Probabilistic Approaches for Ultimate Resistance of Drilled Shafts in Sands Considering Spatial Variability

被引:0
作者
Luo, Z. [1 ]
Wang, L. [2 ]
Gong, W. [3 ]
Juang, C. H. [3 ]
机构
[1] Univ Akron, Dept Civil Engn, Akron, OH 44325 USA
[2] Parsons Brinckerhoff, 100 S Charles St, Baltimore, MD 21201 USA
[3] Clemson Univ, Glenn Dept Civil Engn, Clemson, SC 29634 USA
来源
GEOTECHNICAL ENGINEERING | 2015年 / 46卷 / 02期
关键词
Drilled shafts; Standard penetration test; Probability; Random field modeling; Spatial variability; Spatial average; Spatial correlation;
D O I
暂无
中图分类号
P5 [地质学];
学科分类号
0709 ; 081803 ;
摘要
In this paper, existing probabilistic approaches for determining the ultimate resistance of drilled shafts in sands considering the spatial variability of soil properties are evaluated and compared. The first approach is realized through random field modeling implemented with Monte Carlo simulation (MCS). The second approach is a simplified method based on the spatial averaging technique. The third approach is yet another simplified method based on the spatial correlation between spatial averages. The last two (simplified) approaches can be efficiently implemented without MCS so that much less computational effort is required. The comparison study shows that the three probabilistic approaches yield practically identical results (i.e., probability of failure in the designed drilled shafts). This study also highlights the importance of considering the spatial variability of soil properties and the model bias in the design of drilled shafts. Results indicate that: (1) neglecting spatial variability often leads to an overestimation of the probability of failure; (2) ignoring the model bias results in either overestimation or underestimation of the probability of failure, depending on the compression load applied to the drilled shafts.
引用
收藏
页码:102 / 110
页数:9
相关论文
共 47 条
[42]   Probabilistic assessment of tunnel convergence considering spatial variability in rock mass properties using interpolated autocorrelation and response surface method [J].
Lu, Qing ;
Xiao, Zhipeng ;
Zheng, Jun ;
Shang, Yuequan .
GEOSCIENCE FRONTIERS, 2018, 9 (06) :1619-1629
[43]   Probabilistic analysis of offshore single pile composite foundation considering rotated anisotropy spatial variability of multi-layered soils [J].
Yang, Zhiyong ;
Chen, Junxian ;
Feng, Yu ;
Li, Xueyou .
COMPUTERS AND GEOTECHNICS, 2025, 182
[44]   Probabilistic analysis of foundation settlement considering spatial variability in soil parameters: a comparison of hardening soil model and Mohr-Coulomb [J].
Teshager, Daniel Kefelegn ;
Chwala, Marcin ;
Pula, Wojciech .
GEORISK-ASSESSMENT AND MANAGEMENT OF RISK FOR ENGINEERED SYSTEMS AND GEOHAZARDS, 2025,
[45]   Probabilistic analysis of width-limited 3D slope considering spatial variability of Hoek-Brown rock masses [J].
Sun, Zhibin ;
Ding, Juncao ;
Yang, Xiaoli ;
Wang, Yixian ;
Dias, Daniel .
ROCK MECHANICS AND ROCK ENGINEERING, 2024, 57 (11) :9759-9780
[46]   Probabilistic analyses of three-dimensional circular footing resting on two-layer c–ϕ soil system considering soil spatial variability [J].
Kouseya Choudhuri ;
Debarghya Chakraborty .
Acta Geotechnica, 2022, 17 :5739-5758
[47]   Probabilistic analyses of three-dimensional circular footing resting on two-layer c-φ soil system considering soil spatial variability [J].
Choudhuri, Kouseya ;
Chakraborty, Debarghya .
ACTA GEOTECHNICA, 2022, 17 (12) :5739-5758