Integrated Construction Project Delivery System in the U.S. Public Sector: An Information Modeling Framework

Integrated project delivery (IPD) method has recently emerged as an alternative to traditional delivery methods. It has the potential to overcome inefficiencies of traditional delivery methods by enhancing collaboration among project participants. Information and communication technology (ICT) facilitates IPD by effective management, processing and communication of information within and among organizations. While the benefits of IPD, and the role of ICT in realizing them, have been generally acknowledged, the US public construction sector is very slow in adopting IPD. The reasons are - lack of experience and inadequate understanding of IPD in public owner as confirmed by the results of the questionnaire survey conducted under this research study. The public construction sector should be aware of the value of IPD and should know the essentials for effective implementation of IPD principles - especially, they should be cognizant of the opportunities offered by advancements in ICT to realize this. In order to address the need an IPD Readiness Assessment Model (IPD-RAM) was developed in this research study. The model was designed with a goal to determine IPD readiness of a public owner organization considering selected IPD principles, and ICT levels, at which project functions were carried out. Subsequent analysis led to identification of possible improvements in ICTs that have the potential to increase IPD readiness scores. Termed as the gap identification, this process was used to formulate improvement strategies. The model had been applied to six Florida International University (FIU) construction projects (case studies). The results showed that the IPD readiness of the organization was considerably low and several project functions can be improved by using higher and/or advanced level ICT tools and methods. Feedbacks from a focus group comprised of FIU officials and an independent group of experts had been received at various stages of this research and had been utilized during development and implementation of the model. Focus group input was also helpful for validation of the model and its results. It was hoped that the model developed would be useful to construction owner organizations in order to assess their IPD readiness and to identify appropriate ICT improvement strategies.

New Developments in Tourism and Hotel Demand Modeling and Forecasting

Abstract Purpose The purpose of the study is to review recent studies published from 2007-2015 on tourism and hotel demand modeling and forecasting with a view to identifying the emerging topics and methods studied and to pointing future research directions in the field. Design/Methodology/approach Articles on tourism and hotel demand modeling and forecasting published in both science citation index (SCI) and social science citation index (SSCI) journals were identified and analyzed. Findings This review found that the studies focused on hotel demand are relatively less than those on tourism demand. It is also observed that more and more studies have moved away from the aggregate tourism demand analysis, while disaggregate markets and niche products have attracted increasing attention. Some studies have gone beyond neoclassical economic theory to seek additional explanations of the dynamics of tourism and hotel demand, such as environmental factors, tourist online behavior and consumer confidence indicators, among others. More sophisticated techniques such as nonlinear smooth transition regression, mixed-frequency modeling technique and nonparametric singular spectrum analysis have also been introduced to this research area. Research limitations/implications The main limitation of this review is that the articles included in this study only cover the English literature. Future review of this kind should also include articles published in other languages. The review provides a useful guide for researchers who are interested in future research on tourism and hotel demand modeling and forecasting. Practical implications This review provides important suggestions and recommendations for improving the efficiency of tourism and hospitality management practices. Originality/value The value of this review is that it identifies the current trends in tourism and hotel demand modeling and forecasting research and points out future research directions.

Mathematical Modeling to Assess the Drivers of the Recent Emergence of Typhoid Fever in Blantyre, Malawi

BACKGROUND: Multiyear epidemics of Salmonella enterica serovar Typhi have been reported from countries across eastern and southern Africa in recent years. In Blantyre, Malawi, a dramatic increase in typhoid fever cases has recently occurred, and may be linked to the emergence of the H58 haplotype. Strains belonging to the H58 haplotype often exhibit multidrug resistance and may have a fitness advantage relative to other Salmonella Typhi strains.

METHODS: To explore hypotheses for the increased number of typhoid fever cases in Blantyre, we fit a mathematical model to culture-confirmed cases of Salmonella enterica infections at Queen Elizabeth Central Hospital, Blantyre. We explored 4 hypotheses: (1) an increase in the basic reproductive number (R0) in response to increasing population density; (2) a decrease in the incidence of cross-immunizing infection with Salmonella Enteritidis; (3) an increase in the duration of infectiousness due to failure to respond to first-line antibiotics; and (4) an increase in the transmission rate following the emergence of the H58 haplotype.

