Machine Learning Aided Reliability Analysis for Spatially Varying Slopes in 2D and 3D

The Random Finite Element Method (RFEM) is a robust stochastic method for slope reliability analysis that incorporates the spatial variability of soil properties. However, the extensive computational time associated with the direct Monte Carlo simulation limits its practical application. To overcome this problem, this study investigates the use...

Estimating the effect of assumed initial damage to the hydraulic stability of pattern-placed revetments on dikes using finite element modeling

Initial damage, caused by previous wave loading or other events, might affect the hydraulic stability of pattern-placed revetments. Three common types of damage are considered in this study. The effect of this assumed initial damage on the hydraulic stability and failure probability of revetments is quantified using a FEM model. This model is...

Machine learning of evolving physics-based material models for multiscale solid mechanics

In this work we present a hybrid physics-based and data-driven learning approach to construct surrogate models for concurrent multiscale simulations of complex material behavior. We start from robust but inflexible physics-based constitutive models and increase their expressivity by allowing a subset of their material parameters to change in...

A comprehensive study of agent-based airport terminal operations using surrogate modeling and simulation

Airport terminals are complex sociotechnical systems, in which humans interact with diverse technical systems. A natural way to represent them is through agent-based modeling. However, this method has two drawbacks: it entails a heavy computational burden and the emergent properties are often difficult to analyze. The purpose of our research...

Surrogate models for the characterization of hydrodynamic loads on perforated monopiles

Perforated monopiles show promise in providing a better alternative to the commonly used jacket-like substructures used in intermediate water depths in the range of 30 to 120 m. By introducing perforations near the vicinity of the splash zone the wave loads on the monopile can be mitigated and the fatigue damage reduced, which is the main...

Contextual Hyperparameter Optimization for the Train Unit Shunting Problem

One of the planning problems encountered by the Dutch Railways (NS) at shunting yards is the train unit shunting problem (TUSP). This problem considers idle train units that have to be parked, be cleaned, undergo regular maintenance, and be reconfigured into the scheduled departing train compositions. A local search algorithm is being developed...

On-the-fly construction of surrogate constitutive models for concurrent multiscale mechanical analysis through probabilistic machine learning

Concurrent multiscale finite element analysis (FE²) is a powerful approach for high-fidelity modeling of materials for which a suitable macroscopic constitutive model is not available. However, the extreme computational effort associated with computing a nested micromodel at every macroscopic integration point makes FE²...

Bayesian model calibration with interpolating polynomials based on adaptively weighted leja nodes

An efficient algorithm is proposed for Bayesian model calibration, which is commonly used to estimate the model parameters of non-linear, computationally expensive models using measurement data. The approach is based on Bayesian statistics: using a prior distribution and a likelihood, the posterior distribution is obtained through application...

A surrogate-based multi-disciplinary design optimization framework modeling wing–propeller interaction

This paper presents a multi-disciplinary design optimization (MDO) framework for design of a general aviation aircraft wing considering the effects of tractor propellers on the wing aerodynamic characteristics. In pursuit of this objective, a wing–propeller full-interaction aerodynamic routine was developed and integrated with structural and...