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X-WR-CALDESC:Events for C2SMART Home
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DTSTART;TZID=America/New_York:20251120T140000
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UID:89969-1763647200-1763650800@c2smart.engineering.nyu.edu
SUMMARY:Seminar: AI-empowered Digital Twin for Traffic Safety Analysis
DESCRIPTION:Abstract: Traffic safety research faces the paradox of rarity—the most critical crashes occur so infrequently that passive methods relying on historical records cannot accurately estimate traffic risks. This talk first reviews current methodologies and highlights their limitations in handling rare\, high-risk events. Building on these insights\, we chart a new path that fuses active safety analysis of near-miss events and generative AI. As a preliminary foundation for the generative AI–enabled safety analysis\, our recent work provides digital-twin–enabled active safety analysis through three pivotal components: (1) a physics-grounded active safety module that identifies near-miss events across diverse traffic contexts; (2) group-wise interaction modeling that captures multi-agent behaviors in traffic; and (3) a high-fidelity digital twin integrating detailed vehicle dynamics\, tire–road interaction\, and the above active-safety and interaction models. Together\, these elements enable proactive risk prevention\, expanding safety analysis beyond observed outcomes to the full spectrum of what could happen\, and lay the foundation for Vision Zero.\n\nBio: Dr. Yang Zhou is an assistant professor in the Zachry Department of Civil and Environmental Engineering at Texas A&M University\, and career initiation fellow of Texas A&M Institute of data science. He received his Ph.D. degree and Master degree from the University of Wisconsin-Madison and the University of Illinois at Urbana and Champaign\, respectively. Before joining Texas A&M\, Yang worked as a postdoctoral research associate supported by the Department of Civil and Environmental Engineering\, University of Wisconsin-Madison.  Yang has over ten years of experience in connected automated vehicle control and analysis\, traffic flow analysis\, AI applications on transportation\, and high-fidelity simulation. Yang has PIed multiple federal and local grants such as FHWA-EAR and SS4A. Yang has published more than 70 top-tier transportation journals\, including Transportation Research Part B\, Transportation Research Part C\, and IEEE Transactions on Intelligent Transportation Systems.
URL:https://c2smart.engineering.nyu.edu/event/seminar-ai-empowered-digital-twin-for-traffic-safety-analysis/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Seminars,Student Events
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DTSTART;TZID=America/New_York:20251202T123000
DTEND;TZID=America/New_York:20251202T143000
DTSTAMP:20260611T182457
CREATED:20251107T191349Z
LAST-MODIFIED:20251107T191420Z
UID:90272-1764678600-1764685800@c2smart.engineering.nyu.edu
SUMMARY:AECOM Fireside Chat & Panel
DESCRIPTION:
URL:https://c2smart.engineering.nyu.edu/event/cmaa/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Seminars,Student Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260109T140000
DTEND;TZID=America/New_York:20260109T150000
DTSTAMP:20260611T182457
CREATED:20251201T170925Z
LAST-MODIFIED:20251201T170925Z
UID:90461-1767967200-1767970800@c2smart.engineering.nyu.edu
SUMMARY:Seminar: A Large-scale Simulation Platform and AI-driven Operational Strategies for On-demand Ride Services
DESCRIPTION:Abstract: In this work\, we develop a novel multi-functional and open-sourced simulation platform for on-demand ride service operations\, which can simulate the behaviors and movements of various agents (including drivers and passengers) on a real transportation network. It provides a few accessible portals for users to train and test various optimization algorithms\, especially reinforcement learning algorithms\, for a variety of tasks\, including on-demand matching\, idle vehicle repositioning\, and dynamic pricing. Evaluated by experiments based on real-world datasets\, the simulator is demonstrated to be an efficient and effective test bed for various tasks related to on-demand ride service operations. \nBio: Dr. Jintao Ke is an Assistant Professor in the Department of Civil Engineering at the University of Hong Kong (HKU). Dr. Ke received his B.S. degree (2016) in Civil Engineering from Zhejiang University\, and his PhD degree (2020) in Civil and Environment Engineering from Hong Kong University of Science and Technology. His research interests include on demand mobility services\, transportation big data analytics\, multimodal transportation system optimization\, transportation pricing\, spatiotemporal traffic prediction\, etc. He has published more than 50 SCI/SSCI indexed research papers in top-tier journals in the field of transportation research and data mining\, such as Transportation Research Part A-F\, IEEE Transactions on\nIntelligence Transportation System\, IEEE Transactions on Knowledge and Data Engineering\, IEEE Internet of Things\, Computer-Aided Civil and Infrastructure Engineering. He has been ranked as the World's Top 2% most-cited scientists by Stanford University since 2023. He is serving as an Editorial Board Member of Transportation Research Part C\, Transportation Research Part E\, and Travel Behavior and Society.
