Efficient reliability analysis of slopes in spatially variable soils with active learning-assisted bootstrap polynomial chaos expansion

Evaluating the reliability of slopes with spatial variability is a challenging issue, especially when the failure probably of the target event is at a low level, because of unaffordable computational cost required in such cases. In this context, an adaptive surrogate model-based approach, namely active learning-assisted bootstrap polynomial chaos expansion, is proposed to alleviate the above computational burden. The proposed approach extends the traditional polynomial chaos expansion by introducing the bootstrap resampling method so that it can deal with reliability issues smoothly and provide a feasible configuration environment to support the active learning algorithm. The computational efficiency can thus be greatly improved by adaptively searching for the most informative samples to train the surrogate model through iterative program. Two spatially varying soil slopes are studied to illustrate the validity of the active learning-assisted bootstrap polynomial chaos expansion. The results show that the proposed approach has superior advantages in terms of efficiency and accuracy, and it is also suitable for handling problems with complex parameter configurations, including high dimensionality and cross-correlation. Besides, the proposed approach has potential in addressing geotechnical engineering problems with low probability levels.