abstract: Imagery data has become important for civil infrastructure operation and maintenance because imagery data can capture detailed visual information with high frequencies. Computer vision can be useful for acquiring spatiotemporal details to support the timely maintenance of critical civil infrastructures that serve society. Some examples include: irrigation canals need to maintain the leaking sections to avoid water loss; project engineers need to identify the deviating parts of the workflow to have the project finished on time and within budget; detecting abnormal behaviors of air traffic controllers is necessary to reduce operational errors and avoid air traffic accidents. Identifying the outliers of the civil infrastructure can help engineers focus on targeted areas. However, large amounts of imagery data bring the difficulty of information overloading. Anomaly detection combined with contextual knowledge could help address such information overloading to support the operation and maintenance of civil infrastructures. Some challenges make such identification of anomalies difficult. The first challenge is that diverse large civil infrastructures span among various geospatial environments so that previous algorithms cannot handle anomaly detection of civil infrastructures in different environments. The second challenge is that the crowded and rapidly changing workspaces can cause difficulties for the reliable detection of deviating parts of the workflow. The third challenge is that limited studies examined how to detect abnormal behaviors for diverse people in a real-time and non-intrusive manner. Using video andii relevant data sources (e.g., biometric and communication data) could be promising but still need a baseline of normal behaviors for outlier detection. This dissertation presents an anomaly detection framework that uses contextual knowledge, contextual information, and contextual data for filtering visual information extracted by computer vision techniques (ADCV) to address the challenges described above. The framework categorizes the anomaly detection of civil infrastructures into two categories: with and without a baseline of normal events. The author uses three case studies to illustrate how the developed approaches can address ADCV challenges in different categories of anomaly detection. Detailed data collection and experiments validate the developed ADCV approaches. / Dissertation/Thesis / Doctoral Dissertation Civil, Environmental and Sustainable Engineering 2020

Maintaining current and future infrastructure will require smart practices to help better meet user needs with fewer financial resources. The recent adoption of information communication technologies, such as, 311-call centers enables city agencies to detect and more quickly respond to real-time infrastructure system service disruptions and maintenance requests. Of the 200 or more cities that use 311, New York City's system is the largest, receiving more than 19.5 million citizen requests since 2010. Current citizen service requests made through 311 range from issues about street and sidewalk conditions to problems with their water, sanitation, snow removal, and traffic congestion. In the first manuscript, service requests were compared to socio-economics within zip codes. Zip codes were clustered by four socio-economic variables including median house value, percent of the population with a bachelor's degree, unemployment rate, and percent non-white to represent socio-economic differences between zones in the city. Results show that citizens from low socio-economic areas, meaning those with low median house values, low population with a bachelor's degree, high unemployment, and high percent non-white are burdened with significantly more infrastructure maintenance requests. When controlling for physical differences such as miles of road, total frequency of calls, and the number of people per zip code, people from low socio-economic zones are more likely to call about issues related to street conditions, sanitation, and their water system. In the second manuscript, service request response time by agency were compared based on location and socio-economic variables. The location of the call based on borough and the socio-economic characteristics of the zip code do significantly influence agency response time. Citizens reporting issues in Queens can expect to wait significantly longer, about 3 days more, to receive a response for a similar request in other boroughs of New York City. This is for issues about water, sewer, traffic lights, and street condition. The Department of Transportation, Department of Sanitation, and the New York Police Department respond significantly faster to service requests in zones classified with high and middle socio-economic groups compared to zip codes with low socio-economic groups of people. These differences in geography and socio-economic characteristics suggest unequal treatment of maintenance issues. These differences in response may expose an implicit bias in maintenance response. By recognizing these differences, city engineers can begin to prioritize maintenance issues based on how communities perceive infrastructure in need of repair, and thus better meet the needs of individual citizens in the future. / Master of Science / Infrastructure includes systems including buildings, roads, water/wastewater, trash, and various other networks that facilitate citizens everyday lives. These infrastructure systems will always require maintenance in order to keep it running effectively and efficiently. Using smart and sustainable practices in this process can help better meet user needs, while saving more money. Using more technology such as 311-call centers can allow cities to detect and more quickly respond to real-time infrastructure disruptions and maintenance issues. 311 call centers receive calls about anything from street condition problems such as potholes to problems with their drinking water. Of the 200 or more cities that use 311, New York City’s system is the largest, receiving more than 20 million citizen requests since 2010. The first manuscript looks into what certain types of people call about. Using zip codes, areas of the city were grouped together based on four socio-economic variables; median house value, percent of the population with a bachelor’s degree, unemployment rate, and percent non-white. People from low socio-economic areas, meaning those with low median house values, low population with a bachelor’s degree, high unemployment, and high percent non-white call more about infrastructure maintenance requests. When controlling for physical aspects of these areas such as miles of road, total amount of calls, and the number of people per zip code, people from low socio-economic areas are more likely to call about issues related to street conditions, sanitation, and their water system. The second manuscript looks into how long it takes government agencies to respond and resolve these calls. The location of the call based on location in the city and the socio-economic characteristics of the zip codes have an effect on agency response time. People reporting issues in Queens can expect to wait much longer, about 3 days more, to receive a response for a similar issue called from another borough of New York City. The Department of Transportation, Department of Sanitation, and the New York Police Department respond significantly faster to service requests in areas with high and middle socio-economic status groups compared to zip codes with low socio-economic groups of people. By knowing that location and socio-economic status matter when citizens call 311, city engineers can begin to use this data to help prioritize maintenance issues based on specific areas and needs of individual people in the future. These differences in location and socio-economic characteristics could possibly suggest unequal treatment of maintenance issues. However, since the differences seen in this research are with only certain variables accounted for, further research will be needed to help show possible causation for these differences.

