Building Information Modeling (BIM) has been in use in the Hong Kong Architecture, Engineering and Construction (AEC) industry as an Information and Communication Technology (ICT) tool for more than a decade. However, the increasing usage and rapid development in both the AEC industry and academia point to the potential multiple applications, impacts and much broader potential benefits that may be generated from BIM implementation. The current frequently used BIM applications have advanced the project performances levels and consequential benefits of relevant stakeholders in different dimensions. However, BIM development in the Hong Kong industry has not been smooth. Attention has been diverted from potential benefits to the barriers and constraints that retard BIM implementation. This has in turn limited the applications, hence not convincing industry participants of their potential benefits in quantitative terms. Furthermore, conflicts between BIM implementation and the existing project processes also retard the smooth development of BIM. A higher level of collaborative working is required for deriving more benefits from BIM. This research aims to develop possible feasible solutions to reduce the conflicts/barriers in BIM implementation and advance current BIM implementation towards more collaborative and integrated working arrangements (IWAs), with expected broader potential benefits in the context of the Hong Kong industry. IWAs in this research refer to: a) organization structures, b) information exchange mechanisms, and c) project processes. An inter-locking set of research methods were applied in this study to achieve the research goal. Literature reviews were conducted to extract and illustrate the basic concepts in BIM and lay the foundation for proposal development. Semi-structured interviews and a questionnaire survey were conducted to explore BIM implementation scenarios in the Hong Kong AEC industry. Two case studies helped to map out BIM implementation processes in real projects in Hong Kong. Finally, a focus group meeting was held to discuss, validate and improve the relevant research findings and improvement proposals. The main outcomes of this research are the proposed short term IWAs and long term IWAs for optimizing overall potential benefits of BIM implementation. The proposed short term IWAs consist of specific measures for participants to address the barriers and conflicts in BIM implementation within existing project processes. The proposed long term IWAs are based on a conceptual framework and processes to build an integrated working environment for BIM. The outcomes of this research can help industry practitioners to overcome current barriers and derive more benefits from BIM by developing specific measures targeting the current scenarios, as well as provide possible directions for moving further forward in the long term. The research outcomes also offer relevant contributions to knowledge by proposing fresh concepts and approaches to creating and developing collaborative working environment for BIM implementation based on relevant principles and guidelines that are in turn derived from Relational Contracting (RC) frameworks. / published_or_final_version / Civil Engineering / Master / Master of Philosophy

The three-level multivariate multilevel model (MVMM) is a multivariate extension of the conventional univariate two-level hierarchical linear model (HLM) and is used for estimating and testing the effects of explanatory variables on a set of correlated continuous outcome measures. Two simulation studies were conducted to investigate the sample size requirements for restricted maximum likelihood (REML) estimation of three-level MVMMs, the effects of sample sizes and other design characteristics on estimation, and the performance of the MVMMs compared to corresponding two-level HLMs. The model for the first study was a random-intercept MVMM, and the model for the second study was a fully-conditional MVMM. Study conditions included number of clusters, cluster size, intraclass correlation coefficient, number of outcomes, and correlations between pairs of outcomes. The accuracy and precision of estimates were assessed with parameter bias, relative parameter bias, relative standard error bias, and 95% confidence interval coverage. Empirical power and type I error rates were also calculated. Implications of the results for applied researchers and suggestions for future methodological studies are discussed. / text

