Modeling only constitutes one aspect of decision making. The prevailing limitation of applying modeling to practice is the absence of explicit consideration of uncertainties. This review paper covers uncertainty quantification (soil properties, stratification, and model performance) and uncertainty calculation with a focus on how it enhances the role of modeling in decision making (reliability analysis, reliability-based design, and inverse analysis). The key output from a reliability analysis is the probability of failure, where “failure” is defined as any condition that does not meet a performance criterion or a set of criteria. In contrast to the global factor of safety, the probability of failure respects both mechanics and statistics, is sensitive to data (thus opening one potential pathway to digital transformation), and it is meaningful for both system and component failures. Resilience engineering requires system level analysis. As such, geotechnical software can provide better decision support by computing the probability of failure/reliability index as one basic output in addition to stresses, strains, forces, and displacements. It is further shown that more critical non-classical failure mechanisms can emerge from spatially variable soils that can escape notice if the engineer were to restrict analysis to conventional homogeneous or layered soil profiles.
- Burland triangle
- Decision making
- Numerical modeling
- Risk management
ASJC Scopus subject areas
- Civil and Structural Engineering
- Geotechnical Engineering and Engineering Geology