BIST katılım endeksi için bir Markov zinciri analizi

This study addresses the trend estimation of the participation indices (PARTI) in the Istanbul Stock Exchange (BIST) using Markov chain (MC) theory. PARTI can be regarded as the Participation 50 Index (KAT50) and the Participation 30 Index (KATLM). Since KAT50 has only been calculated since 9th July 2014, there are only a few studies on this index. Therefore, in this study, we examine the PARTI indices. Firstly, we have employed MC method using 520 daily closing values of KATLM, between 1st July 2014 and 29th July 2016. For the KAT50 index, we used 514 daily closing values between 9th July 2014 and 29th July 2016, considering the states of these indices as increasing, decreasing or remaining stable. In order to perform a Markov chain analysis relating to prediction of the future situation, a transition probability matrix was created. Using this matrix, a steady-state analysis of the chain was performed and the future trends of KAT50-KATLM were forecasted successfully. It can be concluded that the results of this study are very helpful for individual and institutional investors’ investment decisions within global economies.Bu çalışma bir Markov zinciri (MZ) modeli ile Borsa İstanbul (BIST)’da yer alan Katılım Endekslerinin (KATLM, KAT50) hareketlerini tahmin etmeyi amaçlar. KAT50 Endeksi 9 Temmuz 2014 tarihinden bu yana işlem görmeye başladığından dolayı, literatürde bu endeks üzerinde yapılmış yeterince çalışma yoktur. Bu çalışmada ilk olarak, KATLM endeksinin 520 günlük (01.07.2014-29.07.2016), KAT50 endeksinin ise 514 günlük (09.07.2014-29.07.2016) kapanış değerleri göz önüne alınarak bir MZ modeli oluşturuldu. Bu modelde endekslerin artış, azalış ve sabit kalma durumları dikkate alındı. Endekslerin gelecekteki değerlerine ilişkin bir MZ analizi yapmak için geçiş olasılıkları matrisi oluşturuldu. Bu matristen yararlanılarak, kararlı durum analizi yapıldı ve KATLM-KAT30 endekslerinin gelecekteki hareketleri başarılı bir şekilde öngörüldü. Bu çalışmanın sonuçlarının, bireysel ve kurumsal yatırımcıların küresel ekonomilerdeki yatırım kararları için çok yararlı olduğu sonucuna varılabilir

Artificial neural networks to forecast failures in water supply pipes

Article number 8226The water supply networks of many countries are experiencing a drastic increase in the number of pipe failures. To reverse this tendency, it is essential to optimise the replacement plans of pipes. For this reason, companies demand pioneering techniques to predict which pipes are more prone to fail. In this study, an Artificial Neural Network (ANN) is designed to classify pipes according to their predisposition to fail based on physical and operational input variables. In addition, the usefulness and effectiveness of two sampling methods, under-sampling and over-sampling, are analysed. The implementation of the model is done using the open-source software Weka, which is specialised in machine-learning algorithms. The system is tested with a database from a real water network in Spain, obtaining high-accurate results. It is verified that the balance of the training set is imperative to increase the predictions’ accurateness. Furthermore, under-sampling prioritises true positive rates, whereas over-sampling makes the system learn to predict failures and non-failures with the same precision.Universidad de Sevilla VI-PPIT-U

An evolutionary fuzzy system to support the replacement policy in water supply networks: The ranking of pipes according to their failure risk

Article number 107731In this study, an evolutionary fuzzy system is proposed to predict unexpected pipe failures in water supply networks. The system seeks to underpin the decisions of management companies regarding the maintenance and replacement plans of pipes. On the one hand, fuzzy logic provides high degrees of interpretability over other black box models, which is requested in engineering application where decisions have social consequences. On the other hand, the genetic algorithm helps to optimize the parameters that govern the model, specifically, for two purposes: (i) the selection of variables; and (ii) the optimization of membership functions. Data from a real water supply network are used to evaluate the accuracy of the developed system. Several graphs that depict the ranking of pipes according to their risk of failure against the network length to be replaced support the choice of the most successful model. In fact, results demonstrate that the annual replacement of 6.75% of the network length makes it possible to prevent 41.14% of unexpected pipe failuresEm

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

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

With an increasingly aging water infrastructure, decision makers are directing their attention to better ways to model and predict asset failure. Failure modeling is a discipline that addresses complex deterioration processes to better inform asset management practices. These deterioration processes and contributing factors have been addressed using a variety of models. One statistical model that has been explored in this thesis is the logistic regression model. The proposed approach consisted of developing a logistic regression model to estimate pipe-level failure probabilities in a flexible time interval. The approach further used the probabilities to estimate a Mean Time to Failure and assign pipe condition scores according to a methodology suggested by Opila and Attoh-Okine [1]. This thesis contributes with a practical and systematic methodology to capitalize on failure records and generate actionable failure probabilities and condition scores to integrate in asset prioritization strategies.Civil, Architectural, and Environmental Engineerin

