Search Results for author:
Found 9 papers, 2 papers with code
Quantifying the value of information transfer in population-based SHM
Population-based structural health monitoring (PBSHM), seeks to address some of the limitations associated with data scarcity that arise in traditional SHM.
A decision framework for selecting information-transfer strategies in population-based SHM
Decision-support for the operation and maintenance of structures provides significant motivation for the development and implementation of structural health monitoring (SHM) systems.
Towards risk-informed PBSHM: Populations as hierarchical systems
The current paper presents a formal representation of populations of structures, such that risk-based decision processes may be specified within them.
Mitigating sampling bias in risk-based active learning via an EM algorithm
For SHM applications, the value of information is evaluated with respect to a maintenance decision process, and the data-label querying corresponds to the inspection of a structure to determine its health state.
Improving decision-making via risk-based active learning: Probabilistic discriminative classifiers
For risk-based active learning in SHM, the value of information is evaluated with respect to a maintenance decision process, and the data-label querying corresponds to the inspection of a structure to determine its health state.
On statistic alignment for domain adaptation in structural health monitoring
The practical application of structural health monitoring (SHM) is often limited by the availability of labelled data.
On robust risk-based active-learning algorithms for enhanced decision support
In contrast, the discriminative classifiers are shown to have excellent robustness to the effects of sampling bias.
Probabilistic Inference for Structural Health Monitoring: New Modes of Learning from Data
In data-driven SHM, the signals recorded from systems in operation can be noisy and incomplete.
Essential guidelines for computational method benchmarking
In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses.
