Show simple item record

dc.contributor.advisorFranz-Josef Ulm, Jeremy Gregory and Randolph Kirchain.en_US
dc.contributor.authorHester, Joshua C. (Joshua Colón)en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Civil and Environmental Engineering.en_US
dc.date.accessioned2018-11-28T15:43:24Z
dc.date.available2018-11-28T15:43:24Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/119328
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2018.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references.en_US
dc.description.abstractIn order to design buildings with reduced environmental impacts, it is important to analyze and compare a variety of design alternatives starting at early stages of the design process. This dissertation discusses the development of a probabilistic life cycle assessment (LCA) methodology for single-family residential buildings called the Building Attribute to Impact Algorithm (BAIA), which was created to reduce the amount of time and detail required to conduct LCAs, thus facilitating their use for early design exploration. Within BAIA, the building geometry, systems, occupant behavior, and materials are defined by flexible attributes, with options organized into hierarchies representing different levels of precision or under-specification. Parametric models based on these attributes provide estimates of the material quantities and use-phase energy consumption of the building, and Monte Carlo simulation is used to calculate the variability in predicted impacts and costs resulting from under-specified attributes. Two design guidance methods are explored: sequential specification - in which influential attributes are iteratively identified and specified - and genetic optimization. The latter is found to be more efficient because it identifies solutions with lower impacts and costs while maintaining a higher degree of flexibility in the probabilistic design, as measured by information entropy. In a genetically optimized design, quasi-optimum design solutions with 75% of the optimal reduction of costs and impacts are shown to provide a 40% increase in flexibility over the optimized design. These quasi-optimum solutions are analyzed to identify which attributes are flexible vs. critical (having quasi-optimum ranges that are greater than or less than half of their initial under-specified ranges, respectively). Twelve cases are studied representing different locations, analysis periods, uncertainty in energy-related impacts, and weightings of costs vs. impacts in the optimization objective. Of the geometrical attributes, the building aspect ratio and window-to-wall ratios are critical, while seven others (including orientation, number of stories, and window overhangs) are flexible in all cases. Most occupant-related attributes (including window shading and natural ventilation) are also flexible in all cases. Among the systems-related attributes, the mini-split heat pump efficiency, air leakage, and ratio of LED lighting fixtures are critical in most or all cases.en_US
dc.description.statementofresponsibilityby Joshua C. Hester.en_US
dc.format.extent149 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectCivil and Environmental Engineering.en_US
dc.titleFlexibility for improved design : probabilistic quasi-optimization of building life cycle impacts and costsen_US
dc.title.alternativeProbabilistic quasi-optimization of building life cycle impacts and costsen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Civil and Environmental Engineering
dc.identifier.oclc1065522454en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record