Prediction of iceberg-seabed interaction using machine learning algorithms

Every year thousands of icebergs are born out of glaciers in the Arctic zone and carried away by the currents and winds into the North Atlantic. These icebergs may touch the sea bottom in shallow waters and scratch the seabed, an incident called “ice-gouging”. Ice-gouging may endanger the integrity of the buried subsea pipelines and power cables because of subgouge soil displacement. In other words, the shear resistance of the soil causes the subgouge soil displacement to extend much deeper than the ice keel tip. This, in turn, may cause the displacement of the pipelines and cables buried deeper than the most possible gouge depth. Determining the best burial depth of the pipeline is a key design aspect and needs advanced continuum numerical modeling and costly centrifuge tests. Empirical equations suggested by design codes may be also used but they usually result in an over-conservative design. Iceberg management, i.e., iceberg towing and re-routing, is currently the most reliable approach to protect the subsea and offshore structures, where the approaching icebergs are hooked and towed in a safe direction. Iceberg management is costly and involves a range of marine fleets and advanced subsea survey tools to determine the iceberg draft, etc. The industry is constantly looking for cost-effective and quick alternatives to predict the iceberg draft and subgouge soil displacements. In this study, powerful machine learning (ML) algorithms were used as an alternative cost-effective approach to first screen the threatening icebergs by determining their drafts and then to predict the subgouge soil displacement to be fed into the structural integrity analysis. Developing a reliable solution to predict the iceberg draft and subgouge soil displacement requires a profound understanding of the problem's dominant parameters. Therefore, the present study started with dimensional analyses to identify the dimensionless groups of key parameters governing the physics of the problem. Two comprehensive datasets were constructed using the monitored characteristics of the real icebergs for draft prediction and experimental studies for the subgouge soil displacements reported in the literature. Using the constructed database, 14 ML algorithms ranging from neural network-based (NN-based) to three-based methods were sequentially used to predict the iceberg draft and the subgouge soil displacement. The studies were conducted both in clay and sand seabed. By different combinations of the input parameters, several ML models were developed and assessed by performing sensitivity analysis, error analysis, discrepancy analysis, uncertainty analysis, and partial derivative sensitivity analysis to identify the superior ML models along with the most influential input parameters. The best ML model was able to predict the iceberg drafts alongside the subgouge soil features with the highest level of precision, correlation, and lowest degree of complexity. A set of ML-based explicit equations were also derived from the wide range of field and experimental measurements for the estimation of iceberg drafts, subgouge soil deformations, and ice keel reaction forces, which outperformed the existing empirical equations. The study resulted in developing a set of tools that can be used for both a cost-effective screening of the threatening icebergs and the prediction of the corresponding subgouge soil displacements. The outcome of the study can effectively contribute to a significant reduction of iceberg management costs and greenhouse gas (GHG) emissions through the mitigation of the marine spread operation

Hydrostatic Performance Of Reinforced Concrete Pipe For Infiltration

Groundwater infiltration into underground sewer systems has long been a costly issue for municipalities. With reinforced concrete pipe (RCP) being a primary option for sewer systems, existing hydrostatic testing methods conducted by manufacturers to measure internal pipe pressure, as required by specifications, do not reflect in-situ external hydrostatic conditions. This thesis records the development of a novel testing method to evaluate the RCP joint performance for infiltration. The test is safe and easy to conduct by RCP producers at the factory. The test method mimics field conditions of possible RCP joint gap and joint offset. Over 100 tests were conducted, including 600 mm, 900 mm and 1200 mm RCP with conventional single offset self-lubricated gaskets. This study also evaluates the gasket performance for infiltration. Pipe joint performance curves were developed based on the test results. Comparison to laboratory load deformation tests on gaskets were conducted, indicating that predictions of the sealing potential derived using gasket geometry agreed well with results of infiltration tests. The study shows that the joint gap plays an important role in the sealing potential. The developed apparatus allows the observation of gasket movement under infiltration pressure against the gasket leading to failure. The performance curves also allow the prediction of an infiltration potential leading to a practical applicational procedure to guide RCP installation. A case study of deep RCP pipe subjected to groundwater pressure illustrated the usefulness of the performance curves to derive maximum allowable joint gaps, which contractors could rely on during RCP installation. The findings should allow deducing technical guidance on how water tightness of RCP can be achieved at installation below the prevailing groundwater level. Two oversampling methods: Synthetic Minority Over-sampling Technique (SMOTE) and Density-Based SMOTE were employed to address the unbalanced dataset. Accordingly, applying advanced machine learning techniques, the scale of variation in the test data can be analyzed and accurately predicted using tree-based supervised classification methods: random forest, extra trees and gradient boosting