RESULTS: Increasing population density or decreasing cross-immunity could not fully explain the observed pattern of typhoid emergence in Blantyre, whereas models allowing for an increase in the duration of infectiousness and/or the transmission rate of typhoid following the emergence of the H58 haplotype provided a good fit to the data.

CONCLUSIONS: Our results suggest that an increase in the transmissibility of typhoid due to the emergence of drug resistance associated with the H58 haplotype may help to explain recent outbreaks of typhoid in Malawi and similar settings in Africa.

Modeling of Turbulent Natural Gas and Biogas Flames of the Delft Jet-in-Hot-Coflow Burner: Effects of Coflow Temperature, Fuel Temperature and Fuel Composition on the Flame Lift-Off Height

The structure of a turbulent non-premixed flame of a biogas fuel in a hot and diluted coflow mimicking moderate and intense low dilution (MILD) combustion is studied numerically. Biogas fuel is obtained by dilution of Dutch natural gas (DNG) with CO2. The results of biogas combustion are compared with those of DNG combustion in the Delft Jet-in-Hot-Coflow (DJHC) burner. New experimental measurements of lift-off height and of velocity and temperature statistics have been made to provide a database for evaluating the capability of numerical methods in predicting the flame structure. Compared to the lift-off height of the DNG flame, addition of 30 % carbon dioxide to the fuel increases the lift-off height by less than 15 %. Numerical simulations are conducted by solving the RANS equations using Reynolds stress model (RSM) as turbulence model in combination with EDC (Eddy Dissipation Concept) and transported probability density function (PDF) as turbulence-chemistry interaction models. The DRM19 reduced mechanism is used as chemical kinetics with the EDC model. A tabulated chemistry model based on the Flamelet Generated Manifold (FGM) is adopted in the PDF method. The table describes a non-adiabatic three stream mixing problem between fuel, coflow and ambient air based on igniting counterflow diffusion flamelets. The results show that the EDC/DRM19 and PDF/FGM models predict the experimentally observed decreasing trend of lift-off height with increase of the coflow temperature. Although more detailed chemistry is used with EDC, the temperature fluctuations at the coflow inlet (approximately 100K) cannot be included resulting in a significant overprediction of the flame temperature. Only the PDF modeling results with temperature fluctuations predict the correct mean temperature profiles of the biogas case and compare well with the experimental temperature distributions.

Data-Adaptive Modeling using Convex Regression

Thesis (Ph.D.)--University of Washington, 2016-08

Recovering Natural History: Modeling Cardiovascular Biomarkers in the Presence of Endogenous Medication Use

Thesis (Ph.D.)--University of Washington, 2016-08

Risk Prediction In Cardiovascular Epidemiology: Considerations for Modeling Composite Endpoints

Thesis (Master's)--University of Washington, 2016-08

Flexible modeling and estimation for high-dimensional graphs

Thesis (Ph.D.)--University of Washington, 2016-08

Methods for Confounding Adjustment and High-Dimensional Environmental Exposures

Thesis (Ph.D.)--University of Washington, 2016-08

Quantifying and Modeling the Influence of Forest on the Magnitude and Duration of Mountain Snow Storage in the Pacific Northwest, USA

Thesis (Ph.D.)--University of Washington, 2016-08

Multivariate statistical modeling of an anode backing furnace - Modélisation statistique multivariée du four à cuisson des anodes utilisées dans la fabrication d'aluminium primaire

La stratégie actuelle de contrôle de la qualité de l’anode est inadéquate pour détecter les anodes défectueuses avant qu’elles ne soient installées dans les cuves d’électrolyse. Des travaux antérieurs ont porté sur la modélisation du procédé de fabrication des anodes afin de prédire leurs propriétés directement après la cuisson en utilisant des méthodes statistiques multivariées. La stratégie de carottage des anodes utilisée à l’usine partenaire fait en sorte que ce modèle ne peut être utilisé que pour prédire les propriétés des anodes cuites aux positions les plus chaudes et les plus froides du four à cuire. Le travail actuel propose une stratégie pour considérer l’histoire thermique des anodes cuites à n’importe quelle position et permettre de prédire leurs propriétés. Il est montré qu’en combinant des variables binaires pour définir l’alvéole et la position de cuisson avec les données routinières mesurées sur le four à cuire, les profils de température des anodes cuites à différentes positions peuvent être prédits. Également, ces données ont été incluses dans le modèle pour la prédiction des propriétés des anodes. Les résultats de prédiction ont été validés en effectuant du carottage supplémentaire et les performances du modèle sont concluantes pour la densité apparente et réelle, la force de compression, la réactivité à l’air et le Lc et ce peu importe la position de cuisson.

