This thesis draws on techniques from Management Science and Artificial Intelligence to explore organisational aspects of asset management in electricity transmission enterprises. In this research, factors that influence policies and practices of asset management within electricity transmission enterprises have been identified, in order to examine their interaction and how they impact the policies, practices and performance of transmission businesses. It has been found that, while there is extensive literature on the economics of transmission regulation and pricing, there is little published research linking the engineering and financial aspects of transmission asset management at a management policy level. To remedy this situation, this investigation has drawn on a wide range of literature, together with expert interviews and personal knowledge of the electricity industry, to construct a conceptual model of asset management with broad applicability across transmission enterprises in different parts of the world. A concise representation of the model has been formulated using a Causal Loop Diagram (CLD). To investigate the interactions between factors of influence it is necessary to implement the model and validate it against known outcomes. However, because of the nature of the data (a mix of numeric and non-numeric data, imprecise, incomplete and often approximate) and complexity and imprecision in the definition of relationships between elements, this problem is intractable to modelling by traditional engineering methodologies. The solution has been to utilise techniques from other disciplines. Two implementations have been explored: a multi-level fuzzy rule-based model and a system dynamics model; they offer different but complementary insights into transmission asset management. Each model shows potential for use by transmission businesses for strategic-level decision support. The research demonstrates the key impact of routine maintenance effectiveness on the condition and performance of transmission system assets. However, performance of the transmission network, is not only related to equipment performance, but is a function of system design and operational aspects, such as loading and load factor. Type and supportiveness of regulation, together with the objectives and corporate culture of the transmission organisation also play roles in promoting various strategies for asset management. The cumulative effect of all these drivers is to produce differences in asset management policies and practices, discernable between individual companies and at a regional level, where similar conditions have applied historically and today.