Application of Machine Learning Algorithms to the Prediction of Water Main Deterioration

Drinking water networks are among the essential infrastructure in cities worldwide. The failure of water mains jeopardizes this essential service and the safety of water users. However, across North America, the failure rate of older water mains has been increasing. The goal of this study is to compare the accuracy and applicability of machine learning algorithms to predict water main deterioration across Canadian water systems. In previous studies, different approaches were applied to only one or a few utilities. Nevertheless, it is valuable to compare results among various networks with different characteristics and levels of data collection. Accordingly, data was collected from thirteen Canadian water utilities, including Barrie, Calgary, Halifax, Kitchener, Markham, Region of Durham, Region of Waterloo, Saskatoon, St. John’s, Waterloo, Winnipeg, Victoria, and Vancouver. A variety of factors, including intrinsic, environmental, and operational, were used to develop more reliable predictions and assess the relative importance of each factor. Random forest (RF), artificial neural networks (ANN), extreme gradient boosting (XGBOOST), and logistic regression (LR) were applied to predict the probability of failure. Furthermore, RF, ANN, XGBOOST, and ElasticNet regression models were employed to predict age at first failure and the current rate of failures. Results indicated the superiority of XGBOOST over other models in predicting the probability of failure and the current rate of failure. However, for age at first failure, RF performed better. When datasets were significantly imbalanced, the application of the Synthetic Minority Oversampling Technique (SMOTE) provided more accurate predictions. Because these models provide predictions for every pipe in the network, they can be mapped to facilitate the visualization of deterioration. While models created for one utility cannot be accurately applied to other utilities, the same machine learning algorithms can be quickly and effectively adapted to multiple utilities. Overall, these models support robust and data-driven asset management decision-making

Proactively managing drinking water distribution networks: A data- driven, statistical modelling approach to predict the risk of pipe failure.

Water distribution networks are critical infrastructures, providing clean water to millions of people. 3 billion litres of water are lost through pipe failure every day in the UK, impacting serviceability. Statistical pipe failure models can reduce pipe failures by providing valuable insights to enhance decision-making and promote proactive management. This research aims to understand the complexity of pipe failures in water distribution networks and develop a methodology for a reliable pipe failure model that identifies the risk of failure. Through an embedded case study and data-driven approach, several Objectives have been undertaken that comprise the body of research delivered through several research papers. This study offers several contributions to the immediate field of pipe failure research. Firstly, the findings investigate new factors that form the various modes and mechanisms of pipe failure, using alternative methods not commonly used in pipe failure research are used, including Generalized Additive Model and Dijkstra’s algorithm, and using data from a large UK water distribution network. Secondly, the research develops a suitable methodology for predicting annual pipe failures using an advanced machine learning method; a methodology that is easily transferrable. Thirdly, the research provides a useful means of predicting the risk of failure and visualising the results. Fourthly, the research investigates the challenges of pipe failure models using a semi-structured interview approach to review current practice. Finally, the research contributes by exploring several different data-driven methods and an embedded case study design to contribute to the broader context of pipe failure modelling. The approach presented in this research provides a methodological framework to enhance decision-making for asset management of pipes in clean water networks. Furthermore, it highlights the main limitations, particularly data quality and quantity, data-pre-processing, and model development, highlighting areas for future progress.Jude, Simon (Associate)PhD in Environment and Agrifoo

Models de priorització d'inversions utilitzant criteris de desenvolupament sostenible

