There are existing standards and guidelines for the effective management of infrastructure through infrastructure asset management planning (IAM). However, few if any of these standards explicitly address the financial implications associated with the uncertainty that underlies the risk associated with service provision. Without credibly quantifying the potential implications of this network variable uncertainty (i.e. an extreme weather event that affects the performance and costs of many segments within the study network, or the introduction of a new technology that may impact the network cost estimates) infrastructure management systems may actually regularly and significantly over or under estimate the actual financial requirements required to provide services. Therefore, financial projections may actually include a systematic bias. It was hypothesized that a model could be developed that quantifies and communicates the financial implications of network variable uncertainty within the IAM context. A model was developed to demonstrate how network variable uncertainty could be included in financial planning for infrastructure networks. The model was able to: (1) be applied to various types of infrastructure networks, (2) incorporate network variable uncertainty, (3) compare alternatives and scenarios, and (4) support effective communication of results. The outputs of the model were the average network annual worth (AW) and network present worth (PW). These outputs, along with tornado plots, risks curves, level of service dashboards, and existing budget levels, were used to communicate the impacts of the network variable uncertainty on the financial projections. The model was developed using Excel tools linked to DPL software to utilize probabilistic methods. The Life Cycle Cost (LCC) portion of the model was successfully verified against an existing infrastructure costing tool, the Land and Infrastructure Resiliency Assessment (LIRA) tool developed by the Agri-Environmental Services Branch of Agriculture and Agri-Food Canada. The impact of the network variable uncertainty within the variables was also quantified in terms of levels of service provided by the organization. The developed model was first applied to a hypothetical twelve segment road network for illustrative purposes. For the hypothetical road network there were four events, representing network variable uncertainty, that were considered. These decisions or events included the: (1) decision to implement a new technology, (2) event of changing standards, (3) event of increased material costs, and (4) occurrence of an extreme rainfall event. The hypothetical network illustrated that if the defined decisions or events occurred then the expected network AW would increase by 41%. The impacts of decisions or events on the hypothetical network levels of service, stemming from network variable uncertainty, were also considered. The measured levels of service for the hypothetical network included the network financial sustainability indicator (an indicator reflecting the network current budget divided by the network annual worth as a percentage) and the frequency of blading of the roads. The model was next applied to a case study using the Town of Shellbrook sanitary main network. The Town has a large quantity of aging mains which were constructed in the 1960’s and are expected to require renewal in the near term. The network variable uncertainty for the case study resulted from the potential decision to implement a new trenchless technology for the renewal of sanitary mains. The new technology was expected to decrease the renewal costs. However, there was uncertainty as to what percentage of the sanitary mains would be found to be suitable for the new technology. Using the model it was determined that if the decision was made to implement the new technology, there would be an expected reduction of 17% in the network AW. The levels of service that were used for the Shellbrook case study were the network financial sustainability indicator (annual budget / network AW) and the meeting of standards set by regulating bodies. It was determined that the network financial sustainability indicator was sensitive to the decision to implement the trenchless technology, while the meeting of regulating bodies was not. If the decision was made to implement the new technology the network sustainability indicator would be expected to increase from 28% (if the new technology was not implemented) to 34% (if the new technology were implemented). The model was finally applied to a case study looking at the RM of Wilton gravel road network. The network variable uncertainty for this case study resulted from the potential increase in gravel material costs. The network variable uncertainty represented the magnitude of the annual increase in gravel costs. Given the event of increasing gravel costs the expected network AW would increase by 14%. The levels of service indicators used for the RM of Wilton case study were the network financial sustainability indicator and the frequency of blading. It was determined that the network financial sustainability indicator was sensitive to the event (increasing gravel costs), while the frequency of blading was not directly impacted (although it may be indirectly impacted). If the event of increasing gravel costs were to occur then the network financial sustainability indicator would be expected to decrease from 59% (if gravel costs did not increase) to 52% (if gravel costs did increase). This research proved that the hypothesis was correct, and that a model could be developed that quantified and communicated the financial implications and level of service impacts of network variable uncertainty for IAM planning. This research illustrated and quantified that IAM planning without accounting for network variable uncertainty, such as: (1) changing technology, (2) changing standards, (3) increasing material costs, and (4) extreme weather events, managers may introduce a systematic bias into long term planning. Network variable uncertainty can significantly impact the projected expenditures required for the long term provision of services. Infrastructure managers and decision makers need to manage infrastructure in a sustainable way over the long term in the face of uncertainty. It is necessary that decision makers have information regarding the impacts of network variable uncertainty on both LCCs and levels of service to make fully informed decision.

