Probabilistic assessment of pile group response considering superstructure stiffness and three-dimensional soil spatial variability

Leung, Yiu-Wing; Lo, M.K.

doi:10.1016/j.compgeo.2018.07.010

Cited by 24 publications

(4 citation statements)

References 31 publications

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“…The findings in this study enhance our understanding of the processes that lead to the natural variability of soil properties, which is an important factor in the occurrence of natural phenomena such as landslides and various applications of engineering geology. For example, Zhu et al (2018) showed that directions of spatial correlations can have significant impacts on the safety factors of slopes, while Leung and Lo (2018) also demonstrated the importance of three-dimensional spatial variability in evaluations of foundation settlements and potentials of tilting. This study elucidated the geological origins of spatial patterns observed in soils, which should be crucial considerations when analyzing geotechnical data and understanding its implications for engineering projects.…”

Section: Discussionmentioning

confidence: 99%

“…For example, the two-dimensional correlation model is often adopted in probabilistic analyses, considering horizontal and vertical correlations in soil properties (Li et al, 2015a(Li et al, , 2017Bong and Stuedlein, 2018). Recently, three-dimensional spatial variability of soils has been taken into account in site characterization and geotechnical reliability assessments (Li et al, 2015b;Xiao et al, 2016;Leung and Lo, 2018;Xiao et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Spatial variability of saprolitic soil properties and relationship with joint set orientation of parent rock: Insights from cases in Hong Kong

Liu

Leung

2018

Engineering Geology

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial variability of saprolitic soil properties and relationship with joint set orientation of parent rock: Insights from cases in Hong Kong

Liu

Leung

2018

Engineering Geology

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“…The influence of earthquakes, the nonlinear behavior of pile foundations [5,6], the pile-soil interaction of pile foundations under earthquake motions [7,8], and the liquefaction-induced settlement of buildings [9] have been studied. In the study of pile-soil interactions, many scholars have focused on the failure mode of pile foundations under extreme environments [10,11], the interaction between frozen soil and pile foundations [12][13][14], and the analysis of pile-soil interactions under normal environments [15][16][17][18][19]. In addition to the mechanical behavior of pile foundation structures under scouring and earthquakes, many scholars have conducted targeted research on the special stress mode of pile foundations.…”

Section: Introductionmentioning

confidence: 99%

Condition Diagnosis of Long-Span Bridge Pile Foundations Based on the Spatial Correlation of High-Density Strain Measurement Points

Liu

2021

Sustainability

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Pile foundations of long-span bridges are often deeply buried in soil, and their structural condition is difficult to accurately diagnose by conventional methods. To address this issue, a method for diagnosing the structural condition of bridge pile foundations based on the spatial correlation of high-density strain measurement points is proposed. The strain data of the high-density measurement points of a bridge pile foundation are obtained by using distributed optical fiber sensing technology based on Brillouin scattering, and then an algorithm for diagnosing the structural condition of the pile foundation based on geographically weighted regression analysis is presented. On this basis, aiming at the scour of the pile foundation of long-span bridges, an algorithm for estimating the scour depth of the pile foundation based on sliding plane clustering is proposed. Finally, the effectiveness of the proposed method is verified by numerical simulation and actual bridge data.

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“…For example, Blatman and Sudret employed the stepwise regression technique and the least angle regression technique to select important terms, discarding unimportant terms, for a sparse representation. The application of SPCEs to probabilistic analysis of geotechnical problems can be found in previous stuies …”

Section: Introductionmentioning

confidence: 99%

A sequential sparse polynomial chaos expansion using Bayesian regression for geotechnical reliability estimations

Pan

Liu

et al. 2020

Num Anal Meth Geomechanics

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Summary Polynomial chaos expansions (PCEs) have been widely employed to estimate failure probabilities in geotechnical engineering. However, PCEs suffer from two deficiencies: (a) PCE coefficients are solved by the least‐square minimization method which easily causes overfitting issues; (b) building a high order PCE is often computationally expensive. In order to overcome the aforementioned drawbacks, the Bayesian regression technique is employed to evaluate PCE coefficients, which not only provides a sparse solution but also avoids overfitting. With the aid of the predictive means and variances given by Bayesian analysis, a learning function is proposed to sequentially select the most informative samples that are critical to build a PCE. This sequential learning scheme can highly enhance the computational efficiency of PCEs. Besides, importance sampling (IS) is incorporated into the sequential learning (SL)‐PCEs to deal with geotechnical problems with small failure probabilities. The proposed method of SL‐PCE‐IS is applied to three illustrative examples, which shows that the improved PCE method is more effective and efficient than the common PCEs method, leading to accurate estimations of small failure probabilities using fewer training samples.

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Probabilistic assessment of pile group response considering superstructure stiffness and three-dimensional soil spatial variability

Cited by 24 publications

References 31 publications

Spatial variability of saprolitic soil properties and relationship with joint set orientation of parent rock: Insights from cases in Hong Kong

Spatial variability of saprolitic soil properties and relationship with joint set orientation of parent rock: Insights from cases in Hong Kong

Condition Diagnosis of Long-Span Bridge Pile Foundations Based on the Spatial Correlation of High-Density Strain Measurement Points

A sequential sparse polynomial chaos expansion using Bayesian regression for geotechnical reliability estimations

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