The Center for Smart Infrastructure (CSI) recently hosted a visit by State Assembly Members Buffy Wicks, Pilar Schiavo, and Juan Alanis. During the visit, CSI provided an overview of its ongoing initiatives related to water infrastructure, natural disasters, and wildfire evacuation, and discussed the issues that the state is currently facing in these areas.
The highlight of the event was a large-scale 4-point bending testing of a 48-inch diameter ductile iron water pipeline manufactured by US Pipe – the largest test of its kind at CSI so far! The purpose of the test was to examine the performance of the pipeline’s joint under large external deformation that could be caused by landslide or seismic fault movements.
The visit by the state assembly members coincided with a CSI workshop to showcase the current research progress of the CSI project and engage relevant industry and state stakeholders. Over 60 people from water agencies, consultants, and academic researchers participated in the CSI workshop. During the workshop, members of the center demonstrated emerging technologies such as Distributed Fiber Optic Sensing (DFOS), intelligent systems and networks, remote sensing and monitoring, and data analytics for decision-making. The workshop included presentations on cutting-edge research in pipeline degradation data analytics by Dayu Apoji, simulation activities by Matt DeJong, smart-city technology adoption by Alison Post and Shih-Hung Chiu, as well as upcoming research projects.
CSI is a partnership between infrastructure owners, academia, industry, and regulators to address the most pressing and challenging issues facing water and wastewater utilities, especially within the domains of underground lifeline infrastructure, water infrastructure, energy systems, smart city technology, natural hazards, community resilience & equity, transportation infrastructure and sustainability. CSI brings together utilities with researchers and industry experts to test, develop, and deploy the latest technologies for laboratory testing, field sensing, robotics, numerical modeling and simulations, and data analytics/machine learning. This collaboration will advance the development of sustainable, cost-effective, equitable, and resilient systems and communities through applied research and validation in real-world environments.