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A platform for research: civil engineering, architecture and urbanism
A maximum entropy method using fractional moments and deep learning for geotechnical reliability analysis
https://orcid.org/http://orcid.org/0000-0002-9907-0193
The spatial variability of the properties of natural soils is one of the major sources of uncertainties that can influence the performance of geotechnical structures. The direct Monte-Carlo simulation (MCS) method, although robust and versatile, may incur prohibitively high computational burdens, especially for cases involving low failure probability levels. In this paper, a hybrid strategy that fuses convolutional neural networks (CNNs) and maximum entropy distribution with fractional moments (MaxEnt-FM) is proposed. MaxEnt-FM is a post-processing technique that fits a probability distribution to a set of sample data. The proposed hybrid strategy starts by training a CNN as the metamodel of the time-demanding random field finite element model. The trained CNN is then used to generate sample data, which is subsequently processed using the MaxEnt-FM technique to obtain failure probability. The use of a CNN allows MaxEnt-FM to be carried out without explicit calls to the finite element model. Therefore, the proposed hybrid strategy has the potential to provide a computationally efficient technique to calculate failure probability. For the illustrative slope stability example that has a failure probability of 3 × 10−4, the proposed hybrid strategy yields a less than 10% error in the predicted failure probability with only hundreds of finite element simulations.
A maximum entropy method using fractional moments and deep learning for geotechnical reliability analysis
https://orcid.org/http://orcid.org/0000-0002-9907-0193
The spatial variability of the properties of natural soils is one of the major sources of uncertainties that can influence the performance of geotechnical structures. The direct Monte-Carlo simulation (MCS) method, although robust and versatile, may incur prohibitively high computational burdens, especially for cases involving low failure probability levels. In this paper, a hybrid strategy that fuses convolutional neural networks (CNNs) and maximum entropy distribution with fractional moments (MaxEnt-FM) is proposed. MaxEnt-FM is a post-processing technique that fits a probability distribution to a set of sample data. The proposed hybrid strategy starts by training a CNN as the metamodel of the time-demanding random field finite element model. The trained CNN is then used to generate sample data, which is subsequently processed using the MaxEnt-FM technique to obtain failure probability. The use of a CNN allows MaxEnt-FM to be carried out without explicit calls to the finite element model. Therefore, the proposed hybrid strategy has the potential to provide a computationally efficient technique to calculate failure probability. For the illustrative slope stability example that has a failure probability of 3 × 10−4, the proposed hybrid strategy yields a less than 10% error in the predicted failure probability with only hundreds of finite element simulations.
A maximum entropy method using fractional moments and deep learning for geotechnical reliability analysis
https://orcid.org/http://orcid.org/0000-0002-9907-0193
The spatial variability of the properties of natural soils is one of the major sources of uncertainties that can influence the performance of geotechnical structures. The direct Monte-Carlo simulation (MCS) method, although robust and versatile, may incur prohibitively high computational burdens, especially for cases involving low failure probability levels. In this paper, a hybrid strategy that fuses convolutional neural networks (CNNs) and maximum entropy distribution with fractional moments (MaxEnt-FM) is proposed. MaxEnt-FM is a post-processing technique that fits a probability distribution to a set of sample data. The proposed hybrid strategy starts by training a CNN as the metamodel of the time-demanding random field finite element model. The trained CNN is then used to generate sample data, which is subsequently processed using the MaxEnt-FM technique to obtain failure probability. The use of a CNN allows MaxEnt-FM to be carried out without explicit calls to the finite element model. Therefore, the proposed hybrid strategy has the potential to provide a computationally efficient technique to calculate failure probability. For the illustrative slope stability example that has a failure probability of 3 × 10−4, the proposed hybrid strategy yields a less than 10% error in the predicted failure probability with only hundreds of finite element simulations.
A maximum entropy method using fractional moments and deep learning for geotechnical reliability analysis
Acta Geotech.
Wang, Ze Zhou (author) / Goh, Siang Huat (author)
Acta Geotechnica ; 17 ; 1147-1166
2022-04-01
20 pages
Article (Journal)
Electronic Resource
English
Convolutional neural networks , Fractional moments , Maximum entropy distribution , Metamodel , Small probability of failure , Spatial variability Engineering , Geoengineering, Foundations, Hydraulics , Solid Mechanics , Geotechnical Engineering & Applied Earth Sciences , Soil Science & Conservation , Soft and Granular Matter, Complex Fluids and Microfluidics
Estimation of the maximum entropy quantile function using fractional probability weighted moments
| British Library Online Contents | 2008
Estimation of the maximum entropy quantile function using fractional probability weighted moments
| Online Contents | 2008