<p>  </p> <p>The inspection of highway bridge structures in the United States is a task critical to the national transportation system. Inspection images contain abundant visual information that can be exploited to streamline bridge assessment and management tasks. However, historical inspection images often go unused in subsequent assessments as they are disorganized and unlabeled. Further, due to the lack of GPS metadata and visual ambiguity, it is often difficult for other inspectors to identify the location on the bridge where past images were taken. While many approaches are being considered toward fully- or semi-automated methods for bridge inspection, there are research opportunities to develop practical tools for inspectors to make use of those images already in a database. In this study, a deep learning-based image similarity technique is combined with image geolocation data to localize and retrieve historical inspection images based on a current query image. A Siamese convolutional neural network (SCNN) is trained and validated on a gathered dataset of over 1,000 real world bridge deck images collected by the Indiana Department of Transportation. A composite similarity (CS) metric is created for effective image ranking and the overall method is validated on a subset of eight bridge’s images. The results show promise for implementation into existing databases and for other similar structural inspections, showing up to an 11-fold improvement in successful image retrieval when compared to random image selection.</p>

In power system asset management, component data is crucial for decision making. This thesis mainly focuses on two aspects of asset data: data quality and data availability. The quality level of data has a great impact on the optimality of asset management decisions. The goal is to quantify the impact of data errors from a maintenance optimization perspective using random population studies. In quantitative terms, the impact of data quality can be evaluated financially and technically. The financial impact is the total maintenance cost per year of a specific scenario in a population of components, whereas the technical impact is the loss of a component's useful technical lifetime due to sub-optimal replacement time. Using Monte-Carlo simulation techniques, those impacts are analyzed in a case study of a simplified random population of independent and non-repairable components. The results show that missing data has a larger impact on cost and replacement year estimation than that of under- or over-estimated data. Additionally, depending on problem parameters, after a certain threshold of missing data probability, the estimation of cost and replacement year becomes unreliable. Thus, effective decision making for a certain population of components requires ensuring a minimum level of data quality. Data availability is another challenge that faces power system asset managers. Data can be lacking due to several factors including censoring, restricted access, or absence of data acquisition. These factors are addressed in this thesis from a decision making point of view through case studies at the operation and maintenance levels. Data censoring is handled as a data quality problem using a Monte-Carlo simulation. While the problems of restricted access and absence of data acquisition are studied using event trees and multiphysics modelling.  While the quantitative data quality problem can be abstract, and thus applicable to different types of physical assets, the data availability problem requires a case-by-case analysis to reach an effective decision making strategy. / <p>QC 20210528</p> / CPC5

This thesis deals with asset management of structures through Building Information Modelling (BIM) and Digital Twins. Background: Current inspection and management processes for civil structures are time-consuming and can even be inaccurate. There is an increasingly high potential to improve these processes through recent advances in technology. Digital Twins offer a common platform to these technologies, so they can interact and be used to their optimal performance. Other industries have significantly advanced in the development of Digital Twins, however, in the construction industry there are still many gaps and room for improvement. Aim and objectives: The main aim of this project was to investigate the status of Digital Twins in the construction industry and propose a methodology for a Digital Twin for asset management of structures. The three immediate objectives sought are (i) Perform a literature review to establish the current practice with digital twins, in both construction and other industries, and what are the gaps for asset management of structures; (ii) Participate in a pilot experimental program that yields data to a potential digital twin prototype; and (iii) Define a methodology for a digital twin for asset management of structures which fills the identified gaps. Methods of investigation: A literature review was performed and served as basis for the development of a methodology for a digital twin. A pilot experimental program was defined and performed, and its results were used for BIM and Finite Element (FE) models. A webapp was also created using Autodesk Forge and Java programming language, andthe BIM model was uploaded into it. Results: The literature review provided insight into the maturity level of digital twins, as well as on bridge inspection, maintenance and monitoring, BIM, facility and asset management, and Bridge Management Systems (BMS). A methodology to achieve a digital twin for asset management was proposed, and the conducted experimental program yielded data results to be used in future research. Conclusion: There has been significant progress in technology to improve structural assessment and analysis, however, their full potential is still under-explored. A digital twin created in a common data environment can provide a platform for these technologies to improve efficiency of current practices. Nonetheless, the construction industry is still significantly behind other industries such as aerospace and automotive.