<p>Highway infrastructure, including roads/pavements, contributes significantly to a country’s economic growth, quality of life improvement, and negative environmental impacts. Hence, highway agencies strive to make efficient and effective use of their limited funding to maintain their pavement infrastructure in good structural and functional conditions. This necessitates predicting pavement performance and scheduling maintenance interventions accurately and reliably by using appropriate performance modeling and maintenance optimization methodologies, while considering the impact of influential variables and the uncertainty inherent in pavement condition data.</p> <p> </p> <p>Despite the enormous research efforts toward stochastic pavement performance modeling and maintenance optimization, several research gaps still exist. Prior research has not provided a synthesis of Markovian models and their associated methodologies that could assist researchers and highway agencies in selecting the Markov methodology that is appropriate for use with the data available to the agency. In addition, past Markovian pavement performance models did not adequately account for the marginal effects of the preventive maintenance (PM) treatments due to the lack of historical PM data, resulting in potentially unreliable models. The primary components of a Markov model are the transition probability matrix, number of condition states (NCS), and length of duty cycle (LDC). Previous Markovian pavement performance models were developed using NCS and LDC based on data availability, pavement condition indicator and data collection frequency. However, the selection of NCS and LDC should also be based on producing pavement performance models with high levels of prediction accuracy. Prior stochastic pavement maintenance optimization models account for the uncertainty of the budget allocated to pavement preservation at the network level. Nevertheless, variables such as pavement condition deterioration and improvement that are also associated with uncertainty, were not included in stochastic optimization models due to the expected large size of the optimization problem.</p><p>The overarching goal of this dissertation is to contribute to filling these research gaps with a view to improving pavement management systems, helping to predict probabilistic pavement performance and schedule pavement preventive maintenance accurately and reliably. This study reviews Markovian pavement performance models using various Markov methodologies and transition probabilities estimation methods, presents a critical analysis of the different aspects of Markovian models as applied in the literature, reveals gaps in knowledge, and offers suggestions for bridging those gaps. This dissertation develops a decision tree which could be used by researchers and highway agencies to select appropriate Markov methodologies to model pavement performance under different conditions of data availability. The lack of consideration of pavement PM impacts into probabilistic pavement performance models due to absence of historical PM data may result in erroneous and often biased pavement condition predictions, leading to non-optimal pavement maintenance decisions. Hence, this research introduces and validates a hybrid approach to incorporate the impact of PM into probabilistic pavement performance models when historical PM data are limited or absent. The types of PM treatments and their times of application are estimated using two approaches: (1) Analysis of the state of practice of pavement maintenance through literature and expert surveys, and (2) Detection of PM times from probabilistic pavement performance curves. Using a newly developed optimization algorithm, the estimated times and types of PM treatments are integrated into pavement condition data. A non-homogeneous Markovian pavement performance model is developed by estimating the transition probabilities of pavement condition using the ordered-probit method. The developed hybrid approach and performance models are validated using cross-validation with out-of-sample data and through surveys of subject matter experts in pavement engineering and management. The results show that the hybrid approach and models developed can predict probabilistic pavement condition incorporating PM effects with an accuracy of 87%.</p><p>The key Markov chain methodologies, namely, homogeneous, staged-homogeneous, non-homogeneous, semi- and hidden Markov, have been used to develop stochastic pavement performance models. This dissertation hypothesizes that the NCS and LDC significantly influence the prediction accuracy of Markov models and that the nature of such influence varies across the different Markov methodologies. As such, this study develops and compares the Markovian pavement performance models using empirical data and investigates the sensitivity of Markovian model prediction accuracy to the NCS and LDC. The results indicate that the semi-Markov is generally statistically superior to the homogeneous and staged-homogeneous Markov (except in a few cases of NCS and LDC combinations) and that Markovian model prediction accuracy is significantly sensitive to the NCS and LDC: an increase in NCS improves the prediction accuracy until a certain NCS threshold after which the accuracy decreases, plausibly due to data overfitting. In addition, an increase in LDC improves the prediction accuracy when the NCS is small.</p><p>Scheduling pavement maintenance at road network level without considering the uncertainty of pavement condition deterioration and improvement over the long-term (typically, pavement design life) likely results in mistiming maintenance applications and less optimal decisions. Hence, this dissertation develops stochastic pavement maintenance optimization models that account for the uncertainty of pavement condition deterioration and improvement as well as the budget constraint. The objectives of the stochastic optimization models are to minimize the overall deterioration of road network condition while minimizing the total maintenance cost of the road network over a 20-year planning horizon (typical pavement design life). Multi-objective Genetic Algorithm (MOGA) is used because of its robust search capabilities, which lead to global optimal solutions. In order to reduce the number of combinations of solutions of stochastic MOGA models, three approaches are proposed and applied: (1) using PM treatments that are most commonly used by highway agencies, (2) clustering pavement sections based on their ages, and (3) creating a filtering constraint that applies a rest period after treatment applications. The results of the stochastic MOGA models show that the Pareto optimal solutions change significantly when the uncertainty of pavement condition deterioration and improvement is included.</p>

Rail Control Solutions (RCS) is one division of Bombardier Transportation, aimed at optimising flow of trains. OPTIFLO is a new solution package within RCS, providing services and solutions to address challenges in modern railway infrastructures worldwide. Infrastructure Management (IM) Service is a significant sub-module under OPTIFLO, performing monitoring and diagnostic functionalities for each impacted system or component in railway signalling systems to continuously improve safety, reliability and availability. Requirement management is a significant stage while dealing with engineering problems. In this master thesis project, three modules in railway signalling scope are focused, including system level Infrastructure Management, sub-system level Maintenance and Diagnostic Centre (MDC) and sub-system level Remote Sensor Unit (RSU). For each part, requirement managements have been implemented, referring to CENELEC standards where necessary. The work starts with the draft Requirement Specification for IM and then identify the requirements related to diagnostics and performance in each sub-system. Both links between the requirements in different modules and links between the requirements and their test cases are built from the requirement management tool DOORS to realize verification and validation following the system engineering process. Finally, the standard documentations "System Requirement Specification" for each impacted module that are mostly concerned in the thesis have been released. / Rail Control Solutions (RCS) är en del av Bombardier Transportation, som syftar till att optimera flödet av tåg. OPTIFLO är ett nytt programspaket inom RCS, som erbjuder tjänster och lösningar för att hantera utmaningar inom modern järnvägsinfrastruktur världen över. Infrastrucutre Management (IM) Service är en viktig delmodul under OPTIFLO, som utför övervakning och diagnostiska funktioner för varje påverkat system eller komponent i järnvägssignalsystem för att kontinuerligt förbättra säkerhet, tillförlitlighet och tillgänglighet. Kravhantering är ett viktigt steg när man arbetar med tekniska problem. Det här mastersprojektet är inriktat på tre moduler inom järnvägssignalområdet: systemnivå Infrastructure Management, underhållssystem för Maintenance and Diagnostic Centre (MDC) och delsystemnivå Remote Sensor Unit (RSU). För varje del har kravhantering implementerats, med hänvisning till CENELEC-standarder vid behov. Arbetet har utgått från utkast till kravspecifikation för IM och identifierat kraven för diagnostik och prestanda i varje delsystem. Både kopplingar mellan kraven i olika moduler och kopplingar mellan kraven och deras testfall är byggda i systemet DOORS för att realisera verifiering och validering i en systemteknisk process. Slutligen släpps standarddokumentationen "Systemkrav Specifikation" för de moduler som behandlar i detta arbete.

