Bayesian framework optimizes SHM by integrating data to minimize life cycle risk and cost through posterior probabilities and utility models.
Detection theory outperforms competitors in defect identification, using probabilistic testing for reliable localizations with sparse ultrasound sensor arrays.
Sensor design reduces risk and cost, utilizing machine learning to find optimal configurations while accommodating sensor degradation for planning.
Educational innovation targets AI integration, with online programs and physical expansion at Indianapolis focusing on high-quality workforce training.
Research funding gains from partnerships through national platforms and interdisciplinary collaboration, maximizing competitiveness for large grants.
Ethical considerations in AI are prioritized, addressing risk perception and societal implications to ensure responsible education and engineering practice.
