establish the culvert rating methodology. A homogenous Markov model, which was calibrated using the Metropolis-Hastings Algorithm, was utilized in the computation of the deterioration rate of culverts at the network level. A real world case study consisting of datasets of three highways inspected regularly by Oregon DOT is also presented. The performance of the model was validated using Pearson\u27s chi-square test

    A Risk Based Approach for Proactive Asset Management of Sewer Structural Conditions in England and Wales

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    The aim of this research is to create a risk based framework to prioritise proactive investment for sewers in England and Wales. This research proposes a sewer deterioration model that will enhance and not replace the industry’s business as usual process and it also recommends how standard sewer assessment reports can be better utilised to inform business decisions. The methodology used to complete this research project is a mixture of qualitative and quantitative approaches to analyse a total length of 24,252 km which represents 703,156 records of historic sewer structural condition inspection data. This was used to build an improved deterioration model. Proactive investment (future condition prediction) assessments have been made within Thames Water and other wastewater utilities in the UK. The approaches are reviewed, compared, limitation identified and a robust approach was defined, devising means to mitigate the limitations identified. Existing approaches within and outside the industry to assess sewer condition and model sewer deterioration for risk management was reviewed. Data analytical software such as MATLAB and Tibco Spotfire were used to create an intuitive risk framework that will aid sewer investment decision making. An improved deterioration model and inspection frequencies for sewers were developed as a premise for proactive investment. This deterioration model and the inspection frequencies were then used to create a risk based framework to help set proactive priorities for sewer management. This would enable sewerage asset owners with large kilometres of sewers to manage the sewerage system more proactively before they reach a critical point and reduce the reliance on industry expert judgement and further surveys. The improved deterioration model and inspection frequencies provided in this research would enable sewer asset managers to determine the most cost-effective time to invest in repairs or replacement. Also, a plausible and reliable validation that was provided would give a high level of confidence in the risk based framework

    Simulation of railway drainage asset service condition degradation in the UK using a Markov chain–based approach

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    UK railway drainage systems are facing increasing challenges due to poor completeness of the asset inventory, long asset life cycles, more intense use of the UK railway system, and a changing climate. It is therefore important for drainage managers to acquire a better understanding of the current and future condition of the drainage assets for which they are responsible. This study presents a Markov model for simulating the potential future service condition of various classes of UK railway drainage assets based on observed historical changes in asset condition. Linear regression analysis was performed on distinct asset groups and the influence of the characteristics of asset construction material, size, shape, and location on the rate of the degradation process was quantified. These results were incorporated with the continuous time Markov chain model to improve the accuracy of the degradation rate prediction for several drainage asset classes. The model is illustrated on a case study of the Network Rail drainage assets showing the minimum number of samples required to make a reliable estimation of the service condition degradation process

    Methodology for identifying the key and enough factors for achieving objectives in sewer asset management

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    El principal objetivo de la tesis doctoral fue desarrollar una metodología para determinar los factores suficientes y necesarios para alcanzar objetivos específicos en la gestión patrimonial de alcantarillados, teniendo en cuenta la cantidad y calidad de la información disponible. El documento consta de cuatro partes: Parte A consiste en el marco teórico de los principales conceptos, pruebas, métodos, y métricas utilizados como base para desarrollar la metodología propuesta; Parte B contiene la descripción de los materiales (casos de estudio y herramientas computacionales) y los argumentos científicos de los modelos escogidos para desarrollar la metodología propuesta; Parte C es la más importante parte del documento, ya que describe las herramientas desarrolladas que apoyan la gestión patrimonial de alcantarillados y la metodología propuesta; y por último Parte D ilustra los resultados de las herramientas desarrolladas y la aplicación de la metodología propuesta a dos casos de estudio (Bogotá y Medellín). Las principales contribuciones de la tesis doctoral son: (i) una metodología basada en redes bayesianas para seleccionar un modelo rentable para apoyar la gestión patrimonial de activos como una herramienta de selección de atributos; (ii) métricas de desempeño vinculadas con objetivos en gestión patrimonial de alcantarillados; (iii) una metodología de optimización para modelos basados en aprendizaje de máquina para encontrar los hiper-parámetros óptimos para alcanzar objetivos de gestión; y finalmente (iv) la construcción de modelos de deterioro basados en diferentes métodos estadísticos y de aprendizaje de máquina en diferentes casos de estudio evaluado las predicciones a partir de diferentes perspectivas.The main objective of the doctoral thesis was to develop a methodology for determining which factors are enough and necessary to achieve specific objectives in sewer asset management considering the quantity and quality of the available information. The manuscript consists on four parts: Part A depicts the theoretical framework of the main concepts, tests, methods, and metrics used as the basis for developing the proposed methodology; Part B concerns the description of materials (case studies and computer-based tools) and the scientific arguments of the choosing methods for developing the proposed methodology; Part C is the most essential part of this manuscript because it describes the developed sewer asset management tools and the proposed methodology, objective of this doctoral thesis; and Part D illustrates the results of the proposed sewer asset management tools and the application of the proposed methodology in two case studies (Bogota and Medellin). The main contributions of the doctoral thesis are: (i) a Bayesian network-based methodology for selecting a cost-effective sewer asset management model as a feature selection tool; (ii) performance metrics linked with management objectives in sewer asset management; (iii) an optimization methodology for machine learning-based models to find the optimal hyperparameters for achieving management objectives; and (iv) building deterioration models based on different statistical and machine learning methods on different case studies, evaluating the predictions from different perspectives.Doctor en IngenieríaDoctoradohttps://orcid.org/0000-0001-5084-7937https://scholar.google.com/citations?hl=en&user=WSY6pA0AAAAJhttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=000146448

