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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.

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An important practical problem in the survival analysis is predicting the time to a future event such as the death or failure of a subject. It is of great importance for the medical decision making to investigate how the predictor variables including repeated measurements of the same subjects are affecting the future time-to-event. Such a prediction problem is particularly more challenging due to the fact that the future values of predictor variables are unknown, and they may vary dynamically over time. In this dissertation, we consider a predictive approach based on modeling the forward intensity function. To handle the practical difficulty due to missing data in longitudinal measurements, and to accommodate observations at irregularly spaced time points, we propose a smoothed composite likelihood approach for estimations. The forward intensity function approach intrinsically incorporates the future dynamics in the predictor variables that affect the stochastic occurrence of the future event. Thus the proposed framework is advantageous and parsimonious from requiring no separated modeling step for the stochastic mechanism of the predictor variables. Our theoretical analysis establishes the validity of the forward intensity modeling approach and the smoothed composite likelihood method. To model the parameters as continuous functions of time, we introduce the penalized B-spline method into the proposed approach. Extensive simulations and real-data analyses demonstrate the promising performance of the proposed predictive approach. / Statistics

There have been several infamous incidences where world-renowned corporations have been caught by surprise when a low-tier downstream supplier has been publicly found to be non-compliant with basic corporate social responsibilities (CSR) codes. In such instances, the company reputation, and consequently financial health, suffer greatly. Motivated by the advances in predictive modeling, we present a predictive analytics model for detecting possible supplier deviations before they become a corporate liability. The model will be built based on publicly available data such as news and online content. We apply text mining and machine learning tools to design a corporate social responsibility "early warning system" on the upstream side of the supply chain. In our literature review we found that there is a lack of studies that focus on the social aspect of sustainability. Our research will help fill this gap by providing performance measures that can be used to build prescriptive analytics models to help in the selection of suppliers. To this end, we use the output of the predictive model to create a supplier selection optimization model that takes into account CSR compliance in global supply chain context. We propose a heuristic to solve the problem and computationally study its effectiveness as well as the impact of introducing CSR on procurement costs as well as ordering and supplier selection patterns. Our models provide analytics tools to companies to detect supplier deviance behaviour and act upon it so as to contain its impact and possible disruptions that can shake the whole supply chain. / Thesis / Master of Science (MSc)

Today's news agencies are moving from traditional journalism, where publishing just a few news articles per day was sufficient, to modern content generation mechanisms, which create more than thousands of news pieces every day. With the growth of these modern news agencies comes the arduous task of properly handling this massive amount of data that is generated for each news article. Therefore, news agencies are constantly seeking solutions to facilitate and automate some of the tasks that have been previously done by humans. In this dissertation, we focus on some of these problems and provide solutions for two broad problems which help a news agency to not only have a wider view of the behaviour of readers around the article but also to provide an automated tools to ease the job of editors in summarizing news articles. These two disjoint problems are aiming at improving the users' reading experience by helping the content generator to monitor and focus on poorly performing content while allow them to promote the good-performing ones. We first focus on the task of popularity prediction of news articles via a combination of regression, classification, and clustering models. We next focus on the problem of generating automated text summaries for a long news article using deep learning models. The first problem aims at helping the content developer in understanding of how a news article is performing over the long run while the second problem provides automated tools for the content developers to generate summaries for each news article. / Doctor of Philosophy / Nowadays, each person is exposed to an immense amount of information from social media, blog posts, and online news portals. Among these sources, news agencies are one of the main content providers for each person around the world. Contemporary news agencies are moving from traditional journalism to modern techniques from different angles. This is achieved either by building smart tools to track the behaviour of readers’ reaction around a specific news article or providing automated tools to facilitate the editor’s job in providing higher quality content to readers. These systems should not only be able to scale well with the growth of readers but also they have to be able to process ad-hoc requests, precisely since most of the policies and decisions in these agencies are taken around the result of these analytical tools. As part of this new movement towards adapting new technologies for smart journalism, we have worked on various problems with The Washington Post news agency on building tools for predicting the popularity of a news article and automated text summarization model. We develop a model that monitors each news article after its publication and provide prediction over the number of views that this article will receive within the next 24 hours. This model will help the content creator to not only promote potential viral article in the main page of the web portal or social media, but also provide intuition for editors on potential poorly performing articles so that they can edit the content of those articles for better exposure. On the other hand, current news agencies are generating more than a thousands news articles per day and generating three to four summary sentences for each of these news pieces not only become infeasible in the near future but also very expensive and time-consuming. Therefore, we also develop a separate model for automated text summarization which generates summary sentences for a news article. Our model will generate summaries by selecting the most salient sentence in the news article and paraphrase them to shorter sentences that could represent as a summary sentence for the entire document.

