About
How INSITES Was Born
The INfrastructure Sensing for Intelligent Transportation and Energy Systems (INSITES) Consortium concept was born from a vision and eagerness for changing the reality of aging infrastructure across the country with novel and advanced sensor technology and systems. In 2022, Dr. Paul Ohodnicki, at the time still an associate professor at the University of Pittsburgh, and colleagues from UPitt and National Energy Technology Laboratory (NETL) established the first University of Pittsburgh (Upitt) Infrastructure Sensing Collaboration (UPISC) Workshop, to bring together stakeholders spanning industry, government and academia around important topics of infrastructure sensing, artificial intelligence, digital twins, and physics-based modeling. Based upon the early discussions in that meeting, key action items emerged including the attendees’ desire to hold the workshop annually.
Since then, the meeting and associated interest has grown each year including the expansion into a National Academia of Engineering member led workshop in 2024, bringing to the discussion topics of policy related aspects at local, state, and federal levels. Continued success and engagement of UPISC fostered a discussion that highlighted importance and urgency of establishing INSITES, a formalized collaboration that establishes a research – education – innovation ecosystem to advance next generation workforce education and engagement of industry and government organizations to research and deploy novel technology enabling a new era of intelligent infrastructure.
Previous UPISC Workshops
UPISC
Organized by a collaboration of the University of Pittsburgh, the National Energy Technology Laboratory (NETL) and the National Academia of Engineering. The workshop has brought together throughout the years diverse groups of industry, academia and government peers with the common goal to discuss and advance topics related to intelligent infrastructure monitoring.
SHM for Nuclear Energy
Modernization and increased demand for clean energy sources have once again put in evidence the need for advancement in Nuclear Energy generation. Taking advantage of novel sensing technologies, members of INSITES have been focused on building lifelong Structural Health Monitoring systems specialized to the extreme and unique environments of Nuclear Reactor Plants, Transportation and Storage, to improve reliable and safe operation.
Distributed Chemical Sensing for Energy Infrastructure
Either by detecting leaks along extensive chemicals transportation infrastructure spread across the country, or determining environmental chemical composition and contamination over extensive areas such as deep oceanographic locus, groups on INSITES have been developing innovative fiber sensing capabilities for distributed chemical sensing applications
AI Integrated with Sensing
To breach the gap between complex and extensive data generation associated with advanced sensing technology, and human processing capacity; INSITES collaborators are dedicated to incorporating powerful and intensive processing techniques based on AI and Digital Twin methods and models to expand sensing systems capabilities, focused on high precision sensing, multiparameter processing and assisted interpretation.
Dark Fiber for Geological Monitoring
Taking advantage of the vast unused fiber networks spread across the country, groups associated with INSITES have been pushing through the possibilities for widespread use types of fibers to turn into distributed sensors, envisioning geological monitoring through seismic activity detection and even radiation monitoring during the darkening process of the fiber.
Electric Grid Sensing
The electric grid has become vital to daily life, but the intense environments of many of its components still poses a challenge to properly understand and monitor. Tackling this task, INSITES members continue to explore and develop more sensitive and precise multiparameter fiber sensors integrated with intelligent processing models to ensure stability and predictability and robustness of the grid.
