Resilient Networks


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Resilient Urban Networks will will apply the knowledge and digital tools of network resilience to dense urban infrastructure, informing the design and fabrication of working physical models.


The complex connectedness of urban infrastructure, commodities, processes, and services gives rise to risk. The failure of any particular system can cause far reaching harm to propagate through network s of other systems. To safeguard our increasingly dense urban environments, we need to connect with our connectedness - to predict and respond to such failures, and design new infrastructure to minimize them. Resilient Urban Networks will apply the digital tools and concepts of network resilience to dense urban infrastructure. Through a series of short presentations by experts in the field and hands-on experimentation with a digital simulation engine, participants will examine an urban location of their choice and solve real problems with network design. The results from these explorations will inform a series of physical models that will test the digital model by pumping liquids through a sealed system capable of reacting to different disruptive inputs in real time. Work shop participants will develop an intuitive understanding of important concepts in resilient network s as they apply to the built environment.


alt

resilient_bristow

David Bristow is a Postdoctoral Fellow in the Centre for Resilience of Critical Infrastructure in the Faculty of Engineering at the University of Toronto where he leads the infrastructure resilience thrust. David holds a doctorate and a master’s degree in Civil Engineering from the University of Toronto. His research concerns the sustainability and resiliency of cities, campuses, infrastructure, enterprises and communities. David has developed ecological, network, control system and thermodynamic methodologies for analyzing topics in these areas. Previously, David designed and delivered an interactive workshop for municipal employees incorporating the use of decision support software he developed.

@bristowd


resilient_hambleton

Daniel Hambleton is a mathematician working in the AEC industry: a role he hopes will continue to push the boundaries of current design practice. In 2012, he founded MESH, a Toronto based consulting firm that specializes in implementing new research and technology in the digital design industry. Daniel has worked on projects both locally and abroad, collaborated with established and emerging architects, lectured at the University of Toronto, presented at international conferences, written technical papers, and generally been incorrigibly interested in the interaction between mathematics, engineering, and architecture

MESH

@HambletonDan


resilient_belanger

Zackery Belanger is a designer, researcher, and consultant working in architectural geometry and acoustics. He holds BS and MS degrees in Physics from Oakland University in Rochester, Michigan, and a MS degree in Architectural Sciences from Rensselaer. From 2002 to 2010 he led research and development for Kirkegaard Associates, including work on the Royal Festival Hall in London, UK and the new Experimental Media and Performing Arts Center (EMPAC) in Troy, New York. He was Cluster Champion for Smartgeometry 2012, conceptualizing and overseeing the development of the Manta Reactive Acoustic Surface. In 2013 he was the inaugural Researcher-in-Residence at EMPAC.

@zbelanger

ArchGeometer


resilient_aaron

Aaron Adcock is a PhD candidate in the Department of Electrical Engineering at Stanford University, is advised by Gunnar Carlsson and Michael Mahoney in the Department of Mathematics, but spends most of his time writing software. He is generally interested in any problem which involves the combination of engineering and mathematics. This has led to his research interest in applications of computational topology, the analysis of social and information networks, data mining, and machine learning. Some of his current projects include developing new techniques for applying computational topology to medical images, investigating the treelike structure of complex networks, and using these structures to develop meaningful insights into networks.


 

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