The goal of this dissertation research is to model risk in delivery, operation and maintenance phases of infrastructure asset management. More specifically, the two main objectives of this research are to quantify and measure financial risk in privatizing and operational risks in maintenance and rehabilitation of infrastructure facilities. To this end, a valuation procedure for valuing large-scale risky projects is proposed. This valuation approach is based on mean-risk portfolio optimization in which a risk-averse decision-maker seeks to maximize the expected return subject to downside risk. We show that, in complete markets, the value obtained from this approach is equal to the value obtained from the standard option pricing approach. Furthermore, we introduce Coherent Valuation Procedure (CVP) for valuing risky projects in partially complete markets. This approach leads to a lower degree of subjectivity as it only requires one parameter to incorporate user's risk preferences. Compared to the traditional discounted cash flow analysis, CVP displays a reasonable degree of sensitivity to the discount rate since only the risk-free rate is used to discount future cash flows. The application of this procedure on valuing a transportation public-private partnership is presented. %and demonstrate that the breakeven buying price of a risky project is equal to the value obtained from this valuation procedure. Secondly, a risk-based framework for prescribing optimal risk-based maintenance and rehabilitation (M&R) policies for transportation infrastructure is presented. These policies guarantee a certain performance level across the network under a predefined level of risk. The long-term model is formulated in the Markov Decision Process framework with risk-averse actions and transitional probabilities describing the uncertainty in the deterioration process. Conditional Value at Risk (CVaR) is used as the measure of risk. The steady-state risk-averse M&R policies are modeled assuming no budget restriction. To address the short-term resource allocation problem, two linear programming models are presented to generate network-level polices with different objectives. In the first model, decision-maker minimizes the total risk across the network, and in the second model, the highest risk to the network performance is minimized.

The South African Ports are considered a key engine for the economic growth of the country for import and export trade as well as passenger ships. In order to provide such services, the ports' waterside / wet concrete assets are pivotal to the business as trade and travel are reliant on the availability of safe and well-maintained concrete assets. Transnet is a State-Owned Company (SOC), wholly owned by the Government of the Republic of South Africa and is the custodian of rail, ports, and pipelines. The asset management and maintenance of Transnet's infrastructure assets are, therefore, the cornerstone of delivering on their mandate as a SOC. Moreover, to deliver on their mandate, systematic, holistic, and integrated approaches to asset management and maintenance of their assets are imperative. This dissertation focuses on the Transnet National Ports Authority (TNPA) division. It has a very large asset base of Infrastructure, in particular its marine concrete infrastructure. Regulated by the National Ports Act 2005 (Act No. 12 of 2005), some of their core functions are the planning, provision, maintenance, and improvement of port infrastructure. This dissertation gives a background on the structure of the South African Ports and operations and the types and age of concrete structures within the ports. The study also critically assesses the existence of and the type of Infrastructure Asset Management & Maintenance (IAMM) systems currently in place for the Asset Management and Maintenance of TNPA's concrete infrastructure assets. The approach and methods of managing and assessing assets' condition and maintaining their existing concrete structures are reviewed to ascertain whether their asset management systems are aligned and conform to certain IAMM standards, codes, and guidelines. The Asset Maintenance Principles & Procedures (AMPP) document is also reviewed for its effectiveness in maintaining assets. In order to get a holistic idea of the extent of the possible shortcomings of TNPA's current maintenance and asset management strategies, other ports around the world with similar or the same concrete infrastructure are identified and assessed for commonalities and deviations from best practices. The research methodology used is qualitative using the analysis of existing text and literature as well as case studies of other ports as a source of data. The findings of the research show that although there are good maintenance systems and guidelines in place with some elements of asset management principles, an all encompassing civil infrastructure asset management framework for the marine concrete assets does not exists where the benefits of a properly implemented asset management framework can be realised. The research also shows that asset management is largely treated as a financial exercise with finance being the custodian of asset management. Recommendations are made for dealing with the shortcomings identified. Recommendations are also made for a more in-depth case study for the TNPA to conduct based on the findings of this dissertation.

