Rethinking low-cost green building material selection process in the design of low-impact green housing developments
Creators: name: Ogunkah, Ibuchim
Publisher: name: University of Westminster
Year: 2015

description: Since 1950, the world population has increased by more than double. The sprawling demographic shift due to continuous migration from rural to urban areas in developing countries imposes socio-economic and environmental pressures to the urban areas. Apparently, the high demand for housing and the unsustainable construction practices underlying its production in recent times constitute issues that merit the attention of low-impact green housing developments. The feasibility of such developments also lies in the effective use of low-cost green building materials and components (LCGBMCs),primarily because of their potential to conserve energy use, reduce life-cycle cost, lessen ecological footprints, and revive lost cultural traditions. Until recently however, only very few of these products have been widely established in mainstream, on account that most designers are constrained by their vaguely informed knowledge as to their sustainability impacts during the early stages of the design decision-making process, when most of the important decisions relating to sustainability are made. With the scale of complexity on how to incorporate sustainability principles in the early stages of the material selection decision-making process, and quest to stimulate the motivation for their use in a wider industry context, a clear gap is identified. Drawing on the concept of sustainability, this research aims to narrow the underlying gap by exploring and evaluating the significance of an integrated modular-oriented mode of assessment that is able to assist designers in developing an improved capability to make early-informed choices, when formulating decisions to select LCGBMCs at the early conceptual stages of the design process. With results derived from the relevant literature, industrywide surveys, and through empirical evidence gathered from interviews with a cross-section of house build stakeholders in Nigeria, key sustainability principle indicators impacting the selection of building materials are identified, analysed, grouped and ranked according to the relative importance that each decision factor holds, using a suite of statistical analytical methods. The information gathered from the analysis with inputs elicited from experienced professionals are used to develop a Multi-Criteria Material Selection Decision Support System (MSDSS), and later refined with feedbacks obtained from selected builder and developer companies. The above integration is enhanced using Macro-in-Excel Database Management System (DBMS), while the Analytical Hierarchy Process (AHP) model is adopted as the ideal assessment methodology, given its ability to transform objective and subjective variables into weighted scores. Expert surveys are then used to demonstrate the usefulness of the suggested decision support system. The applicability and validity of this model are further illustrated using an ongoing housing project in Nigeria. By comparing the outputs from the model to monitored data from the case study, it would emerge that LCGBMCs, when properly assessed with consideration of the key sustainability principle indicators (influential factors) at the early stages of the design decision-making process, could reduce the potential life-cycle carbon embodied energy of a typical residential housing project by nearly 40% and yield energy savings of roughly 30-50% per year, when compared to their conventional carbon-embodied equivalents. This study concludes that by addressing integration of sustainability principles into the material selection decision making processes at the early stages of the design, better support will be provided to key decision makers with the expectation of improved understanding and better informed choices, hence stimulate the motivation for more use of LCGBMCs in a wider industry context. The limitations of the study are highlighted and future research directions to better exploit the model capabilities are proposed.