Performance goals for a highway system are an indication of the desired system condition, and the level of service to be provided to its users. Setting the appropriate performance goals has a significant impact on the way highway agencies conduct business. With growing needs and limited resources, the consequences of setting different levels of performance goals should be examined and compared to optimize the highway infrastructure needs at the network level. Three interacting sets of costs are typically considered for a complete economic appraisal of highway projects: construction, maintenance and road use costs. Due to the shift in focus from design-and-build mode to the repair-and-maintain mode, this study focuses on maintenance related costs and the road user cost aspects only. Maintenance and rehabilitation activities on pavement infrastructure are ongoing processes that are required for the entire road network. This suggests that for long planning horizons and geographically extensive networks, their application usually results in significant financial needs. Typically, highway agencies have based their policy decisions such as the target condition levels for the system on the budget needs for maintenance and rehabilitation actions. Since in most cases, the funding needs exceed the available budget, the required preventive and routine maintenance activities suffer or are overlooked completely. Failure to timely apply these maintenance actions cause the pavements to deteriorate more rapidly into condition states that require for more expensive rehabilitation actions during the life cycle of the pavement. Over time, a vicious cycle is instigated in which the maintenance and rehabilitation needs of the network keep increasing each year. Although most highway administrators acknowledge the fact that pavement preservation is perhaps the most effective way of using the limited budgets available, the costs associated with deferring maintenance actions is oftentimes overlooked when establishing performance goals for the system. Road user costs in the form of costs for vehicle operation have been recognized as another large component of the total transportation related costs. These costs are then arguably the most important to consider for a complete economic appraisal. Ironically, they are also often disregarded while making important policy decisions. Other road user costs such as those related to the impact of traffic congestion and detours caused by construction and maintenance activities are difficult to quantify and were not accounted for in this study. Although it is widely accepted that establishing suitable performance goal is critical for system maintenance and preservation, a framework that considers the inter-relationship between conflicting objectives of minimum maintenance and rehabilitation costs, deferred maintenance costs, and vehicle operating costs to the users does not exist. This thesis proposes a methodological framework that is aimed at assisting highway agencies with the problem of objectively analyzing policy decisions in terms of the performance goals for their highway networks that would minimize the total transport costs to the society. In a case study of the proposed framework, the highway network managed by the Texas Department of Transportation was examined for different performance goals. The results from the case study indicate that setting lower performance goals lead to savings in the M&R needs, but at the same time, they also significantly increase the exogenous costs such as deferred maintenance costs and the vehicle operating costs. / text

