Bayesian back analysis for dynamic CPTu strain-rate correction.

Cone penetration testing (CPT) has become the industry standard for in situ testing of cohesionless soils, and in particular, field liquefaction evaluation. The empirical methods for the interpretation of CPT data are either based on field data or the observation of CPT measurements in laboratory samples. In this study, a miniature cone penetrometer (with a diameter of 6 mm) is developed for understanding the response of loose to medium-dense sands. A modified triaxial cell is used for sample preparation and containment of the sample during cone penetration. The miniature cone can measure cone tip resistance, sleeve friction, and excess pore water pressure developed at the cone tip. While cone tip resistance is measured by a separate load cell, sleeve friction is obtained by subtracting cone tip resistance from a combined measurement of tip resistance and sleeve frictional force. Due to the free-draining nature of the sand tested in this study, no excess pore water pressure is developed during cone penetration. The measured data from the miniature cone are verified by comparison with CPT resistances measured in several other calibration chamber experiments on similar sands. Compared to a large calibration chamber with a standard size cone, the miniature cone allows quicker and less expensive CPT experiments in a more uniform sample.

Field evaluation of deformation characteristics of a lacustrine clay deposit using seismic piezocone tests

Offshore soil sampling, conducted for the purpose of site characterization, is often plagued with sample disturbance issues and other difficulties related to the sampling process. Field tests, such as the piezocone penetration test (PCPT), are not subject to such difficulties and are routinely used to complement the sampling program. However, the conventional practice is to directly evaluate the in-situ undrained shear strength using an assumed or fitted bearing factor, Nkt. The hybrid cavity-expansion and critical state theory establishes the relationship between OCR and piezocone measurements. Once the stress history is known, the various modes of soil strength may be estimated through the use of appropriate S values. This paper presents interpretations of piezocone data at 12 separate locations in the Jubilee field development in the Gulf of Guinea. Separate and independent profiles of stress history and shear strength of the deep water clays found at this site are compared with the results of a detailed and extensive laboratory testing program. The experimentally determined SHANSEP parameter m, was found to be representative of the soil, but the strength ratio from DSS testing was judged to be too high. S values used in the interpretations were based on those reported in the literature for similar soils. Internal friction angles of 30° to 31° and an IR of 100 adequately predicted OCR with depth. The profiles derived from the PCPT data compared well with the results of CRS and DSS tests consolidated to in-situ pressures. The use of piezocone data in this manner allows the development of a site-specific family of undrained shear strength profiles that are continuous throughout the depth of testing. This profile can complement data from the soil sampling program which inherently has information gaps arising from the limitations of sampling and testing or as a result of uncertainty caused by sample disturbance. The interpretations are relatively quick and easy to perform and can increase the degree of confidence in the soil modeling parameters selected. Introduction Deepwater field development projects often include a scope for piezocone penetration tests (PCPT). These provide quick profiles of the subsurface soil stratigraphy. The cone tip resistance (qc), sleeve friction (fs), and induced penetration porewater pressures (u) are measured continuously and expediently with depth (z). These readings are post processed and interpreted to provide relevant geotechnical parameters. The PCPT results can be used to supplement the data obtained from cores (piston core and jumbo piston core) or from boreholes if they are present. This is often a very useful exercise because sampling from deepwater sediments is in itself a very costly process and sample disturbance is an ever present concern. Comparison of shear strengths as measured from soil samples and those derived from the PCPT measurements can clarify whether an observed reduction in strength at a particular depth is related to sample disturbance or not. The advantage is due to the test being conducted in-situ rather than in the laboratory. Site investigations for deepwater offshore projects generally include a soil sampling and testing program where the soil stress history and strength profiles with depth are defined. PCPT data is used to complement this program and is often very useful in providing information between sampling depths and through data gaps which may exist due to failed sample recovery or insufficient penetration. PCPT data also provides a separate and independent estimation of undrained shear strength which is routinely compared with the compiled results of various laboratory shear strength tests (i.e. UU and minivane test). Since no single soil test is singularly definitive of the " true" strength, PCPT data is a powerful tool in the arsenal of geotechnical engineers who are tasked with the rather elusive goal of characterizing and modeling soil profiles in deep water that are, very often, green-field regions.

An accurate knowledge of ground properties is essential in the design of civil engineering structures and this information is usually obtained by a combination of in situ and laboratory testing. The interpretation of in situ testing depends in the main on empirical correlations with published data, whilst the usefulness of laboratory soil testing depends on how realistically the field conditions can be reproduced. This paper provides a comparative study of ground properties determined using a number of different techniques in a sequence of Pleistocene sands and gravels overlying till in the Vale of St Albans. In situ testing techniques included both static and dynamic cone penetration tests and also a limited number of self-boring pressuremeter tests in the sands. Recovery of bulk disturbed samples from the site enabled laboratory shear tests to be carried out on large samples of the soil compacted to their maximum density conditions. Values for modulus, angles of shearing resistance and relative density from the in situ and laboratory tests on the soil are compared, and these in turn are correlated with data published by others for similar soil types.

