Collection
Special Issue on Probabilistic learning for optimal decision-making on civil insfrastructures under uncertain environments
This special issue aims at identifying the latest methodological advances and transformative application examples in the topic area of probabilistic learning for optimal decision-making on civil infrastructures under uncertain environments. This aim will be achieved by acquiring contributions from eminent researchers in this topic area from academia, laboratories, and industry. These contributions are expected to establish today’s technological benchmarks from which the next generation of probabilistic learning in civil engineering will evolve.
Editors
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Junho Song
Seoul National University, Korea
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Daniel Straub
Technische Universität München, Germany
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Matteo Pozzi
Carnegie Mellon University, USA
Articles (14 in this collection)
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Reliability Based Structural Topology Optimization Considering Non-stationary Stochastic Excitations
Authors (first, second and last of 4)
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A Resilience Model for Engineered Slopes Subject to Anchor Corrosion
Authors (first, second and last of 5)
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Investigation of the Probability of a Safe Evacuation to Succeed in Subway Fire Emergencies Based on Bayesian Theory
Authors (first, second and last of 6)
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An Improved Non-parametric Bayesian Independence Test for Probabilistic Learning of the Dependence Structure Among Continuous Random Variables
Authors (first, second and last of 4)
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Deep BBN Learning for Health Assessment toward Decision-Making on Structures under Uncertainties
Authors (first, second and last of 4)
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Developing Short Term Drought Severity-Duration-Frequency Curves for Kerala Meteorological Subdivision, India Using Bivariate Copulas
Authors (first, second and last of 6)