Infrastructure management agencies are facing multiple challenges, including aging infrastructure, reduction in capacity of existing infrastructure, and availability of limited funds. Therefore, decision makers are required to think innovatively and develop inventive ways of using available funds. Maintenance investment decisions are generally made based on physical condition only. It is important to understand that spending money on public infrastructure is synonymous with spending money on people themselves. This also requires consideration of decision parameters, in addition to physical condition, such as strategic importance, socioeconomic contribution and infrastructure utilization. Consideration of multiple decision parameters for infrastructure maintenance investments can be beneficial in case of limited funding. Given this motivation, this dissertation presents a prototype decision support framework to evaluate trade-off, among competing infrastructures, that are candidates for infrastructure maintenance, repair and rehabilitation investments. Decision parameters’ performances measured through various factors are combined to determine the integrated state of an infrastructure using Multi-Attribute Utility Theory (MAUT). The integrated state, cost and benefit estimates of probable maintenance actions are utilized alongside expert opinion to develop transition probability and reward matrices for each probable maintenance action for a particular candidate infrastructure. These matrices are then used as an input to the Markov Decision Process (MDP) for the finite-stage dynamic programming model to perform project (candidate)-level analysis to determine optimized maintenance strategies based on reward maximization. The outcomes of project (candidate)-level analysis are then utilized to perform network-level analysis taking the portfolio management approach to determine a suitable portfolio under budgetary constraints. The major decision support outcomes of the prototype framework include performance trend curves, decision logic maps, and a network-level maintenance investment plan for the upcoming years. The framework has been implemented with a set of bridges considered as a network with the assistance of the Pima County DOT, AZ. It is expected that the concept of this prototype framework can help infrastructure management agencies better manage their available funds for maintenance.

The U.S. Air Force (USAF) estimates it has a $33 billion (about 10 percent is airfield pavements) deferred maintenance backlog within its $263 billion infrastructure portfolio. Given the scope of this backlog and the importance of airfields, the USAF has a vested interest in finding strategies to help reverse this growing trend. Without an increase in funding, divestiture of excess infrastructure, or change in strategy, this backlog is estimated to climb to over $50 billion by 2030. Reversing the growing infrastructure backlog trend requires new methods and strategies to rethink how the USAF invests in its infrastructure. As such, the overall goal of this research is to develop a comprehensive and practical asset management approach to reduce the total cost of ownership of USAF airfield pavements. By reducing the cost of ownership, the goal is to reverse the growing maintenance backlog while maintaining a pavement portfolio capable of supporting USAF flying operations into the future. While this research is particularly relevant to the USAF, it seeks to fill research gaps within the current body of knowledge related to pavement management strategies for other agency types by presenting a practical, simulation-based methodology for work planning and budget allocation across a large pavement portfolio over a thirty-year period. The dissertation presents the development of the BEAST and RAMPSS algorithms. The BEAST algorithm is a simulation tool capable of modeling behaviors and decisions of 109 organizations managing a global network of airfield pavements over thirty years. Additionally, the BEAST is used to forecast outcomes of USAF investment decisions utilizing its current management strategies and historical behaviors. The RAMPSS is a simulation algorithm designed to select the most economical maintenance strategy for each pavement section in the USAF’s portfolio (i.e., individualized maintenance recommendation strategy for each pavement section). Analysis from the RAMPSS algorithm of the USAF’s pavement portfolio suggests that airfields are generally more cost-effective to maintain if kept in better conditions with strategies other than localized preventative maintenance. The USAF’s current maintenance strategy is unsustainable; however, switching to recommendations from RAMPSS (incorporated and modeled in the BEAST) provides a potentially significant course correction.

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.

This study set out to develop a framework within which the use of Geographical Information Technologies (GITs) can be enhanced in Road Infrastructure Maintenance (RIM) in Uganda. Specifically it was guided by 3 objectives; 1. To assess the gaps in the use of GITs for RIM in Uganda and the limitations to accessing these technologies, 2. To develop a methodological framework to enhance the use of GITs in RIM and 3. To develop a Geographical Information Systems for Transportation (GIS-T) data model based on the road maintenance data requirements. A participatory approach through a series of interviews, focus group discussions, workshop & conferences, document reviews, field observations & measurements and GIS analysis were employed. Based on the Spatial Data Infrastructure (SDI) concept and the principle of Causality, the gaps and limitations were established to mainly be concerned with data and organisational constraints as opposed to technical issues. They were classified to include; inadequate involvement of GITs in organisational activities, inappropriate institutional arrangements, absence of data sharing frameworks, budget constraints, insufficient geospatial capacity, digital divide in the perception, adoption & affordability of GITs among the stakeholders and the absence of a road maintenance Spatial Data Infrastructure (SDI). A methodological framework, comprising of 6 strategic components was developed to enhance the use of GITs in RIM. This included enactment of relevant policy components to guide GIT use, continuous capacity building, establishment of a road maintenance SDI, fostering collaboration and spatial data sharing frameworks, budgetary allocation based on defined activities inclusive of GIT initiatives, and adoption of a dynamic segmentation data model. Conceptual and logical data models were developed and proposed for the Sector. The conceptual model, presented using an entity relationship diagram, relates the road network to the point and line events occurring on it. The logical object relational model developed using the ESRI provided template represents the road and the point and line events in a total of 19 object classes. The Study concludes that in order to ground GIT benefits in the sector; technical, data and organisational concerns involved in GIT undertakings should be accorded equal emphasis. Institutionalisation and diffusion of GITs as aspects of the component strategies are regarded capacity building mechanisms earmarked to boost success in GIT initiatives. Further research on diffusion and funding models for GIT initiatives is recommended. It is suggested that aspects of the proposed model be considered when establishing GIT standards for the sector. The RIM sector is encouraged to embrace Science and Technology and to participate in Research and Development and particularly to adopt the culture of innovation considering the ready availability of off the shelf equipment, freeware and open source software that can foster informed decision making.

