SMT 2.0: A Surrogate Modeling Toolbox with a focus on hierarchical and mixed variables Gaussian processes

P Saves, R Lafage, N Bartoli, Y Diouane… - … in Engineering Software, 2024 - Elsevier
Abstract The Surrogate Modeling Toolbox (SMT) is an open-source Python package that
offers a collection of surrogate modeling methods, sampling techniques, and a set of sample …

Recent trends in the modeling and quantification of non-probabilistic uncertainty

M Faes, D Moens - Archives of Computational Methods in Engineering, 2020 - Springer
This paper gives an overview of recent advances in the field of non-probabilistic uncertainty
quantification. Both techniques for the forward propagation and inverse quantification of …

UQpy: A general purpose Python package and development environment for uncertainty quantification

A Olivier, DG Giovanis, BS Aakash, M Chauhan… - Journal of …, 2020 - Elsevier
This paper presents the UQpy software toolbox, an open-source Python package for general
uncertainty quantification (UQ) in mathematical and physical systems. The software serves …

Random field modelling of spatial variability in concrete–a review

W Botte, E Vereecken, R Caspeele - Structure and Infrastructure …, 2023 - Taylor & Francis
Concrete is a heterogeneous material, consisting of different constituents such as
aggregates of different sizes and cement paste. Due to the heterogeneity of the concrete, its …

Probabilistic modelling of pitting corrosion and its impact on stress concentrations in steel structures in the offshore wind energy

S Shojai, P Schaumann, T Brömer - Marine Structures, 2022 - Elsevier
Supporting structures for offshore wind turbines and the appropriate transformer platforms
are highly susceptible to corrosion. Especially the phenomenon of pitting corrosion is very …

Rainfall-induced soil slope failure

L Zhang, J Li, X Li, J Zhang… - Florida: Taylor&Francis …, 2016 - api.taylorfrancis.com
Rainfall-induced landslides pose a significant threat to the public safety in many parts of the
world especially in tropical or subtropical mountainous areas. The research field of …

Machine Learning in the Stochastic Analysis of Slope Stability: A State-of-the-Art Review

H Xu, X He, F Shan, G Niu, D Sheng - Modelling, 2023 - mdpi.com
In traditional slope stability analysis, it is assumed that some “average” or appropriately
“conservative” properties operate over the entire region of interest. This kind of deterministic …

Slope stability machine learning predictions on spatially variable random fields with and without factor of safety calculations

M Aminpour, R Alaie, S Khosravi, N Kardani… - Computers and …, 2023 - Elsevier
Abstract Random field Monte Carlo (MC) reliability analysis is a robust stochastic method to
determine the probability of failure. This method, however, requires a large number of …

Time-dependent reliability assessment of aging structures considering stochastic resistance degradation process

Y Yang, J Peng, CS Cai, Y Zhou, L Wang… - Reliability Engineering & …, 2022 - Elsevier
Reasonable assessment of structural resistance degradation and reliability is the premise of
formulating targeted maintenance strategy of aging structures. In this paper, a Gamma …

Direct simulation of random field samples from sparsely measured geotechnical data with consideration of uncertainty in interpretation

Y Wang, T Zhao, KK Phoon - Canadian Geotechnical Journal, 2018 - cdnsciencepub.com
Random field theory has been increasingly used in probabilistic geotechnical analyses over
the past few decades, where a random field generator with random field parameters is …