Aplicat embargament des de la data de defensa fins al dia 1 d'abril de 2022This Doctoral Thesis addresses aspects of interest for hydraulic infrastructure managers, such as investment decision support tools, in order to provide both decision support and the justification tools which are required by the entities regulating the service of future investments. The aim is to develop a new methodology for the prioritization of investments that allows an objective, transparent and participatory distribution using the Sustainable Development criteria. To implement the methodology, two models have been nurtured: the Distribution Network Renewal Model (an ordering model) and the Investment Prioritization by Entries Model (a distribution model for the total volume of investment). First, a methodology for prioritizing distribution network investments using sustainable development criteria is developed: the Distribution Network Renewal Model, using sustainable development criteria. The management model aims to materialize the methodology of prioritizing the actions from a list of inventoried assets. Second, a methodology for the distribution of investment with sustainable development criteria is developed: the Prioritization Model for Matching Investments. The distribution model aims to distribute the volume of investment between the different items and sub-headings that make up the investment plan of the company. In this case, due to the heterogeneous nature of the investment plan, a methodology has been developed consisting of three phases. A first phase where the investment volume is determined by legal factors, a second one where the remaining investment is distributed between entries, and a last phase where the outlay on each entry is distributed among those sub-items of investment that make up each of the entries. Finally, a sensitivity analysis was performed on both methodologies to check the robustness of the developed models and its sensitivity to the diverse contributions of each group. The Distribution Network Renewal Model has been verified in Aigües de Barcelona network after a first year of execution of investments in the renewal of the distribution network by implementing the prioritization of the Model.En aquesta Tesi Doctoral s’aborden aspectes d’interès per als gestors d’infraestructures hidràuliques com són les eines d’ajuda a la decisió en matèria d’inversions amb l’objectiu de proporcionar tant eines d’ajuda a la decisió com la justificació exigida per les entitats reguladores del servei de les inversions a realitzar. L’objectiu és desenvolupar una nova metodologia per a la priorització de les inversions que permeti realitzar un repartiment objectiu, transparent i participatiu utilitzant criteris de Desenvolupament Sostenible. Per materialitzar la metodologia es desenvolupen dos models: el Model de Renovació de la Xarxa de Distribució (un model d’ordenació) i el Model de Priorització d’Inversions per Partides (un model de repartiment del volum total de la inversió). En primer lloc es desenvolupa una metodologia de priorització de les inversions de la xarxa de distribució, el Model de Renovació de la Xarxa de Distribució utilitzant criteris de desenvolupament sostenible. El model d’ordenació té per objectiu materialitzar la metodologia de priorització de les actuacions d’un llistat d’actius inventariats. En segon lloc es desenvolupa una metodologia de repartiment de la inversió, el Model de Priorització d’Inversions per Partides amb criteris de desenvolupament sostenible. El model de repartiment té per objectiu repartir el volum d’inversió entre les diferents partides i subpartides que conformen el pla d’inversions de l’empresa. En aquest cas, i degut a la naturalesa heterogènia del pla d’inversions, s’ha desenvolupat una metodologia que consta de tres fases. Una primera fase on es determina el volum d’inversió degut a condicionants legals, una segona on es reparteix la inversió restant a nivell de partides i una última fase on es distribueix aquesta inversió de cadascuna de les partides entre aquelles subpartides d’inversió que conformen cadascuna de les partides. Finalment, en ambdós casos s’ha realitzat una anàlisi de sensibilitat per comprovar la robustesa dels models desenvolupats i la sensibilitat a les diferents aportacions dels grups de relació. Pel Model de Renovació de la Xarxa de Distribució s’ha pogut verificar a la xarxa d’Aigües de Barcelona després del primer any d’execució de les inversions de renovació de la xarxa de distribució utilitzant la priorització del Model.En esta Tesis Doctoral se abordan aspectos de interés para los gestores de infraestructuras hidráulicas como son las herramientas de ayuda a la decisión en materia de inversiones. Con el objetivo de proporcionar herramientas de ayuda a la decisión y la justificación exigida por las administraciones reguladoras del servició de las inversiones a realizar. El objetivo es desarrollar una nueva metodología para la priorización de las inversiones que sea capaz de realizar un reparto objetivo, transparente y participativo usando criterios de Desarrollo Sostenible. Para materializar la metodología se desarrollan dos modelos: el Modelo de Renovación de la Red de Distribución (modelo de ordenación) y el Modelo de Priorización de Inversiones por Partidas (modelo de reparto del volumen total de la inversión). En primer lugar, se desarrolla una metodología de priorización de las inversiones de la red de distribución, el Modelo de Renovación de la Red de Distribución usando criterios de desarrollo sostenible. El modelo de ordenación tiene por objetivo materializar la metodología de priorización de las actuaciones de una lista de activos actualmente inventariados. En segundo lugar, se desarrolla una metodología de reparto de la inversión, el Modelo de Priorización de Inversiones por Partidas usando criterios de desarrollo sostenible. El modelo de reparto tiene por objetivo repartir el volumen de inversión entre las diferentes partidas y subpartidas que conforman el plan de inversiones de la empresa. En este caso, y debido a la naturaleza heterogénea del plan de inversiones se ha desarrollado una metodología que consta de tres fases. Una primera fase donde se determina el volumen de inversión para dar respuesta a los condicionantes legales, una segunda donde se reparte la inversión restante a nivel de partidas y una última fase donde se distribuye la inversión de cada partida entre las subpartidas de inversión que conforman dicha partida. Finalmente, en ambos casos se ha realizado un análisis de sensibilidad para comprobar la robustez de los modelos desarrollados y la sensibilidad a las diferentes aportaciones de los grupos de relación. Adicionalmente, para el Modelo de Renovación de la Red de Distribución se ha podido verificar la red de Aigües de Barcelona tras el primer año de ejecución de las inversiones de renovación de la red de distribución utilizando el modelo de priorización.Postprint (published version