Yes / Public–Private Partnerships (PPPs) have become a critical vehicle for delivering infrastructure worldwide. Yet, the use of such a procurement strategy has received considerable criticism, as they have been prone to experiencing time/cost overruns and during their operation poorly managed. A key issue contributing to the poor performance of PPPs is the paucity of an effective and comprehensive performance measurement system. There has been a tendency for the performance of PPPs to be measured based on their ex-post criteria of time, cost and quality. Such criteria do not accommodate the complexities and lifecycle of an asset. In addressing this problem, the methodology of sequential triangulation is used to develop and examine the effectiveness of a ‘Process Management Life Cycle Performance Measurement System’. The research provides public authorities and private-sector entities embarking on PPPs with a robust mechanism to effectively measure, control and manage their projects’ life cycle performances, ensuring the assets are ‘future proofed’.

The effective implementation of infrastructure asset management systems within organizations that own, operate, and manage infrastructure assets is critical to address the main challenges facing the infrastructure industry (e.g., infrastructure ageing and deterioration, maintenance backlogs, strict regulatory operating conditions, limited financial resources, and losing valuable experience through retirements). Infrastructure asset management systems contain connectivity between major operational components and such connectivity can lead to systemic risks (i.e., dependence-induced failures). This thesis analyzes the asset management system as a network of connected components (i.e., nodes and links) to identify critical components exposed to systemic risks induced by information asymmetry and information overload. This thesis applies descriptive and prescriptive analytics strategies to address information asymmetry and information overload and predictive analytics is employed to enhance the resilience. Specifically, descriptive analytics was employed to visualize the key performance indicators of infrastructure assets ensuring that all asset management stakeholders make decisions using consistent information sources and that they are not overwhelmed by having access to the entire database. Predictive analytics is employed to classify the resilience key performance indicator pertaining to the forced outage rapidity of power infrastructure components enabling power infrastructure owners to estimate the rapidity of an outage soon after its occurrence, and thus allocating the appropriate resources to return the infrastructure to operation. Using predictive analytics allows decision-makers to use consistent and clear information to inform their decision to respond to forced outage occurrences. Finally, prescriptive analytics is applied to optimize the asset management system network by increasing the connectivity of the network and in turn decreasing the exposure of the asset management system to systemic risk from information asymmetry and information overload. By analyzing an asset management system as a network and applying descriptive-, predictive-, and prescriptive analytics strategies, this dissertation illustrates how systemic risk exposure, due to information asymmetry and information overload could be mitigated and how power infrastructure resilience could be enhanced in response to forced outage occurrences. / Thesis / Doctor of Science (PhD) / Effective infrastructure asset management systems are critical for organizations that own, manage, and operate infrastructure assets. Infrastructure asset management systems contain main components (e.g., engineering, project management, resourcing strategy) that are dependent on information and data. Inherent within this system is the potential for failures to cascade throughout the entire system instigated by such dependence. Within asset management, such cascading failures, known as systemic risks, are typically caused by stakeholders not using the same information for decision making or being overwhelmed by too much information. This thesis employs analytics strategies including: i) descriptive analytics to present only relevant and meaningful information necessary for respective stakeholders, ii) predictive analytics to forecast the resilience key performance indicator, rapidity, enabling all stakeholders to make future decisions using consistent projections, and iii) prescriptive analytics to optimize the asset management system by introducing additional information connections between main components. Such analytics strategies are shown to mitigate the systemic risks within the asset management system and enhance the resilience of infrastructure in response to an unplanned disruption.

