Probabilistic Framework for Strength Limit and Service Limit Checks of Drilled Shafts considering Soil Spatial Variability

Author(s):  
Haijian Fan ◽  
Robert Liang

This paper presents a performance-based, probabilistic framework for design of a drilled shaft under axial and lateral loading that can consider spatial variability of soil properties at a project site. The performance criteria of a drilled shaft are stated in relation to the limiting tolerable deformations for strength limit and service limit, respectively. The computational algorithm for calculating the deformation of a drilled shaft is based on the commonly adopted load transfer method and the p-y method. “Geotechnical failure” is defined as an event in which the specified performance criteria are not met. Three failure modes are considered: axial movement, lateral deflection, and angular distortion. The spatial variability of soil properties is considered by using random field modeling techniques in which correlation length is introduced to account for site variability in addition to mean and variance. The method of fitting a sample autocorrelation function to a prescribed correlation function by using the method of ordinary least squares is introduced for determining site-specific correlation length for soil parameters. Geostatistical principles known as kriging are employed to estimate unknown parameters at unsampled locations from neighboring sampled locations. A numerical example is given to illustrate the application of the proposed methodologies. The example demonstrates that correlation length is an important statistical descriptor for characterizing site variability. Performance-based design provides unified consideration for both strength limit and service limit. Finally, the overall probability of failure for a drilled shaft when all three failure modes are considered is greater than the failure probability for any individual failure mode.

Author(s):  
Chao Shi ◽  
Yu Wang

Consolidation analysis is a key task for reclamation design. Although consolidation is a long-term process, acceleration of consolidation is often preferred for speeding up the reclamations. Before proposing measures to accelerate consolidation and reclamation process, it is imperative to have an accurate prediction of consolidation settlement for fine-grained materials, which is greatly affected by spatial distribution of subsurface zones with different soil types (i.e., stratigraphic heterogeneities and uncertainty) and spatial variability of soil properties. In current practice, calculation of consolidation settlement often uses simplified stratigraphic boundaries and deterministic consolidation parameters without considering stratigraphic uncertainty or soil property spatial variability. The oversimplified practice might result in unconservative estimations of consolidation settlement and pose threats to safety and serviceability of constructed facilities on reclaimed lands. In this study, a stochastic framework is proposed for consolidation settlement assessment with explicit modeling of stratigraphic uncertainty and spatial variability of soil properties by machine learning and random field simulation from limited site investigation data. The proposed method effectively generates multiple realizations of geological cross-section and random field samples of geotechnical properties from limited measurements and offers valuable insights into spatial distribution of the estimated total primary consolidation settlement curves and angular distortion.


2019 ◽  
Vol 55 (9) ◽  
pp. 1329-1337
Author(s):  
N. V. Gopp ◽  
T. V. Nechaeva ◽  
O. A. Savenkov ◽  
N. V. Smirnova ◽  
V. V. Smirnov

2020 ◽  
Vol 14 (4) ◽  
pp. 597-608
Author(s):  
Mohammad Ajami ◽  
Ahmad Heidari ◽  
Farhad Khormali ◽  
Mojtaba Zeraatpisheh ◽  
Manouchehr Gorji ◽  
...  

2003 ◽  
Vol 72 (1) ◽  
pp. 31-41 ◽  
Author(s):  
Gerd Dercon ◽  
Jozef Deckers ◽  
Gerard Govers ◽  
Jean Poesen ◽  
Henrry Sánchez ◽  
...  

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Gabriel Soropa ◽  
Olton M. Mbisva ◽  
Justice Nyamangara ◽  
Ermson Z. Nyakatawa ◽  
Newton Nyapwere ◽  
...  

AbstractA study was conducted to examine spatial variability of soil properties related to fertility in maize fields across varying soil types in ward 10 of Hurungwe district, Zimbabwe; a smallholder farming area with sub-humid conditions and high yield potential. Purposively collected and geo-referenced soil samples were analyzed for texture, pH, soil organic carbon (OC), mineral N, bicarbonate P, and exchangeable K. Linear mixed model was used to analyze spatial variation of the data. The model allowed prediction of soil properties at unsampled sites by the empirical best linear unbiased predictor (EBLUP). Evidence for spatial dependence in the random component of the model was evaluated by calculating Akaike’s information criterion. Soil pH ranged from 4.0 to 6.9 and showed a strong spatial trend increasing from north to south, strong evidence for a difference between the home and outfields with homefields significantly higher and between soil textural classes with the sand clay loam fraction generally higher. Soil OC ranged from 0.2 to 2.02% and showed no spatial trend, but there was strong evidence for a difference between home and outfields, with mean soil OC in homefields significantly larger, and between soil textural classes, with soil OC largest in the sandy clay loams. Both soil pH and OC showed evidence for spatial dependence in the random effect, providing a basis for spatial prediction by the EBLUP, which was presented as a map. There were significant spatial trends in mineral N, available P and exchangeable K, all increasing from north to south; significant differences between homefields and outfields (larger concentrations in homefields), and differences between the soil textural classes with larger concentrations in the sandy clay loams. However, there was no evidence for spatial dependence in the random component, so no attempt was made to map these variables. These results show how management (home fields vs outfields), basic soil properties (texture) and other factors emerging as spatial trends influence key soil properties that determine soil fertility in these conditions. This implies that the best management practices may vary spatially, and that site-specific management is a desirable goal in conditions such as those which apply in Ward 10 of Hurungwe district in Zimbabwe.


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