Having a good understanding of the load requirements in the datacenter improves the capability to effectively provision the resources available to the meet the demands and objectives of application services. Especially in a large project like XIFI this aspect becomes even more critical because of the limited availability of the resources and the complexity of the various entities present.In this study we frame a structure that provides deep insights to comprehend XIFI infrastructure. Further, we model the user requests that approach the node for resource allocation to run their applications. We aim to provide an understanding on different aspects involved in modelling. The objective of this present study is to investigate the effect of load on the XIFI node. To achieve this objective, we model the XIFI node by examining the various entities involved in it. Furthermore, we provide an overview about what constitutes as load in the XIFI node.We conduct a detailed specifications study after which we identify the imperative entities required for the modelling of both the XIFI node and the requests. We examine the model by simulating it in CloudSim for two different scenarios varying the specifications.We simulated the designed structure for 30 iterations and analyzed 10,000 user requests for two cases where total RAM of the node is increased in the second case when compared to the first case. We analyze the CPU usage, RAM usage, Bandwidth usage and Storage usage in both the cases and examine the effects of the user requests on each one of them.The results provided evidence that the load indices on the host are dependent on each other. Also, it showed that the request modelling had an impact on the load of the host. It can also be concluded that the resource provisioning can be effective if the user behavior is known.

Groundwater models are often developed as tools for environmental decision-making. However, sparse data availability can limit a model's utility by confounding attempts to select a single structural representation of a system or to find a unique and optimal set of model parameters. As a result, estimates of prediction uncertainty and the value of further data collection may be important results of a modeling effort. The Discrimination/Inference to Reduce Expected Cost Technique (DIRECT) is a new method for developing an ensemble of models that collectively define prediction uncertainty in a manner that supports risk-based decision making and monitoring network design optimization. We apply aspects of DIRECT to a modeling investigation of an aquifer system in Central Utah where a major Coalbed Methane gas field is located and a new approach for stimulating gas production is being explored. In the first stage of this study we develop an ensemble of regional MODFLOW models and calculate their relative likelihood using a set of observation data. These regional results and likelihoods are then transferred to a regional MT3D residence time model and to a local advective transport model to provide further information for the well design. A cost function is applied to the transport results to assess the relative expected costs of several proposed well field designs. The set of hydrologic results and associated likelihoods from the ensemble are combined into cost curves that allow for the selection of designs that minimize expected costs. These curves were found to be a useful tool for visualizing the ways that design decisions and hydrologic results interact to generate costs. Furthermore, these curves reveal ways in which uncertainty can add to the cost of implementing a design. A final analysis explored the cost of having uncertain model results by applying and manipulating synthetic likelihood distributions to the transport results. These results suggest the value that may be added by reducing uncertainty through data collection. Overall, the application of DIRECT was found to provide a rich set of information that is not available when ensemble methods and cost consideration are omitted from a modeling study.

The current study examined the effectiveness of a video modeling procedure on a basicmath skill acquisition in students with Autism Spectrum Disorder (ASD) using a multipleprobe across students design. Participants were four kindergarten/first grade students in aself-contained classroom in an urban public school. All met the criteria for ASD prior tobeginning the study. The independent variable included a video model teaching a basicmath skill. The dependent variable was correct student response. Results indicated thatone of the four students responded to the intervention immediately and these skillsmaintained over time. However, the other three students did not respond to videomodeling instruction. Conclusions regarding the effectiveness of the video modelingprocedure for teaching math skills were inconclusive. Implications for further researchare addressed.

This research presents an approach to accurately simulate flow experiments through a fractured core using experimental, stochastic, and simulation techniques. Very often, a fracture is assumed as a set of smooth parallel plates separated by a constant width. However, the flow characteristics of an actual fracture surface are quite different, affected by tortuosity and the impact of surface roughness. Though several researchers have discussed the effect of friction on flow reduction, their efforts lack corroboration from experimental data and have not converged to form a unified methodology for studying flow on a rough fracture surface. In this study, an integrated methodology involving experimental, stochastic, and numerical simulations that incorporate the fracture roughness and the friction factor is shown to describe flow through single fractures more efficiently. Laboratory experiments were performed to support the study in quantifying the flow contributions from the matrix and the fracture. The results were used to modify the cubic law through reservoir simulations. Observations suggest that the fracture apertures need to be distributed to accurately model the experimental results. The methodology successfully modeled fractured core experiments, which were earlier not possible using the parallel plate approach. A gravity drainage experiment using an X-ray CT scan of a fractured core has also validated the methodology.