Human inspection is a pivotal component of infrastructure asset management within a systems thinking approach to civil engineering. Skilled inspectors are tasked with the evaluation of various civil infrastructure components, conducting assessments of their conditions, identifying maintenance needs, and determining necessary repairs. Despite the growing interest in advanced technologies and automated inspections, the use of human-in-the-loop procedures is still widely practiced. Humans are susceptible to cognitive bias, variability, or uncertainty when inspecting infrastructure, and finding solutions to reduce these factors is paramount. This study presents a comprehensive exploration of inspection variability within infrastructure asset management, drawing insights from datasets of the BUILDER Sustainment Management System (SMS) program. The research delves into infrastructure inventory, inspector data, and inspection data components of an asset management database, shedding light on variability in human inspection. Variations in inspection ratings revealed significant concerns, particularly in Mechanical, Electrical, and Plumbing (MEP) systems, with notable disparities between inspection ratings and condition ratings. Inspector variability analysis, through Coefficient of Variation calculations, indicated substantial disparities within and among inspectors. Further analysis, including Tukey's HSD test, pinpointed significant variability in heating, ventilation, and air conditioning (HVAC) and Fire Protection system inspections. Moreover, this study addresses the specific challenge of reducing inspection uncertainty in HVAC systems. HVAC systems play a critical role in facility energy consumption, and their maintenance is vital to energy efficiency and occupant comfort. However, HVAC-specific inspections primarily require human involvement, making them time-consuming and prone to error. Addressing the challenges surrounding human inspection of HVAC systems, this research presents a multifaceted approach to reduce variability. Drawing from a review of existing literature on HVAC inspection uncertainty, this study extends its focus to the development of predictive models. These models considered parameters including inspection ratings, age-based obsolescence, section condition indices, component characteristics, and unique inspectors . Utilizing Linear Regression, Random Forest, and Gradient Boosting Regression, this model accurately predicted Variability Ratings, signifying the potential for implementation as a decision support tool. Importantly, the findings highlight the need to not only understand the factors affecting HVAC inspection variability but to actively implement technological solutions that can reduce human error and variability in inspections. / Master of Science / Infrastructure inspection is crucial for maintaining buildings and facilities, but it often comes with human errors and uncertainties. This study looks at the inspection process, focusing on case studies and data from the BUILDER Sustainment Management System (SMS) program. It reveals that inspectors sometimes evaluate the condition of parts of a building differently, leading to inconsistencies and poor overall management. One significant area of concern is heating, ventilation, and air conditioning (HVAC) systems. These systems play a critical role in facility energy use and can be challenging to inspect accurately. Previous research has shown that work experience, training, education, and other factors tend to contribute to variability in how inspectors assess HVAC systems. This research not only highlights these issues but also develops predictive models to reduce the variability of HVAC inspections. By doing so infrastructure can be managed correctly and ultimately lead to improved building lifecycles.

Performance prediction modeling is a crucial step in assessing the remaining service life of pipelines. Sound infrastructure deterioration models are essential for accurately predicting future performance that, in turn, are critical tools for efficient maintenance, repair and rehabilitation decision making. The objective of this research is to develop a gravity and force main pipe performance deterioration model for predicting the remaining economic life of wastewater pipe for infrastructure asset management. For condition assessment of gravity pipes, the defect indices currently in practice, use CCTV inspection and a defect coding scale to assess the internal condition of the wastewater pipes. Unfortunately, in practice, the distress indices are unable to capture all the deterioration mechanisms and distresses on pipes to provide a comprehensive and accurate evaluation of the pipe performance. Force main pipes present a particular challenge in performance prediction modeling. The consequence of failure can be higher for the force mains relative to the gravity pipes which increases the risk associated with these assets. However, unlike gravity pipes, there are no industry standards for inspection and condition assessment for force mains. Furthermore, accessibility issues for inspections add to this challenge. Under Water Environmental and Reuse Foundation (WEandRF)'s Strategic Asset Management (SAM) Challenge, there was a