Effect of Learning Function on Reliability Analysis of Geotechnical Engineering Systems Using Adaptive Bayesian Compressive Sensing and Monte Carlo Simulation
Publication: Geo-Risk 2023
ABSTRACT
This study explored the effect of learning function on reliability analysis of geotechnical engineering system using adaptive Bayesian compressive sensing (ABCS) and Monte Carlo simulation (MCS) (ABCS-MCS). The ABCS-MCS method can provide both response prediction at an unsampled point and quantify explicitly the associated prediction uncertainty. It relies on a learning function to adaptively determine the minimum number of sampling points and corresponding sampling locations for achieving a target accuracy of reliability analysis. Therefore, learning function plays an important role in ABCS-MCS, and its learning criteria and stopping condition directly affect the accuracy and efficiency of reliability analysis. Four different learning functions are investigated together with ABCS-MCS. A comparative study using these four learning functions in ABCS-MCS is illustrated using a two-layered cohesive slope reliability analysis problem. Results show that ABCS-MCS combined with U-learning function has the highest accuracy, efficiency, and robustness for reliability analysis.
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Information & Authors
Information
Published In
Geo-Risk 2023
Pages: 340 - 350
History
Published online: Jul 20, 2023
ASCE Technical Topics:
- Adaptive systems
- Analysis (by type)
- Bayesian analysis
- Compression
- Continuum mechanics
- Dynamics (solid mechanics)
- Engineering fundamentals
- Engineering mechanics
- Geotechnical engineering
- Geotechnical investigation
- Measurement (by type)
- Sensors and sensing
- Solid mechanics
- Statistical analysis (by type)
- Structural dynamics
- System reliability
- Systems engineering
- Systems management
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