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Prestressed concrete bridges are susceptible to deterioration over time which might significantly affect their capacity and overall performance. In previous decades, infrastructure owners have found that continuous monitoring of these assets is a valuable tool for their management as it facilitates the decision-making process regarding the intervention strategies required. However, as data acquisition and measurement technologies have advanced tremendously in recent years, the amount of information that can be retrieved daily is not easy to manage and analyse. This study presents an evaluation of the effectiveness between different machine learning methods regarding prediction and interpretation of structural responses as well as the feasibility of mapping an independent variable, aspects such as metric performance, learning curves and residual plots was analysed. A comparison was made on the machine learning algorithms performing regression analysis where each model scored over 98% in the R-square metric. This study utilised data collected from a prestressed concrete bridge located in Autio, northern Sweden, that has been continuously monitored for more than three years.

Record keeping and management of electrical utilities inside buildings is an important aspect to ensure effective electrical distribution. The ability to find the location of each electrical feature inside a building and extract information about it helps to solve network problems faster. The use of a spatial database structure facilitates the maintenance and general operations of an electrical network across different buildings. The aim of this study is to design and develop a 3D data model to provide a management system for electrical utilities inside buildings. The geodatabase provides integrated information between different electrical components forming the network inside the specified buildings in the study area. A prototype called the PUK geodatabase was designed and developed for the NWU Potchefstroom Campus as a 3D data model. The data model consists of raster and vector data used in network datasets, relationship classes and topology rules. The aim of this project was accomplished through the 3D analysis capabilities of the model. The research determined that the prototype called the PUK geodatabase can be utilized as a 3D management system for electrical utilities across the different floor levels of a building. / Thesis (M.Sc. (Geography and Environmental Studies))--North-West University, Potchefstroom Campus, 2012

Infrastructure privatization decisions have an enormous financial and social impact on all stakeholders, including the public sector, the private sector, and the general public. Appropriate privatization decisions, however, are difficult to make due to the conflicting nature of the objectives of the various stakeholders. This research introduces a multi-criteria decision-making framework for evaluating and comparing a wide range of privatization schemes for infrastructure facilities. The framework is designed to resolve conflicts that arise because of the varying points of view of the stakeholders, and accordingly, determine the most appropriate decision that satisfies all stakeholders’ preferences. The developed framework is expected to help in re-engineering the traditional conflict resolution process, particularly for construction conflict resolution and infrastructure privatization decisions. The framework provides decision support at the management level through three successive decision support processes related to 1. Screening of feasible solutions using the Elimination Method of multiple criteria decision analysis (MCDA); 2. Analyzing the actions and counteractions of decision makers using conflict resolution and decision stability concepts to determine the most stable resolution; and 3. Considering the uncertainty in decision maker’s preferences using Info-gap Theory to evaluate the robustness of varying uncertainty levels of the decisions. Based on the research, a procedure and a decision support system (DSS) have been developed and tested on real-life case studies of a wastewater treatment plant and a construction conflict. The results of the two case studies show that the proposed DSS can be used to support decisions effectively with respect to both construction conflicts and infrastructure privatization. The developed system is simple to apply and can therefore save time and avoid the costs associated with unsatisfactory decisions. This research is expected to contribute significantly to the understanding and selecting of proper Public-Private-Partnership (PPP) programs for infrastructure assets.