<div>Accurate prediction of electricity demand is a critical step in balancing the grid. Many factors influence electricity demand. Among these factors, climate variability has been the most pressing one in recent times, challenging the resilient operation of the grid, especially during climatic extremes. In this dissertation, fundamental challenges related to accurate characterization of the climate-energy nexus are presented in Chapters 2--4, as described below. </div><div><br></div><div>Chapter 2 explores the cost of neglecting the role of humidity in predicting summer-time residential electricity consumption. Analysis of electricity demand in the CONUS region demonstrates that even though surface temperature---the most widely used metric for characterising heat stress---is an important factor, it is not sufficient for accurately characterizing cooling demand. The chapter proceeds to show significant underestimations of the climate sensitivity of demand, both in the observational space as well as under climate change. Specifically, the analysis reveals underestimations as high as 10-15% across CONUS, especially in high energy consuming states such as California and Texas. </div><div><br></div><div>Chapter 3 takes a critical look at one of the most widely used metrics, namely, the Cooling Degree Days (CDD), often calculated with an arbitrary set point temperature of 65F or 18.3C, ignoring possible variations due to different patterns of electricity consumption across different regions and climate zones. In this chapter, updated values are derived based on historical electricity consumption data across the country at the state level. Chapter 3 analysis demonstrates significant variation, as high as +-25%, between derived set point variables and the conventional value of 65F. Moreover, the CDD calculation is extended to account for the role of humidity, in the light of lessons learnt in the previous chapter. Our results reveal that under climate change scenarios, the air-temperature based CDD underestimates thermal comfort by as much as ~22%.</div><div><br></div><div>The predictive analytics conducted in Chapter 2 and Chapter 3 revealed a significant challenge in characterizing the climate-demand nexuses: the ability to capture the variability at the upper tails. Chapter 4 explores this specific challenge, with the specific goal of developing an algorithm to increase prediction accuracy at the higher quantiles of the demand distributions. Specifically, Chapter 4 presents a data-centric approach at the utility level (as opposed to the state-level analyses in the previous chapters), focusing on high-energy consuming states of California and Texas. The developed algorithm shows a general improvement of 7% in the mean prediction accuracy and an improvement of 15% for the 90th quantile predictions.</div>

Le travail réalisé dans ce mémoire s’inscrit dans le cadre très large des études en compatibilité électromagnétique (CEM) de l’association convertisseur statique actionneur électrique. Il aborde la problématique de la CEM des machines électriques au travers de la modélisation prédictive des impédances de mode commun des moteurs électriques triphasés sur une large bande de fréquence, ainsi que certains aspects liés à l’apparition de tensions de roulements. Ce travail débute avec une identification par la méthode des éléments finis (EF) des paramètres qui régissent les chemins de propagation des courants de mode commun dans les parties métalliques des machines électriques : les tôles, la culasse, les flasques et l’arbre. Par la suite, un modèle circuit est élaboré pour la prédiction et la quantification de l’impédance de ces chemins. Cette première analyse a mis en évidence la prédominance des couplages capacitifs entre les enroulements statoriques et le stator. À cet effet, on propose dans une seconde partie un programme Matlab basé sur des formulations analytiques de capacitances qui permet de calculer automatiquement les différents couplages capacitifs ainsi que l’énergie électrostatique présente dans les encoches des machines à faible et moyenne puissance. Les prédictions analytiques obtenues présentent une bonne concordance avec les modèles numériques obtenus par EF et la mesure. Les conséquences de l’existence des capacités parasites de la machine sont illustrées au travers d’une étude sur l’apparition de la tension de palier dans un moteur synchrone à rotor bobiné de véhicule électrique. Le modèle simplifié, déduit de l’analyse électrostatique, permet de quantifier de façon satisfaisante les tensions observées qui induisent notamment la dégradation des chemins de roulement des paliers à billes. Dans les derniers chapitres, nous proposons une méthodologie de modélisation prédictive de l’impédance de mode commun des enroulements statoriques en prenant en considération l’ensemble des phénomènes inductifs et capacitifs. Un modèle électrique à paramètres discrets RLC, implantable dans un logiciel « circuit » de type Spice est généré - les paramètres géométriques et les propriétés des matériaux sont renseignés dans le modèle. Il permet de prédéterminer sans a priori l’impédance de mode commun d’une section d’enroulement expérimentale placée au stator d’une machine asynchrone sur une bande de fréquence de [10kHz -100 MHz]. Les résultats obtenus sont en concordance satisfaisante avec les résultats de mesure - d’autre part la méthodologie de modélisation développée, associée à des routines d’optimisation, permet d’envisager de contrôler les impédances de mode commun d’une machine électrique dès la phase de conception. / The research presented in this report addresses the problem of electromagnetic compatibility (EMC) in AC motors, which are supplied by PWM inverters, through the predictive modeling of the common mode impedance as well as aspect related to bearing voltage. We begin with identification of the parameters that govern the propagation path of common mode currents in the metallic parts of induction machines using finite element (FE) method. From analysis of the results, a circuit model was proposed to predict these propagation paths. The next chapter deals with the relationship between the existing parasitic capacitances and the development of the bearing voltage, which is responsible for premature failure of bearings. The study is realized on a wound-rotor synchronous motor with of an electric car. These approaches bring out the predominance of the capacitive coupling between the winding and the stator. For this reason, in the third chapter we developed a program based on analytical formulations, to automatically calculate the capacitive coupling as well as electrostatic energy within the slots of low and middle power machines. We find that the obtained results are in good agreement with FE calculation and the measurement. Finally, we propose a methodology for predictive modeling of common mode impedance of the stator windings taking into consideration all the inductive and capacitive phenomena. An electric model with lumped parameters RLC is then generated, which is implantable in SPICE type software. The prediction results are in good agreement with the measurement results on frequency band [10kHz - 100 MHz].

