Infrastructure systems in coastal areas are exposed to episodic flooding exacerbated by sea-level rise stressors. To enable assessing the long-term resilience of infrastructure to such chronic impacts of sea-level rise, the present study created a novel complex system modeling framework that integrates: (i) stochastic simulation of sea-level rise stressors, based on the data obtained from downscaled climate studies pertaining to future projections of sea level and precipitation; (ii) dynamic modeling of infrastructure conditions by considering regular decay of infrastructure, as well as structural damages caused by flooding; and (iii) a decision-theoretic modeling of infrastructure management and adaptation processes based on bounded rationality and regret theories. Using the proposed framework and data collected from a road network in Miami, a multiagent computational simulation model was created to assess the long-term cost and performance of the road network under various sea-level rise scenarios, adaptation approaches, and network degradation effects. The results showed the capabilities of the proposed computational model for robust planning and scenario analysis to enhance the resilience of infrastructure systems to sea-level rise impacts.

Infrastructure systems are at the core of the sustainability challenge. Currently life cycle assessment (LCA) is widely used for assessing environmental sustainability of infrastructure systems. However, infrastructure systems have specific traits that are incompatible with the requirements of LCA. In particular, infrastructure systems do not have definite ‘life cycle’ as a basis of LCA. In addition, environmental performance of infrastructure systems depends on the dynamic changes in the level of service and performance of infrastructure normally not captured in existing LCA approaches. The objective of the research presented in this paper attempts to address the limitations of existing LCA approaches by creating a service and performance adjusted LCA (SPA-LCA) methodology, one which is specifically tailored for the requirements of environmental assessment of infrastructure systems. Among other improvements, the created methodology introduces a dynamic conception of life cycle inventory analysis and a service-based environmental accounting for the impact assessment phase of LCA. A simulation-based computational model is created to enable implementation of the SPA-LCA methodology. The SPA-LCA method and the created computational model are tested in a case study related to assessing the environmental impacts of a pavement network. Results include assessing impacts of different budget and demand scenarios on the environmental performance of the case study network. The results indicate capabilities of SPA-LCA methodology in addressing the limitations of existing LCA approaches for assessing environmental impacts of infrastructure systems.

The objective of this paper is to discover the emergent energy performance and determinants of energy waste in buildings. Electricity consumption in the U.S. attributes to 73% of energy waste in buildings and much of this waste is due to improper design, operation, and use of appliances. In particular, the operation or use phase of buildings and the way occupants behave significantly contribute to energy waste. Understanding the determinants of energy waste during the operation phase of buildings is a challenging task due to the complex interactions between the occupants, building units, and appliances. To decode these complex interactions and facilitate a better understanding of the determinants of energy waste, a simulation approach is used in this study. An agent-based simulation model was developed to capture the diverse attributes and dynamic behaviors of building occupants at the interface of human-building-appliance interactions. The application of the proposed model is demonstrated in a case study. Using simulation experiments, the interactions between occupant, building unit and appliance on energy consumption were investigated. The simulation model also was used for estimating determinants of energy waste. In addition, the simulation model includes a visualization interface that facilitates communication of strategies between the buildings users and facility managers. The results will highlight the significant attributes and effective strategies for energy waste reduction at the interface of human-building-appliance interactions. This information has potentially significant implications for building designers, facility managers, and users through a better understanding of emergent energy performance of buildings

Project progress and performance assessment is critically important to the successful delivery of capital facility projects. However, there is no standardized approach for the selection and use of project control metrics, making it difficult to analyze project progress and performance for transforming data into meaningful insights. This research identified core predictive and diagnostic metrics that may provide actionable insights into a project’s actual progress, performance, and forecast at completion. The methodology used for identifying these metrics included a literature review, surveys, expert evaluation utilizing the Delphi method, and statistical validation. The researchers analyzed 44 surveys and collected multiple rounds of responses from 16 subject matter experts to validate the findings. Results indicated there are 20 core metrics, seven validation metrics, seven innovative metrics, and 14 other significant metrics, which can be used for multiple project types, sizes, and contracting strategies. Statistical analyses of the survey data were used to further validate the core metrics and demonstrated that use of more core metrics corresponded with project cost performance and using more diagnostic metrics in projects led to better schedule performance.