A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Probabilistic Analysis of Strip Footings Resting on Spatially Varying Soils Using Importance Sampling and Kriging Metamodeling
This paper aims at computing the failure probability against soil punching of a strip footing resting on a spatially varying soil and subjected to a vertical loading. The probabilistic analysis of geotechnical structures presenting spatial variability in the soil properties is generally performed using Monte Carlo Simulation (MCS) methodology. This method is not suitable for the computation of the small failure probabilities generally encountered in practice. This is because it is very time-consuming in such cases due to the large number of simulations required to calculate the failure probability with a small value of the coefficient of variation on this failure probability. In order to overcome the shortcoming related to the excessive number of calls of the mechanical model, this paper proposes an active learning method combining kriging and importance sampling. The soil cohesion and angle of internal friction were considered as random fields. Some probabilistic results are presented and discussed.
Probabilistic Analysis of Strip Footings Resting on Spatially Varying Soils Using Importance Sampling and Kriging Metamodeling
This paper aims at computing the failure probability against soil punching of a strip footing resting on a spatially varying soil and subjected to a vertical loading. The probabilistic analysis of geotechnical structures presenting spatial variability in the soil properties is generally performed using Monte Carlo Simulation (MCS) methodology. This method is not suitable for the computation of the small failure probabilities generally encountered in practice. This is because it is very time-consuming in such cases due to the large number of simulations required to calculate the failure probability with a small value of the coefficient of variation on this failure probability. In order to overcome the shortcoming related to the excessive number of calls of the mechanical model, this paper proposes an active learning method combining kriging and importance sampling. The soil cohesion and angle of internal friction were considered as random fields. Some probabilistic results are presented and discussed.
Probabilistic Analysis of Strip Footings Resting on Spatially Varying Soils Using Importance Sampling and Kriging Metamodeling
Al-Bittar, Tamara (author) / Ahmed, Ashraf (author) / Soubra, Abdul-Hamid (author) / Thajeel, Jawad (author)
Geo-Risk 2017 ; 2017 ; Denver, Colorado
Geo-Risk 2017 ; 440-449
2017-06-01
Conference paper
Electronic Resource
English
Kriging-Based Reliability Analysis of Strip Footings Resting on Spatially Varying Soils
| British Library Online Contents | 2018
| British Library Online Contents | 2014