The objective of this study was to create an integrated simulation framework to examine the dynamics that affect the long-term resilience of infrastructure systems. The proposed framework captures three phenomena that shape the dynamics of infrastructure systems: (1) deterioration of physical networks; (2) renewal and adaptation decision-making processes of agencies; and (3) chronic and acute stressors. Accordingly, a complex systems approach was adopted to examine the long-term resilience of infrastructure based on understanding of performance regimes as well as critical points at which shifts in the performance regime of infrastructure occur under chronic and acute stressors. The application of the proposed framework is demonstrated in a case study of a water distribution network where the system resilience was examined over a 100-year horizon under stressors such as aging infrastructure, population change, and funding constraints. The results identified the effects of internal dynamics and external stressors on the resilience landscape of infrastructure systems. The proposed framework enables building complex system-based theories for a more holistic understanding of infrastructure resilience.

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.

Occupant behaviors contribute significantly to energy consumption and waste in buildings. Effective implementation of intervention strategies is the key to minimize energy wastes derived by occupant behaviors. In particular, intervention strategies should be personalized, compatible with the attributes of occupants, and based on real-time and predictive patterns of occupants’ behaviors. This paper presents a smart system for integrated sensing, simulation, and feedback of occupant behaviors that enables implementation of personalized interventions. The proposed system includes a sensor network composed of non-invasive and inexpensive distance, light, temperature, and plug-load sensors to capture the presence of occupants and energy expenditures in different regions of a building. The data is stored for real-time evaluation of energy use and waste analysis. The data is also used for calibration and parameter estimation of an occupant energy simulation model used to predict occupants’ behaviors and energy use projections. Both real-time and predicted data are stored in a database of a Smart Android Application. The application utilizes both antecedent and consequence intervention strategies to promote energy saving behaviors. Using robust machine learning algorithms, the application enables implementation of personalized intervention strategies based on the attributes and behaviors of each occupant. For example, the application enables providing feedback regarding the current and projected energy consumption by each occupant, personalized information-graphs of energy consumption, rating of energy performance for each occupant compared to other occupants, and personalized energy saving tips. The implementation of the smart system developed in this research introduces great opportunities for moving beyond the conventional one-size-fits-all approaches toward a more personalized approach for effective implementation of intervention strategies to reduce energy waste in buildings.