Student number: 0200613W Faculty of Engineering and the Built Environment Master of Property Development and Management. / This paper is a report on the research undertaken to evaluate the implementation of the Urban Renewal strategies that the City of Johannesburg adopted for the CBD renewal through the Blue IQ. The study was restricted to the views expressed by the general community, business community, Johannesburg Development Agency (JDA). Johannesburg Housing Company (JHC), Blue IQ, Gauteng Development Agency and Gauteng Economic Development Agency (GEDA), which are the main role players in the CBD renewal effort. There was administered questioner to the members of the Business and general communities that were randomly picked using the fish bowl method. Interviews were conducted with the senior executive staff of JDA. Blue IQ, JHC. Statistics derived from the above company s websites was used as well. The results of the study show that the renewal strategy has been largely successful in so far as a number of factors, which have contributed to the CBD decay such as poor infrastructure and slumps. There have also been considerable efforts to address acute shortage of parking space by private partners such as financial institutions. Although crime has decreased it is still posing a serious challenge as most people still consider the CBD high risk. However there is still a room for improvement, which includes: the enhancement of safety and security, the infrastructure maintenance policy and the enforcement of the municipality by laws and town planning scheme. Overall the strategies have also improved the economic performance of the city significantly though unemployment still remain high with the ever increasing number of people coming to seek opportunities.

La plupart des systèmes ferroviaires subissent une demande croissante de capacité. Pour y faire face, il faut construire de nouvelles infrastructures ou exploiter plus efficacement celles existantes, notamment en définissant des grilles horaires optimisées. Dans la littérature, la plupart des approches de construction des grilles sont basées sur des représentations macroscopiques de l'infrastructure, ce qui peut conduireà des solutions infaisables ou inefficaces. En revanche, les approches microscopiques reposent sur une modélisation réaliste du système ferroviaire, ce qui garantit la faisabilité et l'efficacité des résultats. Néanmoins, en raison de leur complexité, l'utilisation de ces approches est généralement limitée à une seule gare. Malgré l'optimisation de la grille horaire, les travaux de maintenance peuvent avoir un fort impact sur les circulations des trains. En présence de maintenances, il peut donc être nécessaire de redéfinir la grille horaire pour assurer une exploitation efficace de la capacité. Nous présentons deux contributions principales sous forme de deux approches microscopiques : une pour la conception de grilles horaires et l'autre pour leur redéfinition en cas de maintenance. La deuxième est la première approche microscopique qui apparaît dans la littérature pour aborder ce problème tout en considérant des aspects comme les limitations temporaires de vitesse. Nous démontrons la validité de nos approches et leur applicabilité dans des scénarios réels. De plus, nous montrons que les approches microscopiques peuvent être utilisées pour traiter des zones de l'infrastructure contenant plusieurs gares. / Most railway systems experience a growing demand of railway capacity. To face this demand, either new infrastructure must be built or a more efficient exploitation of the existing one must be attained. Timetables play a determinant role in the efficient capacity exploitation. Most timetabling approaches in the literature are based on macroscopic representations of the infrastructure. This may lead to inefficient and in some cases, impractical solutions. Instead, microscopic approaches are based on more realistic modelling of the elements of the railway system. This guarantees the feasibility of the timetables while promoting an efficient capacity exploitation. However, due to their complexity, the scope of microscopic approaches is typically restricted to main stations. Despite the optimization of timetables, the performance of infrastructure maintenance may severely impact the trains' circulations in the network. Therefore, the timetable may have to be rearranged to ensure an efficient capacity exploitation. We present two main contributions in this thesis: first, a microscopic approach for timetable design. Second, a microscopic approach for timetable rearrangement to cope with maintenance. This is the first microscopic approach in the literature to tackle this problem while also considering specific aspects as temporary speed limitations. After a thorough experimental analysis, we demonstrate the validity of our approaches and their practical applicability in real life scenarios. In particular, we show that microscopic approaches can be used to tackle large areas of the infrastructure, including several stations.