In the present work high fly ash content flowable fill of Botswana fly ash has been investigated. The lime and cement in the proportion of 3%, 6%, 9%, 12%, and 15% and water equal to 125% of its liquid limit was mixed with fly ash to produce self leveling fill. The safe bearing capacity (S.B.C) of these fills was determined in the laboratory after 1,2,4, 7, and 15 days using drop test and the Dynamic Cone Penetration (DCP) test were determined in the field after 1, 7, and 28 days. The California Bearing Ratio (CBR) and Unconfined Compressive Strength (UCS) of the fills were calculated from DCP values using empirical correlations. It was noted that there is no significant effect of lime or cement on seven days strength. The safe bearing capacity attained after seven days were approximately two to four times the safe bearing capacity of most well compacted earthen backfill materials. The CBR of the fills after seven days was found to be more than the requirement for the base and sub base for roads. The findings suggest that the high fly ash flowable fills of Botswana fly ash can successfully be used without any addition of lime or cement.

This study presents a detailed characterization of the inherent variability of a silty gold tailings deposit on the basis of piezocone penetration test (CPTu) data. The database originates from a comprehensive site investigation campaign and was processed via a layer separation algorithm to identify strata and determine relevant statistical parameters. Profiles were classified considering granular (drained), intermediate (partially drained), and fine (undrained) material behavior, allowing for behavior-specific probabilistic analysis of cone resistance (qc), sleeve friction (fs), and pore pressure (u2). The results revealed greater measurement variability for qc, fs, and u2 in coarse-grained materials, whereas the strength parameters—particularly the undrained shear strength (Su)—exhibit greater variability in fine-grained materials. The classification approach also enabled a consistent estimation of the scale of fluctuation, an essential parameter for spatial variability modeling. A complementary numerical analysis illustrated the influence of inherent variability on the normalized cone resistance (qc/qcUD) under different drainage conditions. Greater dispersion occurred at lower velocities, which is characteristic of drained behavior. The alignment between the theoretical bounds and field data supports the robustness of the methodology, providing a valuable framework for reliability-based geotechnical design.

Bayesian Mixture Analysis of a global database to improve unit weight prediction from CPTu

Over the past decades, the piezocone testing has been increasingly adopted for field investigation as it offers a quick and cost-effective methodology for subsoil profiling and geotechnical parameters estimation. Although the piezocone testing has revealed good applicability worldwide, difficulties are encountered in complex soil conditions, such as soft sensitive clays. One of the key issues in such soils is represented by the poor quality of the sleeve friction (fs) measurement due to inaccuracy and poor resolution of the sleeve sensor. This paper investigates the influence of fs data quality on soil parameters determination with particular emphasis on the soil behavior type (SBT) chart classification. The field investigation was conducted in a soft sensitive clay site located in Finland using two different penetrometers: a standard piezocone and an advanced piezocone characterized by enhance accuracy sleeve friction sensor. Results show that the use of high-resolution piezocone plays a key role in soft clays to avoid misleading soil type classification.

Perception of spatial orientation relies on integration of sensory information encoding body position with external cues, such as visual inputs from the environment. Inherent variability in sensory inputs plays a critical role in how sensory information is processed. Unlike physical stimuli, neural encoding and transformation of these signals are subject to noise introduced at multiple levels within the sensory pathway. The Bayesian observer model provides a robust computational framework for understanding how the brain accommodates uncertainty to generate earth-vertical perception. Within the Bayesian framework, Gaussian prior distributions serve to constrain noisy sensory signals, allowing the brain to generate reliable estimates even in the presence of ambiguous input. These sensory priors are typically assumed to be centered at zero based on the premise that the brain defaults to upright body orientation when interpreting sensory signals. In this study, we compute the sensory priors associated with the eye and head orientation as two key sensory contributors to spatial orientation. These priors were estimated as free parameters within the Bayesian spatial model by combining measurements of subjective visual vertical (SVV) with perceived head and eye orientations. Our findings reveal that the Bayesian framework can effectively identify the sensory priors for eye and head orientation that contribute to spatial orientation. The results demonstrate the potential of using sensory priors as individualized functional markers for the neural mechanisms underlying spatial orientation.NEW & NOTEWORTHY By combining measurements of subjective visual vertical (SVV) with perceived head and eye orientations, we applied the Bayesian observer model to derive the sensory priors for eye and head orientation. Our findings reveal that the Bayesian spatial model can effectively identify the sensory priors of eye and head orientation that contribute to spatial orientation. This approach demonstrates the potential of sensory priors as individualized functional markers for the neural mechanisms underlying spatial orientation.

