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Dr. Khalid Mosalam

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Dr. Khalid Mosalam

Taisei Professor of Civil Engineering and the Director of the Pacific Earthquake Engineering Research (PEER) Center at UC Berkeley, USA

BIOGRAPHY

Prof. Khalid M. Mosalam received his B.S. and M.S. degrees in Structural Engineering from Cairo University and his Ph.D. from Cornell University. In 1997, he joined the Department of Civil and Environmental Engineering at the University of California, Berkeley, where he is the Taisei Professor of Civil Engineering and Director of the Pacific Earthquake Engineering Research (PEER) Center. His research focuses on the performance and health monitoring of structures, the assessment and rehabilitation of essential facilities, and building energy efficiency and sustainability. His work spans large-scale computational modeling and experimental testing, including hybrid simulation. Prof. Mosalam’s contributions have been recognized with numerous awards, including the ASCE Huber Civil Engineering Research Prize (2006), the UC Berkeley Chancellor’s Award for Public Service (2013), the EERI Outstanding Paper Award in Earthquake Spectra (2015), the ASCE Best Journal Paper in Materials and Structural Response (2020), and the Hojjat Adeli Award for Innovation in Computing (2021). He is a Corresponding Member of the Mexican Academy of Engineering in Civil Engineering and a Fellow of ASCE. He has held visiting professorships at Kyoto University (Japan), Middle East Technical University (Türkiye), and Nanyang Technological University (Singapore). Further information is available at: https://ce.berkeley.edu/people/faculty/mosalam.

ABSTRACT

From Modeling and Identification to Decision-Making: Data, Digital Twins, and a Rapid Bridge Monitoring System for California
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​While a wealth of monitoring data and advanced modeling techniques are now available, transforming raw data into a set of decision-ready metrics remains a well-recognized hurdle to proactive inspection and maintenance of bridge infrastructure. This keynote describes a framework for bridges and utilizing structural Digital Twins (DTs) that enables continuous health monitoring and rapid post-event assessment. Designed to bridge the gap between academic research and practical, on-site applications, this framework offers a unified methodology for integrating diverse modeling and prediction techniques. Moreover, it allows expansion to a wide range of infrastructure beyond bridges for assessment of community resilience following extreme events. This methodology is centered around five core abstractions: Assets, Events, Predictors, Evaluations, and Metrics. Assets are linked to their unique vulnerabilities and contextual metadata, informing prioritization for inspection following an event. Predictors include both conventional finite element analyses (including commonly used computational platforms in academia and practice) and advanced system identification. This allows for both rapid, data-driven “partial DTs” and comprehensive “full DTs” that integrate physics-based models for detailed analyses. Events, such as an earthquake, trigger real-time Evaluations of the bridge’s health, which are then distilled into actionable Metrics. Although this abstraction is currently applicable to bridges, it is expandable to other structural systems and networks. The Bridge Rapid Assessment Center for Extreme Events (BRACE2), a platform developed for the California Department of Transportation, demonstrates this approach. This dynamic web-based platform scales our methods to the statewide inventory of 2,449 bridges, and currently deploys few full and many partial DTs for the 80 instrumented bridges in California, to produce timely and relevant insights for decision-makers.

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