Exploring social representations of adapting to climate change using topic modeling and Bayesian networks

When something unfamiliar emerges or when something familiar does something unexpected people need to make sense of what is emerging or going on in order to act. Social representations theory suggests how individuals and society make sense of the unfamiliar and hence how the resultant social representations (SRs) cognitively, emotionally, and actively orient people and enable communication. SRs are social constructions that emerge through individual and collective engagement with media and with everyday conversations among people. Recent developments in text analysis techniques, and in particular topic modeling, provide a potentially powerful analytical method to examine the structure and content of SRs using large samples of narrative or text. In this paper I describe the methods and results of applying topic modeling to 660 micronarratives collected from Australian academics / researchers, government employees, and members of the public in 2010-2011. The narrative fragments focused on adaptation to climate change (CC) and hence provide an example of Australian society making sense of an emerging and conflict ridden phenomena. The results of the topic modeling reflect elements of SRs of adaptation to CC that are consistent with findings in the literature as well as being reasonably robust predictors of classes of action in response to CC. Bayesian Network (BN) modeling was used to identify relationships among the topics (SR elements) and in particular to identify relationships among topics, sentiment, and action. Finally the resulting model and topic modeling results are used to highlight differences in the salience of SR elements among social groups. The approach of linking topic modeling and BN modeling offers a new and encouraging approach to analysis for ongoing research on SRs.

Building Information Modeling (BIM)

Los procesos de diseño y construcción tradicionales de la industria de la construcción que se han conservado inalterados durante años, han impedido vislumbrar los impactos positivos que se pueden dar al trabajar en conjunto con las tecnologías de información -- El miedo al cambio hace que las empresas del sector denominen “normal” a la fragmentación del proceso constructivo, y presupuesten desde el inicio de la obra atrasos por incompatibilidades en los diseños causados principalmente por la falta de comunicación y coordinación entre los diferentes participantes del proyecto -- La implementación de tecnologías de información busca no sólo ayudar como una herramienta a los diseños del proyecto, sino que también ofrece elementos para integrar a los participantes y contratistas del proyecto en un equipo de trabajo donde exista mayor interoperabilidad entre las áreas implicadas en el planteamiento, diseño y ejecución del proyecto, y se pueda disminuir de sobremanera la fragmentación que vuelve tan poco competitiva esta industria a nivel mundial -- En el presente trabajo de grado se realizó un estado del arte de la tecnología de información BIM con el fin de sintetizar las enormes cantidades de información disponibles y de posibilitar una mejor comprensión de este -- Con la teoría y los manuales estudiados a profundidad de los software que componen esta tecnología, se procedió a desarrollar un ejercicio exploratorio confrontando los conocimientos adquiridos teóricos con la parte práctica, parte en donde se pudieron vislumbrar las ventajas y desventajas al implementar este software en la industria de la construcción de nuestro país -- Finalmente se exponen los requerimientos y limitaciones que trae consigo la aplicación de esta tecnología a los proyectos de construcción

User Behavioral Modeling of Web-based Systems for Continuous User Authentication

Authentication plays an important role in how we interact with computers, mobile devices, the web, etc. The idea of authentication is to uniquely identify a user before granting access to system privileges. For example, in recent years more corporate information and applications have been accessible via the Internet and Intranet. Many employees are working from remote locations and need access to secure corporate files. During this time, it is possible for malicious or unauthorized users to gain access to the system. For this reason, it is logical to have some mechanism in place to detect whether the logged-in user is the same user in control of the user's session. Therefore, highly secure authentication methods must be used. We posit that each of us is unique in our use of computer systems. It is this uniqueness that is leveraged to "continuously authenticate users" while they use web software. To monitor user behavior, n-gram models are used to capture user interactions with web-based software. This statistical language model essentially captures sequences and sub-sequences of user actions, their orderings, and temporal relationships that make them unique by providing a model of how each user typically behaves. Users are then continuously monitored during software operations. Large deviations from "normal behavior" can possibly indicate malicious or unintended behavior. This approach is implemented in a system called Intruder Detector (ID) that models user actions as embodied in web logs generated in response to a user's actions. User identification through web logs is cost-effective and non-intrusive. We perform experiments on a large fielded system with web logs of approximately 4000 users. For these experiments, we use two classification techniques; binary and multi-class classification. We evaluate model-specific differences of user behavior based on coarse-grain (i.e., role) and fine-grain (i.e., individual) analysis. A specific set of metrics are used to provide valuable insight into how each model performs. Intruder Detector achieves accurate results when identifying legitimate users and user types. This tool is also able to detect outliers in role-based user behavior with optimal performance. In addition to web applications, this continuous monitoring technique can be used with other user-based systems such as mobile devices and the analysis of network traffic.