planned three-phase development of this performance prediction model. Only Phases 1 and 2 were completed for gravity pipes under the SAM Challenge. Currently, 37 utilities nationally distributed have provided data and support for this research. Data standards are developed to capture the physical, operational, structural, environmental, financial, and other factors affecting the performance. These data standards were reviewed by various participating utilities and service providers for completeness and accuracy. The performance of the gravity and force main pipes are assessed with incorporating the single and combined effects of these parameters on performance. These indices assess the performance regarding; integrity, corrosion, surface wear, joint, lining, blockage, IandI, root intrusion, and capacity. These performance indices are used for the long-term prediction of performance. However, due to limitations in historical performance data, an advanced integrated method for probabilistic performance modeling to construct workable transition probabilities for predicting long-term performance has been developed. A selection process within this method chooses a suitable prediction model for a given situation in terms of available historical data. Prediction models using time and state-dependent data were developed for this prediction model for reliable long-term performance prediction. Reliability of performance assessments and long-term predictions are tested with the developed verification and validation (VeandVa) framework. VeandVa framework incorporates piloting the performance index and prediction models with artificial, field, and forensic data collected from participating utilities. The deterioration model and the supporting data was integrated with the PIPEiD (Pipeline Infrastructure Database) for effective dissemination and outreach. / PHD

Maintenance of transport infrastructure assets is widely advocated as the key in minimizing current and future costs of the transportation network. While effective maintenance decisions are often a result of engineering skills and practical knowledge, efficient decisions must also account for the net result over an asset's life-cycle. One essential aspect in the long term perspective of transport infrastructure maintenance is to proactively estimate maintenance needs. In dealing with immediate maintenance actions, support tools that can prioritize potential maintenance candidates are important to obtain an efficient maintenance strategy. This dissertation consists of five individual research papers presenting a microdata analysis approach to transport infrastructure maintenance. Microdata analysis is a multidisciplinary field in which large quantities of data is collected, analyzed, and interpreted to improve decision-making. Increased access to transport infrastructure data enables a deeper understanding of causal effects and a possibility to make predictions of future outcomes. The microdata analysis approach covers the complete process from data collection to actual decisions and is therefore well suited for the task of improving efficiency in transport infrastructure maintenance. Statistical modeling was the selected analysis method in this dissertation and provided solutions to the different problems presented in each of the five papers. In Paper I, a time-to-event model was used to estimate remaining road pavement lifetimes in Sweden. In Paper II, an extension of the model in Paper I assessed the impact of latent variables on road lifetimes; displaying the sections in a road network that are weaker due to e.g. subsoil conditions or undetected heavy traffic. The study in Paper III incorporated a probabilistic parametric distribution as a representation of road lifetimes into an equation for the marginal cost of road wear. Differentiated road wear marginal costs for heavy and light vehicles are an important information basis for decisions regarding vehicle miles traveled (VMT) taxation policies. In Paper IV, a distribution based clustering method was used to distinguish between road segments that are deteriorating and road segments that have a stationary road condition. Within railway networks, temporary speed restrictions are often imposed because of maintenance and must be addressed in order to keep punctuality. The study in Paper V evaluated the empirical effect on running time of speed restrictions on a Norwegian railway line using a generalized linear mixed model.

Kyoto University (京都大学) / 0048 / 新制・課程博士 / 博士(工学) / 甲第14919号 / 工博第3146号 / 新制||工||1472(附属図書館) / 27357 / UT51-2009-M833 / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 小林 潔司, 教授 大津 宏康, 教授 河野 広隆 / 学位規則第4条第1項該当

