Impact of sample path smoothness on geotechnical reliability

The scale of fluctuation (SOF) of a spatially variable soil property has been known to be the most important parameter that characterizes the effect of spatial averaging, and the type of the auto-correlation model is thought to be of limited impact. This paper shows that this statement (SOF is the most important parameter) is true if the limit state function is completely governed by spatial averaging. However, this paper also shows that the sample path smoothness can have signifcant impact if the limit state function is not completely governed by spatial averaging. Three practical examples are presented to illustrate the effect of sample path smoothness.The authors would like to thank Dr. Yu-Gang Hu and Miss Tzu-Ting Lin for their efforts in producing the results in some plots

Similarity quantification of soil spatial variability between two cross-sections using auto-correlation functions

In geotechnical engineering, an appreciation of local geological conditions from similar sites is beneficial and can support informed decision-making during site characterization. This practice is known as “site recognition”, which necessitates a rational quantification of site similarity. This paper proposes a data-driven method to quantify the similarity between two cross-sections based on the spatial variability of one soil property from a spectral perspective. Bayesian compressive sensing (BCS) is first used to obtain the discrete cosine transform (DCT) spectrum for a cross-section. Then DCT-based auto-correlation function (ACF) is calculated based on the obtained DCT spectrum using a set of newly derived ACF calculation equations. The cross-sectional similarity is subsequently reformulated as the cosine similarity of DCT-based ACFs between cross-sections. In contrast to the existing methods, the proposed method explicitly takes soil property spatial variability into account in an innovative way. The challenges of sparse investigation data, non-stationary and anisotropic spatial variability, and inconsistent spatial dimensions of different cross-sections are tackled effectively. Both numerical examples and real data examples from New Zealand are provided for illustration. Results show that the proposed method can rationally quantify cross-sectional similarity and associated statistical uncertainty from sparse investigation data. The proposed method advances data-driven site characterization, a core application area in data-centric geotechnics

10th Anniversary Special Issue for Georisk

10.1080/17499518.2016.1277085Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards1111-

Role of reliability calculations in geotechnical design

10.1080/17499518.2016.1265653Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards1114-2