Machine Learning Surrogate Modeling for Reliability Analysis of Spatially Varying Slopes in 3D
Jort Vermeer (Student TU Delft)
Wei Huang (TU Delft - Civil Engineering & Geosciences)
Guillaume Rongier (TU Delft - Civil Engineering & Geosciences)
Guillaume A. Hicks (TU Delft - Civil Engineering & Geosciences)
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Abstract
This study investigates the use of machine learning (ML) models as ready-to-use surrogate models for the Random Finite Element Method (RFEM) in a 3D context. The training set comprises 4000 RFEM realizations, with random fields covering multiple spatial correlation lengths as input and their corresponding factors of safety (FoS) as the target. Our results show that a convolutional neural network performs best for predicting the FoS compared to two other ML models - support vector regression and random forest - combined with principal component analysis. The best-performing model in 3D can conduct a stochastic analysis of 4000 simulations within seconds, compared to 83 days for a standard RFEM reliability analysis, highlighting the efficiency of the approach.
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