As glaciações do Quaternário moldaram os padrões filogeográficos das espécies em geral. Em algumas regiões da América do Sul, (e.g. Cerrado e Caatinga) a mudança estrutural foi mais acentuada, havendo o predomino de savanas, ao passo que em outras regiões (e.g. Amazônica e Mata Atlântica) as mudanças foram menores, formando áreas de refúgios florestais. A Modelagem Preditiva de Distribuição de espécies usa associações entre variáveis ambientais e registros de ocorrência da espécie para estimar modelos que representam as condições ambientais favoráveis à espécie. Neste trabalho foram estudadas três espécies de figueiras Neotropicais com características ecológicas distintas, representando duas linhagens filogenéticas independentes, i.e., seções Americana (Ficus citrifolia) e Pharmacosycea (Ficus adhatodifolia e Ficus insipida). Foram gerados modelos para os cenários passados (Interglacial 140.000 e Glacial 21.000 anos atrás), presente e futuro (2050 e 2070, nos cenários otimistas e pessimistas) para as três espécies estudadas usando o programa Maxent 3.3.3k. Os resultados obtidos mostram que para F. adhatodifolia as variáveis mais importantes nos modelos foram temperatura mínima do mês mais frio e precipitação do mês mais seco. Para F. insipida as variáveis mais importantes nos modelos foram temperatura mínima do mês mais frio e precipitação anual. Para F. citrifolia as variáveis mais importantes nos modelos foram temperatura mínima do mês mais frio e precipitação do mês mais chuvoso. Os modelos projetados no cenário interglacial, para as três espécies estudadas, apresentaram áreas de adequabilidade ambiental próximas ao cenário atual. Durante o período glacial F. adhatodifolia mostrou uma mudança considerável em sua área de ocorrência, ocorrendo em regiões consideradas refúgios para algumas espécies. Ficus insipida apresentou uma retração na sua adequabilidade ambiental, porém mantendo-se na região amazônica, enquanto que F. citrifolia teve um aumento na sua área de adequabilidade. Nos cenários futuros (2050 e 2070) F.adhatodifolia apresentou uma diminuição em sua área de ocorrência em ambos os cenários otimista e pessimista, F. insipida apresentou um aumento em sua área de adequabilidade ambiental e F.citrifolia apresentou uma diminuição e fragmentação na região Amazônica nos cenários otimista e pessimista de 2050 e otimista de 2070. As exigências ambientais e os possíveis padrões filogeográficos das três espécies são discutidos no contexto dos modelos preditivos gerados. / The Quaternary glaciations shaped the phylogeographic patterns of species in general. In some regions of South America (e.g.Cerrado and Caatinga) structural change was more pronounced and savannas predominated, whereas in other regions (e.g. Amazon and Atlantic Forest) changes were minor, forming areas of forest refuges. Species distribution Predictive Modeling uses associations between environmental variables and species occurrence records to estimate models that represent the environmental conditions favorable to the species. In the present study we chose three species of Neotropical Ficus with different ecological characteristics, representing two independent phylogenetic lineages, i.e., sections Americana (Ficus citrifolia) and Pharmacosycea (F.adhatodifolia and F.insipida). We generated models for the past (interglacial 140,000 years ago and Glacial 21,000 years ago), present and future scenarios (2050 and 2070 in optimistic and pessimistic scenarios) for the three study species using Maxent 3.3.3k program. Our results showed thatfor F. adhatodifolia the most important variables in the models were minimum temperature in the coldest month and precipitation in the driest month. For F.insipida the most important variables in the models were minimum temperature in the coldest month and annual precipitation. For F. citrifolia the most important variables in the models were minimum temperature in the coldest month and precipitation in the wettest month. The models designed for the interglacial stage showed areas of environmental suitability similar to the current scenario of the three species. During the glacial period F. adhatodifolia showed a considerable change in its range, occurring in regions considered refuges for some species. Ficus insipida had its environmental suitability decreased, but remained in the Amazon region, while F. citrifolia increased its area of suitability. In the future models (2050 and 2070) F.adhatodifolia showed a decrease in its range on both optimistic and pessimistic scenarios, F.insipida showed an increase in its area of environmental suitability and F.citrifolia has been decreasing and fragmentation in the Amazon region in the optimistic and pessimistic scenarios 2050 and optimistic 2070. The environmental requirements and the potential phylogeographic patterns of the study species are discussed in the context of the generated predictive models.