چكيده به لاتين
Given the urgent environmental, societal, and economic challenges we currently confront, it has become increasingly evident that green buildings play a crucial role in addressing these concerns. Green buildings accomplish this by substantially decreasing energy and water usage, employing recyclable materials, lowering carbon dioxide emissions, and improving the comfort and overall health of the people who inhabit them. However, it is important to realize that implementing green buildings is not without challenges. There are various factors that play an important role in influencing their successful realization and classification as truly green structures. To address these complexities, international standards and specifications have been developed to evaluate green buildings comprehensively. Notable examples include the American LEED, British BREEAM, Australian GREEN STAR, and others, each of which rigorously checks key indicators that influence a building's green status. These indicators are classified into quantitative standards subject to mathematical equations and calculations and qualitative standards based on expert evaluations. Furthermore, the endeavor to achieve excellence in green building has spawned numerous methodologies and technologies aimed at enhancing data exchange. Among these tools, Building Information Modeling (BIM) emerges as a crucial technology that streamlines the process of sharing data efficiently. Accordingly, our research focused on quantitative indicators to calculate the green building index within the design stage and clarify the difference between traditional and green buildings in terms of energy consumption and energy consumption prices for traditional and green buildings. In the initial phase of our research, we began by gathering various indicators and categorizing them into three distinct groups: quantitative environmental indicators, quantitative economic indicators, and quantitative social indicators. To establish their relative significance concerning the ultimate objective of achieving a green building, we designed a questionnaire. This questionnaire involved assessing the importance of each indicator in relation to others. We utilized the Analytical Hierarchy Process (AHP) methodology to define our objective, and the survey instrument was structured into three matrices. Subsequently, we employed the SUPER DECISION program to assign weights to the aforementioned indicators. Following this, we utilized BIM and REVIT applications to create a 3D architectural design for a five-story shopping complex. To facilitate the extraction of essential data for mathematical calculations aimed at determining the green building index, we exported the architectural model to the BIMvision software in the IFC file format. We have chosen five specific quantitative environmental indicators for inclusion in the calculation process for the Green Building Index. These particular indicators were selected because our research primarily concentrated on the design phase. These five indicators accounted for 40% of the overall evaluation criteria, translating to 15.1% of the total evaluation points when considering the seventeen quantitative indicators, which make up 38% of the assessment. The comparison between regular and green buildings revealed a 14% reduction in energy consumption for green buildings. In terms of energy prices, green buildings exhibited a 24% reduction in comparison to local Iranian prices. When compared to international prices, green buildings showed a substantial 37% reduction in energy prices compared to regular buildings. In relation to future research, we underline the need for comprehensive investigation and analysis of additional quantitative indicators in order to establish a green building index for the various phases of construction. Furthermore, it is crucial to integrate these quantitative indicators with qualitative indicators to get the highest possible evaluation scores for green buildings.