Infrastructure deterioration has been attributed to insufficient maintenance budgets, lacking restoration strategies, deficient deterioration prediction techniques, and changing climatic conditions. Considering that the latter adds more challenges to the former, there has been a growing demand to develop and implement climate-informed infrastructure asset management strategies. However, quantifying the impact of the spatiotemporally varying climate metrics on infrastructure systems poses a serious challenge due to the associated complexities and relevant modelling uncertainties. As such, in lieu of complex physics-based simulations, the current study proposes a glass box data-driven framework for predicting infrastructure climate induced deterioration rates. The framework harnesses evolutionary computing, and specifically multigene genetic programming, to develop closed-form expressions that link infrastructure characteristics to relevant spatiotemporal climate indices and predict infrastructure deterioration rates. The framework consists of four steps: 1) data collection and preparation; 2) input integration; 3) feature selection; and 4) model development and result interpretation. To numerically demonstrate its utility, the proposed framework was applied to develop deterioration rate expressions of two different classes of concrete and steel bridges in Ontario, Canada. The developed predictive models reproduced the observed deterioration rate of both bridge classes with coefficient of determination (R2) values of 0.912 and 0.924 for the training subsets and 0.817 and 0.909 for the testing subsets of the concrete and steel bridges, respectively. Attributed to its generic nature, the framework can be applied to other infrastructure systems, with available historical deterioration data, to devise relevant effective asset management strategies and infrastructure restoration standards under future climate scenarios. / Thesis / Master of Applied Science (MASc)

No description available.

No description available.

<p dir="ltr">Traffic Incident Management (TIM) is an important tool for agencies to reduce secondary crashes, improve travel reliability, and ensure safety of first responders. Having “eyes” on the scene from roadside traffic cameras can assist operators to dispatch appropriate personnel, provide situational awareness, and allow for quick response when incident conditions change. Many intelligent traffic systems (ITS) centers deploy pan-tilt-zoom (PTZ) cameras that provide broad coverage but require operators to position. When incidents occur or a public safety vehicle stops for roadside assistance, Traffic Management Center (TMC) operators need to reposition cameras to monitor the event. The camera positioning time depends on operator experience, accuracy of 911 call, location, public safety radio reports, and in some cases, GPS positions. This research outlines the methodology to use GPS data sources to automate camera position to a scene for event nature verification. In general, this GPS information can come from either connected vehicles or public safety vehicles, such as Indiana Department of Transportation (INDOT) Hoosier Helpers. Implementing this research into INDOT daily operations has increased the number of events that cameras verify, while decreasing the time from event occurrence to camera verification from a median of 5 minutes to a median of approximately 90 seconds. The time is driven by the accuracy and frequency of GPS data from devices. With increased telematics polling rates and availability of enhanced vehicle data such as door open/close and seatbelt latch events, this latency is expected to further decline. </p>

