Prof. Spencer is a pioneer in smart structures technology, with foundational contributions in structural health monitoring, structural control, stochastic fatigue, stochastic computational mechanics and topology optimization, and computer vision and machine learning for infrastructure assessment. He has directed more than $70 million in sponsored research and authored over 750 technical papers and reports, including two books. He was the first to study and design magnetorheological fluid dampers for protecting structures against earthquakes and strong winds, overcoming key limitations of traditional passive systems and power-intensive active control approaches now widely used in practice. He led the NSF George E. Brown Network for Earthquake Engineering Simulation system integration project, the nation’s first major engineering cyberinfrastructure initiative, and directed the NEES MUST-SIM hybrid simulation facility at the University of Illinois. His research on smart wireless sensor networks integrates advanced computing with self-interrogating sensing systems for large-scale structural monitoring, including landmark deployments on the Jindo Bridge in South Korea and the Dubai Eye in the United Arab Emirates, the largest wireless smart sensor implementations for civil infrastructure to date. More recently, he has led advances in computer vision-based inspection and developed the first framework for topology optimization under stochastic dynamic loading, enabling optimal design of resilient infrastructure. Prof. Spencer is a Distinguished Member of ASCE and a Foreign Member of the Chinese Academy of Engineering, the Engineering Academy of Japan, and the Polish Academy of Sciences.

In the aftermath of an earthquake, rapid structural inspection and evaluation are critical to ensure restoration of the normal order of life, work, and production. Traditional manual visual assessments by certified inspectors are slow, risky, and subjective, with limited availability delaying inspections. This lecture presents two approaches for automated rapid post-earthquake safety assessment. The first uses sparse acceleration measurements to define damage-sensitive features, inferred through a convolutional neural network. Validated experimentally at E-Defense in Japan, it proves effective for high-rise buildings. The second employs commercial UAV-collected images and a graphics-based digital twin (GBDT), incorporating finite element (FE) and photo-realistic computer graphics (CG) models. This approach is illustrated for a 45-story building in Guangzhou, China. These strategies enable rapid evaluation and efficient decision-making post-earthquake.