No description available.

Thesis. 1975. M.Arch.A.S.--Massachusetts Institute of Technology. Dept. of Architecture. / Includes bibliographical references. / M.Arch.A.S.

No description available.

Thesis (M.S.)--University of Missouri-Columbia, 2004. / Typescript. Includes bibliographical references (leaves 59-64). Also available on the Internet.

Thesis (M.S.)--University of Missouri-Columbia, 2004. / Typescript. Includes bibliographical references (leaves 59-64). Also available on the Internet.

Thesis (Ph. D.)--Ohio State University, 2005. / Title from first page of PDF file. Document formatted into pages; contains xiv, 184 p.; also includes graphics. Includes bibliographical references (p. 153-184). Available online via OhioLINK's ETD Center

Call number: LD2668 .T4 1985 J635 / Master of Landscape Architecture

Retrofitting of existing buildings is essential to reach reduction targets in energy consumption and greenhouse gas emission. In the current practice of a retrofit decision process, professionals perform energy audits, and construct dynamic simulation models to benchmark the performance of existing buildings and predict the effect of retrofit interventions. In order to enhance the reliability of simulation models, they typically calibrate simulation models based on monitored energy use data. The calibration techniques used for this purpose are manual and expert-driven. The current practice has major drawbacks: (1) the modeling and calibration methods do not scale to large portfolio of buildings due to their high costs and heavy reliance on expertise, and (2) the resulting deterministic models do not provide insight into underperforming risks associated with each retrofit intervention. This thesis has developed a new retrofit analysis framework that is suitable for large-scale analysis and risk-conscious decision-making. The framework is based on the use of normative models and Bayesian calibration techniques. Normative models are light-weight quasi-steady state energy models that can scale up to large sets of buildings, i.e. to city and regional scale. In addition, they do not require modeling expertise since they follow a set of modeling rules that produce a standard measure for energy performance. The normative models are calibrated under a Bayesian approach such that the resulting calibrated models quantify uncertainties in the energy outcomes of a building. Bayesian calibration models can also incorporate additional uncertainties associated with retrofit interventions to generate probability distributions of retrofit performance. Probabilistic outputs can be straightforwardly translated into a measure that quantifies underperforming risks of retrofit interventions and thus enable decision making relative to the decision-makers' rational objectives and risk attitude. This thesis demonstrates the feasibility of the new framework on retrofit applications by verifying the following two hypotheses: (1) normative models supported by Bayesian calibration have sufficient model fidelity to adequately support retrofit decisions, and (2) they can support risk-conscious decision-making by explicitly quantifying risks associated with retrofit options. The first and second hypotheses are examined through case studies that compare outcomes from the calibrated normative model with those from a similarly calibrated transient simulation model and compare decisions derived by the proposed framework with those derived by standard practices respectively. The new framework will enable cost-effective retrofit analysis at urban scale with explicit management of uncertainties.

This project proposed methodology that enhances user preference communication, enabling the landscape architect to design spaces which better meet the needs and expectations of the user(s). Semantic and visual preferences were administered to a user core group to determine guidelines that led to a design study master plan that included the design of "outdoor rooms."The advantages and disadvantages of previous written and visual survey methods were reviewed. From this review the proposed methodology was developed and tested. The results showed the users' desires where not predictable more efficient designs were enabled. This method could be used in directing a diverse set of landscape design projects. / Department of Landscape Architecture

Mental models are an important theme within information systems. They show how people understand reality, make decisions and how the information flows in order for them to do so. It is particularly challenging to make such decisions when it comes to determining the health of someone, making it a particularly delicate matter. In that context, this thesis takes BetterDoc as object of study. It is an organization situated in Cologne, Germany, with an increasingly positive record of providing remote healthcare services and allowing patients to have the adequate treatment for their condition. The study was based on the theory of mental models to bring to light the implicit patterns present in making decision in that context. That was done by conducting qualitative interviews with the staff of the organization, across different teams, and synthetizing the findings in a common model that shows points of decision and the supporting information. Those findings are useful for identifying points that need to be structured to provide clarity and understanding, increasing the synergy and transparence of a socio-technical system that can influences the outcomes of healthcare for many people.