Spatial correlation structures of CPT data in a liquefaction site

Dynamic compressive parameters of coal specimens were driven not only by impact velocities, but also by preloaded confining pressure, and impact loading methods. This paper investigates the influences of dynamic parameters based on the split Hopkinson pressure bar (SHPB) experimental data obtained from the dynamic compressive impact tests on 12 coal specimens under four different impact velocity. The analysis of the experimental results confirmed that with an increase of dynamic velocity, and then the dynamic compressive strength is constantly increases. When the dynamic velocity is 8.10 m/s, and then reacheed the maximum dynamic strength of 25.11 MPa of the coal specimen. Furthermore, the dynamic velocity of 8.10 m/s was defined as the threshold value of the dynamic strength increased. The greater of dynamic velocity, the higher degree of fragmentation of the coal specimens. The transmitted energy, reflected energy, and absorbed energy of coal specimens shows a directly proportional relationship with the incident energy. The results highlight the first and second dynamic deformation moduli can clearly explained the deformation laws of elastic and plastic stages of the coal specimens. It is also found that the maximum of strain rate is 177.28 s−1 and the maximum ultimate strain is 2.47 × 10−2of the coal specimens. The research results were beneficial to supplement the coal-rock dynamics theory, thus also providing a direction for safety and effective mining and crushing in coal mines.

Bayesian approach for probabilistic characterization of sand friction angles

Geophysical imaging techniques, such as seismic crosshole tomographic methods, provide spatially continuous information about physical parameter variations in the ground. This physical parameter information, e.g., distribution of seismic velocities, is frequently used to estimate spatial distributions of geotechnical target parameters that could only be calibrated by sparse measurements in boreholes. The employed geophysical model generation procedure is usually deterministic, which does not allow for transducing the ambiguity inherent to the geophysical imaging procedure into the prediction of spatial geotechnical target parameter distributions. Here, we review self-organizing geophysical inversion of tomographic data allowing for data-driven ambiguity assessment of geophysical models. Opposite to deterministic geophysical imaging resulting in a single geophysical model this approach delivers ensembles of different but equally plausible geophysical models. We use this probabilistic information about geophysical model uncertainty and employ fuzzy cluster analysis for propagating the geophysical model generation uncertainty into probabilistic inference of spatial geotechnical parameter distributions exemplary illustrated by generating 2D scenarios of sleeve friction distribution.

Physical modeling of cone penetration testing (CPT) can be carried out under known stress, deformation, and density conditions using laboratory calibration chambers. This study presents the results of reduced-scale model CPTs (MCPT) on Fraser River sand using a calibration chamber that allows anisotropic consolidation of soil specimens, as well as the measurement of shear wave velocity (VS), using bender elements. In addition to VS, MCPT can also measure cone tip resistance (qc), sleeve friction (fs), and pore water pressure developed behind the cone tip (Δu2). The CPT model tests are performed on Fraser River sand at different effective stresses and relative densities. The CPT calibration chamber test results are comparable with those of in situ seismic cone penetration tests (SCPT) in the Fraser River delta, existing correlations between VSand qc, and calibration chamber tests on other sands. This confirms the overall feasibility of the MCPT calibration chamber design for the measurements of qc, fs, and VSin sandy soils.

The evaluation of load-settlement behavior of piles is very crucial in meeting the serviceability criteria for pile analysis and design. The most reliable approach for estimating this behavior can be achieved by conducting pile load tests. However, due to the considerable expense and time requirement of such in-situ testing, the load-transfer methods have been used routinely in practice. In this paper, an alternative tree-based machine learning (ML) modeling is explored to predict the load-settlement behavior of axially loaded single piles from cone penetration test (CPT) data. Two variants of tree-based ML models, the random forest (RF) and gradient boosted tree (GBT), are developed in this study to estimate the load-settlement behavior of piles from CPT data (corrected cone tip resistance, qt, and sleeve friction, fs). A database of load-settlement curves of 64 static pile load tests and the corresponding CPT test data were compiled and used for the development of these ML models. The developed RF and GBT models are evaluated based on several statistical criteria. The load-settlement curves for six PLTs predicted using the developed RF and GBT models were compared with the measured data and the load-settlement curves predicted using the conventional load-transfer methods. The results demonstrated the great potential of tree-based ML (RF, GBT) models for predicting the load-settlement behavior of axially loaded piles from CPT data. The comparison clearly shows that the ML models outperformed the conventional load-transfer methods. Amongst the two ML models, the results show that the GBT model outperformed the RF model.