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Recently many studies have shown interest in understanding the relationship between different project objectives in the context of sustainable development in the architecture, engineering, and construction (AEC) industry. However, those studies were mainly focused on the relationship at the building level. The authors of this study argue that a better understanding of such a relationship at the material level is equally important, because many decisions related to design and construction methods are made based on the selection of materials or products. Therefore, the objective of this study is to determine data patterns of installation time, cost, and environmental impacts in terms of CO2 emissions at the material level. Six material categories, i.e., structural components, exterior claddings, insulation, roofing, concrete footings, and concrete slabs-on-grade, were analyzed to examine the relationship between material cost and CO2 emissions. Two scenarios were considered for each category to better understand the impact of material installation on the relationship and identify dominant materials within each category under a certain level of confidence. Results show that only under a very low level of confidence that a dominant material exists in all six categories. In addition, the relationships at the material level appear to be rather random. It is thus important to have a better tool for decision-making and for future studies to understand how the randomness at the material level affects the relationships at the building level. [ABSTRACT FROM AUTHOR]

Occupant behavior is a significant contributor to energy waste in buildings. This research introduces an advanced smartphone application, developed based on the theoretical underpinnings of situational awareness theory, to effectively implement multi-method and personalized intervention to encourage energy conservation behaviors of building occupants. The new smart application provides several innovative features, such as energy saving points, customized feedback, and visualized user interface, which are implemented in the application to support multi-method interventions. The application was created using the Java language for Android devices. With the use of the Android platform, the app takes advantage of hardware technology from the user’s mobile device. Measurement of occupancy behavior is accomplished by making use of the device’s positional sensors. Orientation and geomagnetic field sensors serve to provide an accurate location of an occupant inside the building. The application can determine energy waste in a zone by using occupancy behavior. Moreover, the application offers real-time and projected future energy consumption based on occupants’ behaviors. This novel feature can significantly improve communication that can lead to prompt action for building energy reduction. Results show how the app can compile raw data on energy behavior and make it easy to understand for the user through the use of visuals and statistical algorithms. [ABSTRACT FROM AUTHOR]

Sustainable construction is critical to the architecture-engineering-construction (AEC) industry. The consideration and selection of optimal building components is necessary in order to fulfill multiple objectives of sustainable construction, which are often conflicting. Today, information and communication technologies (ICT) such as building information modeling (BIM) are widely used in the construction industry for decision making during design and construction. However, due to the complexity of building and construction, supporting multiple-objective decision making is not trivial. Currently, BIM has limited capability to support such a process. This paper introduces Simulation of Environmental Impact of Construction (SimulEICon), which is an extension of BIM designed specifically to aid in the decision-making process during the design stage of a construction project. SimulEICon is an add-on to the Autodesk Revit Architecture software, and it uses Microsoft Access for database-related operations. In addition, the genetic algorithm (GA), NSGA-II, is used for optimization in order to find solutions that best conform to project objectives. Moreover, SimulEICon has the capability of finding optimal solutions for all components at a building level or only for specific components and visualizing the solutions in a three- or four-dimensional model to support the decision-making process. Furthermore, the results can be constrained to conform to certain limitations. A case study of a real building, the Future House USA, is used with SimulEICon to demonstrate its use and results. A demonstration of SimulEICon is shown by comparing a set of possible solutions obtained by SimulEICon to those implemented in the building; it is expected that some of the solutions from SimulEICon would match those in the building at a component level, or even at a building level. Moreover, additional solutions to those present in the building show that SimulEICon is capable of presenting a wider range of possible solutions from which the user might choose the most appropriate one for the project goals. [ABSTRACT FROM AUTHOR]