This research asks “How do human behaviors and choices affect the performance of engineered infrastructure systems?”
Research in the Sociotechnical Systems Analysis (STSA) Lab investigates sociotechnical systems, which are systems in which human decision-making affects resource availability and infrastructure performance, while feedback loops from resource and infrastructure systems affect human decisions. Civil engineering infrastructure systems are built to serve the public, and the performance of these systems depends on how communities use them. As a result, there are feedbacks among natural resources, infrastructure, and society. The STSA Lab has developed agent-based models of sociotechnical systems to simulate feedback mechanisms and adaptive behaviors among consumers, infrastructure, and environmental systems. This research also explores optimization models and algorithms to manage the sustainability, security, and resilience of complex infrastructure systems. Agent-based models, water system models, and optimization methods, including evolutionary algorithms, are coupled to identify infrastructure management strategies that adapt to sociotechnical dynamics. New modeling frameworks simulate the decisions of utility managers to optimize water supply decisions and respond to declining levels of water availability. The effects of these decisions on basin-level resources, local supply sustainability, and social welfare are assessed.
For example, research conducted in the STSA lab developed new methods for managing a water supply contamination event and has investigated in depth how a community may respond dynamically to an event and change water demands based on exposure and warning messages. Results demonstrated that water consumers who react to water quality problems in their tap water can change the water flows in a pipe network to the extent that flow directions are reversed in some pipes, compared to the normal operating conditions. These dynamics affect predictions about what segments of the population may be affected by the contaminant and what operations should be implemented to flush out a contaminant.
In related research, Dr. Berglund and her colleagues conducted a national survey to assess public perceptions about water reuse. This data was used to encode an agent-based model to simulate opinion dynamics within a community, based on communication among households. A modeling framework that coupled agent-based modeling with EPANET was used to predict “adoption” as the initiation of new water reuse accounts and to assess the efficiency of water reclamation infrastructure expansion plans and the performance of pipe networks. Related research is exploring policies that encourage the adoption of green infrastructure and simulating the effects of rainwater harvesting, permeable pavements, and rain gardens on watershed flows through the use of the EPA’s Stormwater Management Model (SWMM).
Emily Zechman Berglund, Ph.D.
Department of Civil, Construction, and Environmental Engineering
North Carolina State University
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