Infrastructure consists of facilities and services that are considered essential to the functioning and economic productivity of society (Preqin, 2022). The rapid economic growth over the past decades has led to an increase in the demand for fundamental functions such as energy, telecommunications, and transportation. Increases in infrastructure investments are required on a global scale, not only to support economic growth globally, but also to help fulfill the United Nations Sustainable Development Goals (SDGs). Despite the large demand for infrastructure investments globally, planned governmental investments in infrastructure are not enough to bridge the gap. In order to bridge the infrastructure investment gap, there is a need for institutional investors to intervene, both in private and public markets. However, investors need to be able to assess how the market is performing as well as have accessible investment products that are linked to the infrastructure asset class. The quality of such investment products is dependent on the indices they are linked to, making it essential that infrastructure indices reflect the asset class in the way investors expect them to. Therefore, there is a need to assess the performance of existing infrastructure indices as well as to find a methodology for constructing a new index that more accurately fulfills investor expectations. This thesis compares three existing infrastructure indices in terms of three investment characteristics that investors look for in infrastructure investments: risk adjusted returns, inflation hedging properties, and stability during market downturns. These characteristics are evaluated by measuring the sharpe ratio, correlation with the consumer price index, and downmarket capture ratio. The thesis also proposes a methodology for finding an index that more accurately represents the infrastructure asset class in terms of the three aforementioned investment characteristics. The methodology is based on parameter optimization to find the set of inclusion criteria that maximizes performance. The thesis finds that the infrastructure indices are adequate in terms of risk adjusted returns and inflation hedging properties, but that they do not show consistent performance during down markets. It is concluded that existing indices are somewhat sufficient in what they set out to do, but that there is room for improvement in capturing the desired characteristics. The results of the thesis also indicate that a new index that more accurately represents the infrastructure asset class can be constructed by implementing inclusion criteria based on filters related to financial ratios associated with infrastructure companies, such as fixed asset turnover and dividend yield. In conclusion, an index with a minimum dividend yield criterion and a maximum fixed asset turnover ratio criterion can be constructed to more accurately capture the key characteristics of the infrastructure asset class. / Infrastruktur består av anläggningar och tjänster som anses väsentliga för samhället (Preqin, 2022). Den stora ekonomiska tillväxten under de senaste årtiondena har lett till ökad efterfrågan på grundläggande funktioner som energi, telekommunikation och transport. Ökade infrastrukturinvesteringar krävs globalt, inte bara för att stödja tillväxt utan också för att hjälpa till att uppfylla FN:s mål för hållbar utveckling (SDG). Trots den stora efterfrågan på infrastrukturinvesteringar globalt räcker inte planerade statliga investeringar i infrastruktur för att överbrygga klyftan. För att kunna möta behovet av infrastrukturinvesteringar behöver investerare ingripa, både på privata och publika marknader. Investerare behöver dock kunna bedöma hur marknaden presterar samt ha tillgängliga investeringsprodukter som är kopplade till infrastruktur som tillgångsklass. Kvaliteten på sådana investeringsprodukter är beroende av vilka index de är kopplade till, och därmed är det viktigt att infrastrukturindex speglar tillgångsklassen i enlighet med investerares förväntningar. Därmed finns det ett behov av att bedöma hur nuvarande infrastrukturindex presterar samt eventuellt att hitta en metod för att konstruera ett nytt index som bättre uppfyller investerarnas förväntningar. Denna avhandling jämför tre befintliga infrastrukturindex i termer av tre investeringsegenskaper som investerare söker i infrastrukturinvesteringar: riskjusterad avkastning, inflationsskyddande egenskaper och stabilitet under nedgångar på marknaden. Dessa egenskaper utvärderas genom att mäta sharpe ratio, korrelation med ett konsumentprisindex, samt down market capture ratio. Avhandlingen föreslår också en metodik för att hitta ett index som bättre representerarinfrastruktur som tillgångsklass i termer av de tre ovan nämnda investeringsegenskaperna. Metodiken är baserad på parameteroptimering för att hitta den uppsättning inklusionskriterier som maximerar indexets prestation.Avhandlingen konstaterar att infrastrukturindexen är tillräckliga i termer av riskjusterad avkastning och inflationssäkrande egenskaper, men att de inte uppvisar konsekvent prestation under marknadsnedgångar. Slutsatsen som dras är att de nuvarande indexen delvis är tillräckliga i det de avser uppfylla, men att det finns utrymme för förbättringar. Resultaten av avhandlingen indikerar också att ett nytt index som mer träffsäkert representerar infrastruktur som tillgångsklass kan konstrueras genom att implementera inklusionskriterier baserade på filter relaterade till finansiella nyckeltal förknippade med infrastrukturföretag, såsom omsättning av anläggningstillgångar och direktavkastning. Sammanfattningsvis kan ett index med ett minimimumkriterium för direktavkastning och maximumkriterium för omsättning av anläggningstillgångar konstrueras för att mer träffsäkert fånga viktiga egenskaper hos infrastruktur som tillgångsklass.