<p dir="ltr">The energy consumption in the building sector is responsible for over 36% of the total energy consumption across the globe. Of all the energy-consumer devices within a building, heating, ventilation, and air conditioning (HVAC) systems account for over 50% of the total energy consumed. This makes HVAC systems a source of preventable and unexplored energy waste that can be tackled by incorporating intelligent operations. Since its inception, model predictive control (MPC) has been one of the prospective solutions for HVAC management systems to reduce both costs and energy usage. Additionally, MPC is becoming increasingly practical as the processing capacity of building automation systems increases and a large quantity of monitored building data becomes available. MPC also provides the potential to improve the energy efficiency of HVAC systems via its capacity to consider limitations, to predict disruptions, and to factor in multiple competing goals such as interior thermal comfort and building energy consumption. In this regard, the opening chapter delves into the evolving landscape of the HVAC industry. It explores how rapid advancements in technology, growing concerns about climate change, and the ever-present need for energy efficiency are driving innovation. The chapter highlights the shift from static to dynamic HVAC systems, where buildings become sensor-rich networks enabling advanced control strategies like Model Predictive Control (MPC) and Fault Detection and Diagnosis (FDD). we first provide a comprehensive review of the literature concerning the application of MPC in HVAC systems. Detailed discussions of modeling approaches and optimization algorithms are included. Numerous design aspects such as prediction horizon, time step, and cost function, that impact MPC performance are discussed in detail. The technical characteristics, advantages, and disadvantages of various types of modeling software are discussed. Next, a thorough, real-world case study for the design and implementation of a generalized data-collection and control architecture for HVAC systems in an educational building is proposed. The proposed MPC method adds a supervisory control layer on top of the current BMS by delivering temperature setpoints to the legacy controller. This means that the technique may be used to a variety of current HVAC systems in different commercial buildings. In addition, the utilization of remote web services to host the cloud-based architecture significantly minimizes the amount of technical expertise generally necessary to create such systems. In addition, we provide significant lessons learned from the installation process and we list indicative prices, therefore minimizing uncertainty for other researchers and promoting the use of comparable solutions. Chapter two focuses on Fault Detection and Diagnosis (FDD), a critical component of maintaining optimal HVAC performance and minimizing energy waste. HVAC systems are susceptible to malfunctions over time, leading to increased energy consumption and higher maintenance costs. FDD techniques play a vital role in identifying and diagnosing these faults early on, allowing for timely repairs and preventing further deterioration. This chapter introduces a novel bi-level machine learning framework for diagnosing faults in air handling units. This framework addresses key challenges associated with FDD. A bi-level machine learning framework is developed for diagnosing faults in air handling units (AHUs) and rooftop units (RTUs) based on principal component analysis (PCA), time series anomaly detection, and random forest (RF). By proposing this framework, we address three persistent challenges in this field: (I) minimizing false positives; (II) accounting for data imbalance; and (III) normal condition monitoring of equipment. It is shown that PCA can reduce the dataset dimension with one principal component accounting for 95% of data variance. Also, the random forest could classify the faults with 89% precision for single-zone AHU, 85% precision for RTU, and 79% for multi-zone AHU. Chapter three tackles the practical implementation of Model Predictive Control (MPC) in a real-world commercial building setting. It details the development, implementation, and cost analysis of a universally applicable cloud-based MPC framework for HVAC control systems. This chapter offers valuable insights into the feasibility and effectiveness of MPC in achieving energy efficiency goals while maintaining occupant comfort. The chapter delves into the hardware and software components used for data acquisition and MPC implementation. It emphasizes the use of cloud-based microservices to ensure seamless integration with existing building management systems, promoting wider adoption of this advanced control strategy. Three innovative control strategies are presented and evaluated in this chapter. The chapter presents compelling evidence for the effectiveness of these strategies, showcasing significant energy savings of up to 19.21%. Chapter four focuses on Occupancy-based Demand Controlled Ventilation (DCV) as a means to optimize indoor air quality (IAQ) while minimizing energy consumption. This chapter highlights the growing importance of IAQ in the wake of the COVID-19 pandemic and its impact on occupant health and well-being. Current ventilation standards often rely on static occupancy assumptions, which can lead to over-ventilation during unoccupied pe riods and wasted energy. This chapter proposes a dynamic occupant behavior model using machine learning algorithms to predict CO2 concentrations within buildings. The chapter investigates the performance of various machine learning algorithms, ultimately identify ing a Multilayer Perceptron (MLP) as the most effective in predicting CO2 levels under dynamic occupancy conditions. This model allows for real-time modulation of ventilation rates, ensuring adequate IAQ while minimizing energy consumption. The concluding chapter presents experimental findings on the effectiveness of adaptive Variable Frequency Drive (VFD) control strategies in optimizing HVAC energy consump tion. Variable Frequency Drives allow for adjusting the speed of electric motors, including those powering HVAC fans. This chapter explores the potential of using real-time occu pancy predictions to optimize VFD operation. The proposed control strategy demonstrates impressive energy savings, achieving a 51.4% reduction in HVAC fan energy consumption while adhering to ASHRAE IAQ standards. This chapter paves the way for occupant-centric ventilation strategies that prioritize both human health and energy efficiency. These results underscore the potential of predictive control systems to transform building operations to ward greater sustainability and efficiency. The chapter acknowledges the need for further validation through extended monitoring and analysis. In summary, this thesis contributes significantly to the advancement of smart building technologies by proposing practical frameworks for implementing advanced control strategies in HVAC systems. The findings presented here offer valuable insights for building designers, engineers, facility managers, and policymakers interested in creating sustainable, energy efficient, and occupant-centric buildings. The developed frameworks have the potential to be applied across a wide range of building types and climatic conditions, promoting broader adoption of smart building technologies and contributing to a more sustainable built environment.</p>