Infrastructure privatization decisions have an enormous financial and social impact on all stakeholders, including the public sector, the private sector, and the general public. Appropriate privatization decisions, however, are difficult to make due to the conflicting nature of the objectives of the various stakeholders. This research introduces a multi-criteria decision-making framework for evaluating and comparing a wide range of privatization schemes for infrastructure facilities. The framework is designed to resolve conflicts that arise because of the varying points of view of the stakeholders, and accordingly, determine the most appropriate decision that satisfies all stakeholders’ preferences. The developed framework is expected to help in re-engineering the traditional conflict resolution process, particularly for construction conflict resolution and infrastructure privatization decisions. The framework provides decision support at the management level through three successive decision support processes related to 1. Screening of feasible solutions using the Elimination Method of multiple criteria decision analysis (MCDA); 2. Analyzing the actions and counteractions of decision makers using conflict resolution and decision stability concepts to determine the most stable resolution; and 3. Considering the uncertainty in decision maker’s preferences using Info-gap Theory to evaluate the robustness of varying uncertainty levels of the decisions. Based on the research, a procedure and a decision support system (DSS) have been developed and tested on real-life case studies of a wastewater treatment plant and a construction conflict. The results of the two case studies show that the proposed DSS can be used to support decisions effectively with respect to both construction conflicts and infrastructure privatization. The developed system is simple to apply and can therefore save time and avoid the costs associated with unsatisfactory decisions. This research is expected to contribute significantly to the understanding and selecting of proper Public-Private-Partnership (PPP) programs for infrastructure assets.

Since the end of World War II the United States has grown and maintained a stockpile of nuclear weapons in the interest of preserving world peace, and with the specific intent to provide unparalleled national security to its citizens. It was a commonly held view during this time that a large diverse stockpile was a fundamental key to national security. However, in today’s ever-changing environment, Los Alamos National Laboratory finds itself with an infrastructure unable to quickly adapt to new national security needs and threats. Burdened by the management of a Cold-War-era stockpile, nuclear operations at Los Alamos will benefit from a reduced stockpile initiative. Contrary to previously held beliefs, Los Alamos can be the prime beneficiary to such an approach, and use such a monumental shift in strategy to modernize infrastructure, revitalize critical staff, and effectively manage critical materials and facilities while simultaneously reducing waste and environmental impacts to better support national security needs. / text

The Health Facilities Infrastructure Management Programme in South Africa aims to ensure an appropriate and sustainable platform for the delivery of health services. Since 1994, the average number of hospital beds has decreased from 4.4 beds per 1 000 people to 2.4 beds per 1 000 people. During the same period, there was no significant reduction in the 1 372 clinic backlog. The evaluation of the performance of the Health Facilities Infrastructure Management Programme was based on a systems approach. This performance evaluation was conducted across four dimensions, with 12 assessment instruments and within 134 assessment parameters. Several of these instruments were developed as part of this study. Actual performance, per assessment parameter, was expressed in terms of a four level project management maturity scale. About one third of the parameters indicated a low level of project management maturity, one third indicating a medium-low level of maturity, with less than 10% judged to have reached maturity. It was found that the Infrastructure Unit in the National Department of Health is solely responsible for addressing more than half of the performance areas described by the assessment parameters. The proposed prioritisation model indicated that 50% of the performance areas needed to be addressed as a matter of urgency. The study concludes with 10 system transformation recommendations aimed at maturity growth in the Infrastructure Unit in the National Department of Health, as well as maturity growth in the Health Facilities Infrastructure Management Programme as a whole. The following key terms are relevant: • Health Facilities Infrastructure Management Programme • Performance evaluation • Infrastructure Unit • National Department of Health of South Africa • Project management maturity • Assessment instruments • Assessment parameters • Prioritisation model / Thesis (MArt et Scien (Urban and Regional Planning))--North-West University, Potchefstroom Campus, 2013.