    Whole Life Cost Modelling For Railway Drainage Systems Including Uncertainty

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    The UK railway drainage system is facing significant asset management challenges due to the presence of large numbers of assets with long asset life cycles. Maintaining the required asset performance economically and efficiently, while complying with the relevant legislation and regulations is a major concern for Network Rail's asset managers. The whole life cost (WLC) approach has been developed and implemented in many industries and has proven its usefulness in the management of assets, particularly for assets with long life spans and in situations of uncertain future expenditure. WLC involves estimating the present value of the total cost of ownership over any asset's likely operational life. It is often integrated with decision support tools to enable a more robust decision making process. This has significant benefits in regulated industries in which all expenditure requires clear justification. This project developed a whole life cost model suitable for railway drainage systems, considering the uniqueness and complexity of costs associated with railway business operations. This WLC model can offer prediction of the transitions of drainage assets condition grades; assessments of drainage system operational performance; and provide realistic estimates of financial requirements in order to achieve desired operational performance; and evaluate the financial consequences due to loss of performance. This WLC model provides the information to build decision support tools that can help Network Rail prioritise drainage maintenance and refurbishment based on available and anticipated budgets and operational risks. This work demonstrated that the whole life cost modelling approach can provide an ideal solution for sustainably maintaining drainage systems while optimising the total cost of ownership and minimising operational, social and environmental impacts. The developed WLC approach enables asset managers to make decisions both on a strategic and operational level. Strategically, WLC approaches can forecast the overall budget and workload needed to maintain an infrastructure system over its assets' lifetime or a predefined financial period. Tactically, it can provide the asset owner with an optimum renewal, maintenance and utilisation plan under a given risk/cost requirement. This project provides WLC approaches that operate at both a strategic and tactical level for the UK railway drainage system. The methods developed in this thesis are now being implemented by NR into operational practice

    Smart Urban Water Networks

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    This book presents the paper form of the Special Issue (SI) on Smart Urban Water Networks. The number and topics of the papers in the SI confirm the growing interest of operators and researchers for the new paradigm of smart networks, as part of the more general smart city. The SI showed that digital information and communication technology (ICT), with the implementation of smart meters and other digital devices, can significantly improve the modelling and the management of urban water networks, contributing to a radical transformation of the traditional paradigm of water utilities. The paper collection in this SI includes different crucial topics such as the reliability, resilience, and performance of water networks, innovative demand management, and the novel challenge of real-time control and operation, along with their implications for cyber-security. The SI collected fourteen papers that provide a wide perspective of solutions, trends, and challenges in the contest of smart urban water networks. Some solutions have already been implemented in pilot sites (i.e., for water network partitioning, cyber-security, and water demand disaggregation and forecasting), while further investigations are required for other methods, e.g., the data-driven approaches for real time control. In all cases, a new deal between academia, industry, and governments must be embraced to start the new era of smart urban water systems

    MOEGLICHKEITEN DER ANWENDUNG FACHKUNDIGER METHODEN ZUR OPTIMIERUNG DER WARTUNG VON ABWASSERSYSTEMEN

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    U radu je dan pregled dosadašnjih istraživanja o mogućnostima primjene ekspertnih metoda (umjetne neuronske mreže, genetski algoritmi, ekspertni sustavi, stabla odlučivanja, Markovljevi lanci i algoritam kolonije mrava) za optimizaciju održavanja sustava odvodnje. Pravodobno održavanje sustava odvodnje važno je zbog njegova pravilnog funkcioniranja, manjih troškova popravaka i osiguranja osnovne funkcije sustava odvodnje, te odvođenja otpadne vode iz kućanstava do uređaja za pročišćavanje otpadnih voda i ispuštanja u prijamnik. Navedene su moguće primjene ekspertnih metoda u optimizaciji održavanja sustava odvodnje čiji je cilj smanjiti troškove održavanja.This paper provides an overview of the state-of-the-art research on the possibilities of using expert methods (artificial neural networks, genetic algorithms, expert systems, decision trees, Markov chains, and ant colony algorithm) for optimising maintenance of sewerage systems. Timely maintenance of sewerage systems is significant as it ensures their proper functioning, repair cost reductions, basic operation of the system, drainage of waste water from households to wastewater treatment plants, and discharge to the receiving water body. Possible uses of expert methods for optimising maiIn dieser Arbeit werden bisherige Forschungsergebnisse zu den Möglichkeiten des Einsatzes von fachkundigen Methoden (künstliche neuronale Netze, genetische Algorithmen, Expertensysteme, Entscheidungsbäume, Markov-Ketten und Ameisenkolonie-Algorithmus) zur Optimierung der Wartung von Abwassersystemen vorgestellt. Die rechtzeitige Wartung des Abwassersystems ist wichtig, da es ordnungsgemäß funktioniert, die Reparaturkosten senkt und die Grundfunktionen des Abwassersystems sicherstellt sowie das Abwasser aus Haushalten in die Kläranlage leitet und in den Auffangbehälter ablässt. Es werden mögliche Anwendungen von fachkundigen Methoden zur Optimierung der Wartung von Abwassersystemen mit dem Ziel, die Wartungskosten zu senken, aufgeführt
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