abstract: This study examines the applicability of high dynamic range (HDR) imagery as a diagnostic tool for studying lighting quality in interior environments. It originates from the limitations in lighting quality assessments, particularly from the problematic nature of measuring luminance contrast--a significant lighting quality definer. In this research, HDR imaging method is studied systematically and in detail via extensive camera calibration tests considering the effect of lens and light source geometry (i.e. vignetting, point spread and modulation transfer functions), in-camera variables (i.e. spectral response, sensor sensitivity, metering mode,), and environmental variables (i.e. ambient light level, surface color and reflectance, light source spectral power distribution) on the accuracy of HDR-image-derived luminance data. The calibration test findings are used to create camera setup and calibration guidelines for future research, especially to help minimize errors in image extracted lighting data. The findings are also utilized to demonstrate the viability of the tool in a real world setting--an office environment combining vertical and horizontal tasks. Via the quasi-experimental setup, the relationship between line of sight and perceived luminance contrast ratios are studied using HDR images. Future research can benefit from the calibration guidelines to minimize HDR-based luminance estimation errors. The proposed tool can be used and tested in different contexts and tasks with varying user groups for revising the former luminance-contrast guidelines as well as surface reflectance recommendations. / Dissertation/Thesis / Ph.D. Architecture 2011

Lately the construction industry has become more interested in designing and constructing environmentally friendly buildings (e.g. sustainable buildings) that can provide both high performance and monetary savings. Analyzing various parameters during sustainable design such as Life Cycle Assessment (LCA) and energy consumption, lighting simulation, green building rating system criteria and associated cost of building components at the conceptual design stage is very useful for designers needing to make decisions related to the selection of optimum design alternatives. Building Information Modeling (BIM) offers designers the ability to assess different design options and to select vital energy strategies and systems at the conceptual stage of proposed buildings. This thesis describes a methodology to implement sustainable design for proposed buildings at their conceptual stage. The proposed methodology is to be implemented through the design and development of a model that simplifies the process of designing sustainable buildings, evaluating their Environmental Impacts (EI), assessing their operational and embodied energy and listing their potential accumulated certification points in an integrated environment. Therefore, a Decision Support System (DSS) is developed by using Multiple Criteria Decision Making (MCDM) techniques to help design team decides and selects the best type of sustainable building components and design families for proposed projects based on three main criteria (i.e. Environmental, Economical factor «cost efficiency » and Social wellbeing) in an attempt to identify the influence of design variations on the sustainable performance of the whole building. The DSS outcomes are incorporated in an integrated model capable of guiding users when performing sustainable design for building projects. The proposed methodology contains five modules: 1) Database Management System (DBMS), 2) Energy and lighting analysis, 3) Life Cycle Assessment (LCA), 4) LEED and 5) Life Cycle Cost (LCC). To improve the workability of the proposed model, a use case of abovementioned modules are going to be created as plug-ins in BIM tool. The successful implementation of such a methodology represents a significant advancement in the ability to attain sustainable design of a building during the early stages, to evaluate its EI, and to list its potentially earned certification points and associated soft costs.