URL:https://c2smart.engineering.nyu.edu/event/seminar-a-large-scale-simulation-platform-and-ai-driven-operational-strategies-for-on-demand-ride-services/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Seminars,Student Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260408T173000
DTEND;TZID=America/New_York:20260408T190000
DTSTAMP:20260611T182457
CREATED:20260324T161026Z
LAST-MODIFIED:20260326T180459Z
UID:90686-1775669400-1775674800@c2smart.engineering.nyu.edu
SUMMARY:Women in Transportation 2026
DESCRIPTION:
URL:https://c2smart.engineering.nyu.edu/event/women-in-transportation-2026/
CATEGORIES:Seminars,Student Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260414T123000
DTEND;TZID=America/New_York:20260414T133000
DTSTAMP:20260611T182457
CREATED:20260410T130857Z
LAST-MODIFIED:20260410T130857Z
UID:90748-1776169800-1776173400@c2smart.engineering.nyu.edu
SUMMARY:Morphing Structures for Civil Engineering\, from Gridshells to Burrowing Robots
DESCRIPTION:Shape-morphing metamaterials\, mechanical systems and structures are designed to predictably achieve large shape changes when actuated. While pervasive in aerospace and mechanical engineering\, these systems are seldom considered in civil engineering applications. This talk will concentrate on the morphing mechanics of some of the structures designed in our lab\, highlighting some of their potential civil applications. \nFirst\, we will present a strategy to turn flat arrangements of structural elements into pop-up domes\, investigating their deployment mechanics\, load-bearing capacity and comparing them to existing structures such as gridshells. Then\, we will illustrate how morphing structures can serve as the backbone of burrowing robots for geotechnical engineering applications\, where they need to engage in complex interactions with surrounding soils.
URL:https://c2smart.engineering.nyu.edu/event/morphing-structures-for-civil-engineering-from-gridshells-to-burrowing-robots/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Seminars,Student Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260623T120000
DTEND;TZID=America/New_York:20260623T130000
DTSTAMP:20260611T182457
CREATED:20260526T184820Z
LAST-MODIFIED:20260526T185142Z
UID:90875-1782216000-1782219600@c2smart.engineering.nyu.edu
SUMMARY:Bridge Resource Program Rime Webinar: Fusion of SHM and WIM Data for Minimizing the Cracking Potential of Concrete Bridge Decks
DESCRIPTION:This webinar focuses on the fusion of Structural Health Monitoring (SHM) data and Weigh-In-Motion (WIM) data to predict strain responses from live truck loads and support early-age crack mitigation strategies. A digital twin of a multi-span bridge was developed using a Finite Element Model (FEM) and calibrated based on the SHM data. The webinar will present how truck traffic and load data from WIM and structural responses from SHM can be used in the FEM under realistic loading scenarios during construction to support decision-making for the pouring sequence. The fusion of SHM\, WIM and FEM provides advanced predictions of structural behavior allowing for optimization of the concrete placement schedules by selecting a window of time to mitigate cracking while minimizing traffic restrictions. \nThis webinar will cover innovative methods for obtaining and combining analysis for\nNondestructive Material testing – Testing Concrete Samples and temperature over time for early age nondestructive strength evaluation of the structural element\nDeveloping a Truck live load profile – Onsite Weigh-In-Motion (WIM) System profiles truck traffic\nBridge superstructure behavior under known truck load – Bridge Instrumentation using strain gauges\, accelerometers\, and deflectometers\nLong term concrete shrinkage and thermal strain of bridge deck – Embedded sensor assessment of bridge deck & girder elements\nDigital twin – Finite Element Model for identifying strains that can produce cracking\nPost install evaluation – Crack Mapping and Analysis \nPresented by Rutgers’s Prof. Hani Nassif\, Dr. Chaekuk Na\, and Michael Ruszala
URL:https://c2smart.engineering.nyu.edu/event/bridge-resource-program-rime-webinar-fusion-of-shm-and-wim-data-for-minimizing-the-cracking-potential-of-concrete-bridge-decks/
CATEGORIES:Seminars,Student Events,Virtual Events,Webinars
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