The goal of this thesis is to gain a deep understanding of inverse problems arising from 2D image and 3D surface reconstruction, and to design effective techniques for solving them. Both computational and theoretical issues are studied and efficient numerical algorithms are proposed. The first part of this thesis is concerned with the recovery of 2D images, e.g., de-noising and de-blurring. We first consider implicit methods that involve solving linear systems at each iteration. An adaptive Huber regularization functional is used to select the most reasonable model and a global convergence result for lagged diffusivity is proved. Two mechanisms---multilevel continuation and multigrid preconditioning---are proposed to improve efficiency for large-scale problems. Next, explicit methods involving the construction of an artificial time-dependent differential equation model followed by forward Euler discretization are analyzed. A rapid, adaptive scheme is then proposed, and additional hybrid algorithms are designed to improve the quality of such processes. We also devise methods for more challenging cases, such as recapturing texture from a noisy input and de-blurring an image in the presence of significant noise. It is well-known that extending image processing methods to 3D triangular surface meshes is far from trivial or automatic. In the second part of this thesis we discuss techniques for faithfully reconstructing such surface models with different features. Some models contain a lot of small yet visually meaningful details, and typically require very fine meshes to represent them well; others consist of large flat regions, long sharp edges (creases) and distinct corners, and the meshes required for their representation can often be much coarser. All of these models may be sampled very irregularly. For models of the first class, we methodically develop a fast multiscale anisotropic Laplacian (MSAL) smoothing algorithm. To reconstruct a piecewise smooth CAD-like model in the second class, we design an efficient hybrid algorithm based on specific vertex classification, which combines K-means clustering and geometric a priori information. Hence, we have a set of algorithms that efficiently handle smoothing and regularization of meshes large and small in a variety of situations.

Imitation is a core deficit often observed in children diagnosed with autism. Video modeling has been shown to be effective for teaching children with autism a variety of skills, but there is little research demonstrating the effectiveness of this technique with core skills such as imitation. The purpose of this study was to examine the effectiveness of a video modeling intervention to teach a preschool-age child with autism to imitate novel and acquired actions (with and without objects) in natural contexts (i.e., songs and toy play activities). A general case approach was used to examine the instructional universe of common preschool songs in order to select the exemplars that were most likely to facilitate generalization. In addition to video modeling, additive components that included highlighting the critical features of the video examples and prompting/fading were required to demonstrate a functional relationship. Experimental control was evident in a multiple baseline design across three imitation activities. The results are discussed with reference to previous research, future research directions, and implications for practice in educational settings.

Metabolic models were constructed from the metagenome of a methanogenic benzene-degrading community to understand the metabolite interactions among the key microbes in the culture. The metagenomic sequences were assembled, and it was found that assembling the short DNA fragments before they were combined with longer reads can contribute to the overall lengths of the resulting sequences. The metagenome was then taxonomically classified into the domain of archaea and bacteria, and domain-specific models were built. A mathematical framework to fill metabolic gaps at the community level was then developed and applied to the benzene-degrading community model to study how metabolic gaps can be filled by via interspecies metabolite transfer, and it suggested that among other metabolites, acetate, hydrogen, formate, coenzyme A and histidine produced by the bacteria population could potentially contribute to the growth of the methanogens. The computational framework demonstrated its ability to generate testable hypotheses about microbial interactions.

Thermoelectric modules are an important alternative to heat engines in the harvesting of waste heat. Electrical-thermal analogues are often employed when studying heat conduction and this analogue can be extended to develop an equivalent circuit for thermoelectric effects. For the primarily one-dimensional problem of thermoelectricity, the equations can be discretized to create a simple mathematical model. In this document, such a model is developed from first principles and show that the electro-thermal coupling is properly in- corporated. The results of simulations using the model are then presented and validated experimentally. Furthermore, in one possible application of thermoelectric modules, a self-contained cooling unit with an integrated thermoelectric generator is designed. By performing fluid dynamics simulations on a fan and heat sink model, the geometry and operating conditions can be optimized and the start-up and transient characteristics are studied.