Abstract

During reclamation projects huge amounts of sands are dredged and placed to create artificial land. To increase the density and therefore to mitigate the potential risk of liquefaction as well as to increase the stiffness and internal friction angle of the sand, it is often necessary to compact the reclaimed sand. The performance targets for compaction are frequently verified by means of achieving a particular relative density that is generally correlated from Cone Penetration Tests (CPT). For many reclamation projects, due to the non-availability of local quartz or silica sands, crushable, carbonate or calcareous sands are used. In these crushable sands, due to the very high stress concertation below the CPT cone, the particles tend to crush. The well-known published correlations between the relative density and cone resistance are established for non-crushable silica sands and are thus not applicable to these crushable sands and can result in over treatment costing time and money. Usually, the crushing effect is quantified in a calibration chamber test and a project specific correction factor is introduced. Alternatively, to avoid this costly and time-consuming procedure the use of measuring the shear wave velocity with seismic CPTs (SCPT) is possible. The Cyclic Stress Ratio (CSR) for liquefaction analysis and other soil parameters required for the design verification can be correlated without being influenced by the crushing of particles due to the non-invasive procedure. This paper gives an overview of the common practice for work verification in crushable sand and shows an approach to determine the required compaction parameters using seismic CPTs.

Abstract

This paper is an extension of our previous research investigating the potential of machine learning models to estimate shear wave velocity (Vs) from piezocone penetration test (CPTu) measurements. The aim of this update is to examine the effect of incorporating geographical information, namely latitude and longitude, as input parameters to the machine learning models. New models are developed by incorporating both CPTu parameters and spatial coordinates as input features and are compared to models developed with only CPTu parameters. Furthermore, SHAP (SHapley Additive exPlanations) analysis is employed to assess the importance of different features and variables in the developed machine learning models. The results show improvement in prediction performance when adding geographical data, indicating the influence of geological variations on Vs. The paper shows the potential of using geospatial information to improve the data-driven approach for estimating soil properties from CPTu tests when large worldwide datasets are available.

Abstract

Within the offshore wind sector, following the conclusions of the Pile Soil Analysis (PISA) Project increased emphasis has been placed on the acquisition of in-situ 𝐺𝐺𝑚𝑚𝑚𝑚𝑚𝑚 data, to corroborate laboratory-based measurements, to allow for foundation weight optimization. This requirement for higher fidelity data at all wind turbine locations is coupled with the increased requirement to acquire data in shorter periods to meet ambitious development schedules for offshore wind farms. The development of a deep push seabed SCPT which can be deployed fully autonomously is considered to address this challenge facing the offshore wind industry. Recognising that within the current standards there is a shortfall on what is considered as accurate and reliable data with regards to having confidence in the shear wave velocity (𝑣𝑣𝑠𝑠) measurements obtained offshore, there is a requirement for discussion within the industry; clients, designers and contractors, on how to provide improved set-ups, acquisition and interpretation methods in order to increase the confidence in the 𝑣𝑣𝑠𝑠 data acquired. The case study described within this paper, initiated by such dialog, presents the specification, construction, testing and utilisation of a dual array non-drilling mode seismic cone penetration test (SCPT) device and seismic source to provide demonstrable reliability and accuracy in acquisition and interpretation of in-situ 𝑣𝑣𝑠𝑠 measurements. Within this context, the paper describes; the engineering considerations and optimisation of a novel device intended for deployment from a new generation of robotic vessel; application and limitations of the set-up during trials and offshore operations; commentary on the in-situ data including challenges encountered during interpretation and comparison with existing data acquired at the same location, established correlations and site-specific correlations.

Abstract

Accurate quantification of the shear wave velocity, Vs, of geo-materials is an important consideration in geotechnical design. Seismic Cone Penetration Testing (SCPT) measures shear wave travel times from a source to in situ receivers along assumed travel paths to calculate Vs. Despite complexities and uncertainties associated with obtaining Vs, results are often reported to designers as a single deterministic profile without an intuitive measure of uncertainty that can be incorporated into the design process. A rigorous workflow to rapidly obtain uncertainty-quantified profiles from SCPT using a Bayesian inversion approach is developed. While similar approaches have been documented, this inversion approach explicitly considers sources of measurement error which are generally neglected (i.e., assumed to be low) in order to deliver more realistic probability distributions of true Vs and improve robustness against imperfect data. Such errors can remain undetected when using traditional approaches, despite potentially leading to inaccuracy. Additionally, an outlier detection framework is incorporated into the workflow to improve accuracy. The workflow is demonstrated by application to a large database of SCPT data. The results show significant improvement over existing methods in terms of robustness and validity, and therefore that the workflow is a valuable tool for practical analyses. Further, they provide crucial insight into the prevalence and magnitude of key errors which are traditionally present but undetected.

Abstract

This paper quantifies the influence of seismic Cone Penetration Test (sCPT) interpretation and processing methods on shear wave velocity (VS) profile uncertainty using data from 20 sites in Christchurch, New Zealand. The near-surface soil profiles varied across the sites, both in terms of the soils that were present and the profile layering characteristics, reflecting the depositional environment that is influenced by alluvial and coastal processes. The same experimental setup was used at each site, consisting of a dual receiver sCPT cone and a hammer source method at a consistent horizontal offset distance. Three commonly used shear wave arrival time picking methods and the cross-correlation method were used to define arrival times and time intervals between testing depths for each site. The pseudo-interval, true-interval and slope-based processing methods were used with these arrival times and time intervals to develop 11 VS profiles for each site. Alongside this, a ray tracing inversion method provided an additional VS profile at each site. The uncertainty in the VS profiles that were developed at each site are presented, highlighting the variability resulting from different processing and picking methods. Results across sites are combined to provide a representation of the uncertainty across all methods and the differences in the uncertainty across the various processing methods.