Probabilistic identification of soil stratification

Abstract

Identification of soil stratification is vital to geotechnical structural design and construction where the soil layer, soil type and properties are necessary inputs. Although methods are available for classifying the soil profiling using measured cone penetration test (CPT) data, the identification of soil stratification at unsampled locations is still difficult due to significant variability of natural soil. The identification is further complicated by the considerable uncertainties in the CPT measurements and soil classification methods. This study aims to develop a probabilistic method to predict soil stratification at unsampled locations by explicitly filtering the uncertainties in soil classification systems. An established Kriging interpolation technique is used to estimate the CPT parameters which are further interpreted to identify the soil stratification. Equations are derived to quantify the degree of uncertainties reduced by this method. The approaches are illustrated using a database of 26 CPT tests recently sourced from a dike near Ballina, Australia. Results show that the majority of the uncertainties in the soil parameters are screened by a soil classification index. The remaining uncertainties are further filtered by the soil classification systems. A clear stratification with a high degree of confidence is obtained in both horizontal plane and vertical unsampled locations, which shows excellent agreement with the existing CPT tests. This study provides a methodology to clearly identify the soil strata and reduce the uncertainties in prediction of design properties, paving the way for a more cost-effective geotechnical design.

Site characterisation for the Ballina field testing facility

“…The clay displays an open flocculated structure so that high compressibility and low strength would be expected. The uniformity of the soil profile at the Ballina site has been recently confirmed through a statistical analysis of 26 CPT test data aimed at predicting the stratigraphy at locations away from measurements (Li et al, 2016). The results showed that the correlation distance in the horizontal direction ranges between 40 m and 60 m.…”

Determination of efficient sampling locations in geotechnical site characterization using information entropy and Bayesian compressive sampling

“…In the context of geotechnical site characterization, soil property varies spatially (e.g., along depths or a horizontal direction), and spatial variability of soil property (e.g., a soil property profile along depth or a horizontal direction) may be modeled as a random process (e.g., Webster 2000;Baecher and Christian 2003;Vanmarcke 2010;Wang et al 2010), especially when there are no measurement data available or when the number of measurements is sparse and limited (e.g., Stuedlein et al 2012; Wang and Cao 2013b; Li et al 2016). Therefore, the information entropybased method described by eqs.…”

A novel web based application for storing, managing and sharing geotechnical data, illustrated using the national soft soil field testing facility in Ballina, Australia

“…At the Australian NFTF, three large scale trial embankments have been built , four large scale footing load tests conducted, numerous bore holes drilled with high quality soil samples collected and tested in a range of laboratory apparatus (Pineda et al, 2014;Pineda et al, 2016a,b) and a range of in-situ tests including cone (Kelly et al, 2016, Li et al, 2014and Li et al, 2016 One of the aims in establishing the Australian NFTF was to leave a lasting legacy to the engineering community by making the data freely available in digital form. This will ensure that this comprehensive data set can be reused by academics and practitioners from around the world and therefore maximize the impact of this publically funded research project.…”