Multi-parameter identification of concrete dam using polynomial chaos expansion and slime mould algorithm
This paper presents a novel methodology that combines polynomial chaos expansion and
slime mould algorithm for multi-parameter identification of concrete dams. This methodology …
slime mould algorithm for multi-parameter identification of concrete dams. This methodology …
A surrogate-assisted stochastic optimization inversion algorithm: Parameter identification of dams
Dynamic monitoring data plays an essential role in the structural health monitoring of dams.
This study presents a surrogate-assisted stochastic optimization inversion (SASOI) …
This study presents a surrogate-assisted stochastic optimization inversion (SASOI) …
Optimal sparse polynomial chaos expansion for arbitrary probability distribution and its application on global sensitivity analysis
Polynomial chaos expansion has received considerable attention in uncertainty
quantification since its great modeling capability for complex systems. However, considering …
quantification since its great modeling capability for complex systems. However, considering …
[HTML][HTML] Intelligent modelling of clay compressibility using hybrid meta-heuristic and machine learning algorithms
Compression index C c is an essential parameter in geotechnical design for which the
effectiveness of correlation is still a challenge. This paper suggests a novel modelling …
effectiveness of correlation is still a challenge. This paper suggests a novel modelling …
Polynomial chaos expansion for uncertainty quantification of dam engineering problems
MA Hariri-Ardebili, B Sudret - Engineering Structures, 2020 - Elsevier
Uncertainty quantification is an inseparable part of risk assessment in dam engineering.
Many probabilistic methods have been developed to deal with random nature of the input …
Many probabilistic methods have been developed to deal with random nature of the input …
Modelling the mechanical behaviour of soils using machine learning algorithms with explicit formulations
P Zhang, ZY Yin, YF Jin, XF Liu - Acta Geotechnica, 2021 - Springer
This study systematically presents the application of machine learning (ML) algorithms for
constructing a constitutive model for soils. A genetic algorithm is integrated with ML …
constructing a constitutive model for soils. A genetic algorithm is integrated with ML …
Machine Learning in the Stochastic Analysis of Slope Stability: A State-of-the-Art Review
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 …
“conservative” properties operate over the entire region of interest. This kind of deterministic …
Kriging based reliability and sensitivity analysis–Application to the stability of an earth dam
This article presents a Kriging-based probabilistic analysis of an earth dam. The dam failure
probability with respect to the sliding stability is investigated by considering the influence of …
probability with respect to the sliding stability is investigated by considering the influence of …
[HTML][HTML] Probabilistic analysis of tunnel face seismic stability in layered rock masses using polynomial Chaos Kriging metamodel
Face stability is an essential issue in tunnel design and construction. Layered rock masses
are typical and ubiquitous; uncertainties in rock properties always exist. In view of this, a …
are typical and ubiquitous; uncertainties in rock properties always exist. In view of this, a …
A sequential sparse polynomial chaos expansion using Bayesian regression for geotechnical reliability estimations
Polynomial chaos expansions (PCEs) have been widely employed to estimate failure
probabilities in geotechnical engineering. However, PCEs suffer from two deficiencies:(a) …
probabilities in geotechnical engineering. However, PCEs suffer from two deficiencies:(a) …