Vi lever numera i en tid präglad av teknisk innovation och inte minst inom artificiell intelligens. Samtidigt har vi en ökande popularitet för sparande och investeringar i olika sparplattformar och depåer. Teknologins roll inom kapitalförvaltning har blivit alltmer avgörande och medan AI fortsätter att revolutionera branscher över hela världen har dess integration i finanssektorn väckt både entusiasm och skepsis, möjligheter och hinder.  Syftet med denna kandidatuppsats är att belysa sambandet mellan AI och kapitalförvaltning. Genom att granska det aktuella landskapet för AI-adoptering på finansmarknaderna och undersöka dess potentiella konsekvenser för framtidens kapitalförvaltning, lyfter vår studie både möjligheter och utmaningar som uppstår i denna teknologiska revolution. Genom en kvalitativ analys av respondenternas åsikter och erfarenheter har flera viktiga slutsatser dragits. Mänsklig expertis och närvaro förblir viktigt trots att AI kommer implementeras mer inom kapitalförvaltning. Att AI förväntas komplettera snarare än ersätta mänsklig arbetskraft, är konsensus bland respondenterna. Men det finns hinder för AI-utvecklingen, som kommer från människans behov av mänsklig vägledning, regulatoriska hinder och integritetsfrågor.  Sammanfattningsvis visar vår uppsats att genom att integrera AI-teknologi i samband med mänsklig expertis och ansvarstagande, kan vi skapa en mer effektiv och hållbar hantering av kapitalförvaltning som gynnar investerare och samhället i stort. / We live in an era characterized by technological innovation, particularly in artificial intelligence (AI). At the same time, there is a growing popularity for savings and investments across various savings platforms and depots. The role of technology in capital management has become increasingly crucial, and while AI continues to revolutionize industries worldwide, its integration into the financial sector has sparked both enthusiasm and skepticism, presenting opportunities as well as obstacles. The purpose of this bachelor's thesis is to illuminate the relationship between AI and capital management. By examining the current landscape of AI adoption in financial markets and exploring its potential implications for the future of capital management, our study highlights both opportunities and challenges arising in this technological revolution. Through a qualitative analysis of respondents' opinions and experiences, several important conclusions have been drawn. Human expertise and presence remain important despite the increasing implementation of AI in capital management. Consensus among respondents is that AI is expected to complement rather than replace human labor. However, there are obstacles to AI development, stemming from human needs for guidance, regulatory barriers, and privacy concerns. In conclusion, our thesis demonstrates that by integrating AI technology alongside human expertise and responsibility, we can create a more efficient and sustainable management of capital that benefits investors and society at large.

Thesis (PhD)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: Business-to-business services relating to physical asset management are playing an increasingly important role in industry. This is in the midst of the current pressures which asset owning organisations are experiencing in realising optimal value from their assets. The pursuit of understanding and complying with asset management standards such as ISO 55000 as well as the potential value to be gained from successful and sustainable business relationships contributes towards the importance of these services. The problem is that there is little or no evidence regarding the critical success factors for collaborating successfully in asset management services. The study identi es these critical success factors and demonstrates how the factors can di er between role players, industries, global regions and service types. A decision support model is developed providing the asset management community with access to the critical success factors for decision-making purposes. Based on the synthesis from internationally conducted Delphi- and survey studies it is found that the continued and sustained commitment from the asset owning organisation's senior management in support of the asset management service is the most critical factor for a successful asset management service partnership. Open and e ective communication is also highlighted as being critical, while it is important to have a process in place to improve the service continuously. Laboratory and eld testing con rm the validity of the decision support model for facilitating the decision-making process to improve asset management services, and in addition it also formalises the commercial and contracting processes relating to these services. / AFRIKAANSE OPSOMMING: Besigheid-tot-besigheidsdienste met betrekking tot siese batebestuur speel 'n toenemende belangrike rol in die industrie. Dit is te midde van die druk wat batebesittende organisasies tans ondervind om optimale waarde uit hul siese bates te verkry. Die strewe na beter begrip en om te voldoen aan batebestuurstandaarde soos ISO 55000, asook die potensiële waarde wat verkry kan word uit suksesvolle en volhoubare besigheidheidsvennootskappe, dra by tot die belangrikheid van hierdie dienste. Die probleem is daar bestaan min of geen bewyse rakende die kritiese suksesfaktore vir suksesvolle samewerking in batebestuurdienste. Die studie identi seer die kritiese suksesfaktore en toon aan hoe hierdie faktore kan verskil tussen rolspelers, industrieë, wêreldstreke en dienstipes. 'n Besluitnemingsmodel is ontwikkel wat die batebestuurgemeenskap toegang gee tot die kritiese suksesfaktore vir besluitnemingsdoeleindes. Gebaseeer op die sintese van internasionale Delphi- en opnamestudies is daar bevind dat die mees kritieke faktor vir 'n suksesvolle vennootskap in batebestuurdienste die voortgesette en volgehoue toewyding deur die bate-besittende organisasie se senior bestuur, ter ondersteuning van die batebestuurdiens, is. Doeltre ende en openhartige kommunikasie is ook uitgewys as krities, terwyl dit belangrik is om 'n proses te volg om die diens voortdurend te verbeter. Laboratorium- en praktyk toetsing het bevestig dat die besluitnemingsmodel geldig is vir die fasilitering van die besluitnemingsproses om batebestuursdienste te verbeter asook vir die formalisering van die kommersiële en kontraktuele prosesse wat verband hou met hierdie dienste.