Hybrid mathematical and informational modeling of beam-to-column connections

The analysis of steel and composite frames has traditionally been carried out by idealizing beam-to-column connections as either rigid or pinned. Although some advanced analysis methods have been proposed to account for semi-rigid connections, the performance of these methods strongly depends on the proper modeling of connection behavior. The primary challenge of modeling beam-to-column connections is their inelastic response and continuously varying stiffness, strength, and ductility. In this dissertation, two distinct approaches—mathematical models and informational models—are proposed to account for the complex hysteretic behavior of beam-to-column connections. The performance of the two approaches is examined and is then followed by a discussion of their merits and deficiencies. To capitalize on the merits of both mathematical and informational representations, a new approach, a hybrid modeling framework, is developed and demonstrated through modeling beam-to-column connections. Component-based modeling is a compromise spanning two extremes in the field of mathematical modeling: simplified global models and finite element models. In the component-based modeling of angle connections, the five critical components of excessive deformation are identified. Constitutive relationships of angles, column panel zones, and contact between angles and column flanges, are derived by using only material and geometric properties and theoretical mechanics considerations. Those of slip and bolt hole ovalization are simplified by empirically-suggested mathematical representation and expert opinions. A mathematical model is then assembled as a macro-element by combining rigid bars and springs that represent the constitutive relationship of components. Lastly, the moment-rotation curves of the mathematical models are compared with those of experimental tests. In the case of a top-and-seat angle connection with double web angles, a pinched hysteretic response is predicted quite well by complete mechanical models, which take advantage of only material and geometric properties. On the other hand, to exhibit the highly pinched behavior of a top-and-seat angle connection without web angles, a mathematical model requires components of slip and bolt hole ovalization, which are more amenable to informational modeling. An alternative method is informational modeling, which constitutes a fundamental shift from mathematical equations to data that contain the required information about underlying mechanics. The information is extracted from observed data and stored in neural networks. Two different training data sets, analytically-generated and experimental data, are tested to examine the performance of informational models. Both informational models show acceptable agreement with the moment-rotation curves of the experiments. Adding a degradation parameter improves the informational models when modeling highly pinched hysteretic behavior. However, informational models cannot represent the contribution of individual components and therefore do not provide an insight into the underlying mechanics of components. In this study, a new hybrid modeling framework is proposed. In the hybrid framework, a conventional mathematical model is complemented by the informational methods. The basic premise of the proposed hybrid methodology is that not all features of system response are amenable to mathematical modeling, hence considering informational alternatives. This may be because (i) the underlying theory is not available or not sufficiently developed, or (ii) the existing theory is too complex and therefore not suitable for modeling within building frame analysis. The role of informational methods is to model aspects that the mathematical model leaves out. Autoprogressive algorithm and self-learning simulation extract the missing aspects from a system response. In a hybrid framework, experimental data is an integral part of modeling, rather than being used strictly for validation processes. The potential of the hybrid methodology is illustrated through modeling complex hysteretic behavior of beam-to-column connections. Mechanics-based components of deformation such as angles, flange-plates, and column panel zone, are idealized to a mathematical model by using a complete mechanical approach. Although the mathematical model represents envelope curves in terms of initial stiffness and yielding strength, it is not capable of capturing the pinching effects. Pinching is caused mainly by separation between angles and column flanges as well as slip between angles/flange-plates and beam flanges. These components of deformation are suitable for informational modeling. Finally, the moment-rotation curves of the hybrid models are validated with those of the experimental tests. The comparison shows that the hybrid models are capable of representing the highly pinched hysteretic behavior of beam-to-column connections. In addition, the developed hybrid model is successfully used to predict the behavior of a newly-designed connection.