Sleeve Friction Research Articles

Multi-Sensor Soil Probe and Machine Learning Modeling for Predicting Soil Properties

We present a data-driven, in situ proximal multi-sensor digital soil mapping approach to develop digital twins for multiple agricultural fields. A novel Digital Soil CoreTM (DSC) Probe was engineered that contains seven sensors, each of a distinct modality, including sleeve friction, tip force, dielectric permittivity, electrical resistivity, soil imagery, acoustics, and visible and near-infrared spectroscopy. The DSC System integrates the DSC Probe, DSC software (v2023.10), and deployment equipment components to sense soil characteristics at a high vertical spatial resolution (mm scale) along in situ soil profiles up to a depth of 120 cm in about 60 s. The DSC Probe in situ proximal data are harmonized into a data cube providing vertical high-density knowledge associated with physical–chemical–biological soil conditions. In contrast, conventional ex situ soil samples derived from soil cores, soil pits, or surface samples analyzed using laboratory and other methods are bound by a substantially coarser spatial resolution and multiple compounding errors. Our objective was to investigate the effects of the mismatched scale between high-resolution in situ proximal sensor data and coarser-resolution ex situ soil laboratory measurements to develop soil prediction models. Our study was conducted in central California soil in almond orchards. We collected DSC sensor data and spatially co-located soil cores that were sliced into narrow layers for laboratory-based soil measurements. Partial Least Squares Regression (PLSR) cross-validation was used to compare the results of testing four data integration methods. Method A reduced the high-resolution sensor data to discrete values paired with layer-based soil laboratory measurements. Method B used stochastic distributions of sensor data paired with layer-based soil laboratory measurements. Method C allocated the same soil analytical data to each one of the high-resolution multi-sensor data within a soil layer. Method D linked the high-density multi-sensor soil data directly to crop responses (crop performance and behavior metrics), bypassing costly laboratory soil analysis. Overall, the soil models derived from Method C outperformed Methods A and B. Soil predictions derived using Method D were the most cost-effective for directly assessing soil–crop relationships, making this method well suited for industrial-scale precision agriculture applications.

Physical property of MICP-treated calcareous sand under seawater conditions by CPTU

MICP (Microbially induced calcite precipitation), an environmentally friendly soil improvement technique, has great potential in ocean engineering due to its ability to promote the precipitation of calcium carbonate through microbial activity to enhance the engineering properties of geomaterials. In this study, piezocone penetration test (CPTU) is used to evaluate the effectiveness of MICP treatment in calcareous sand. The change of physical properties (relative density Dr and total unit weight γt) of MICP treated calcareous sand is investigated by conducting CPTU on the geomaterials prepared in a series of mini calibration chambers (25 cm × 50 cm). Results indicate that CPTU (tip stress, sleeve friction, and porewater pressure) measurements can be used to interpret the physical characteristics of calcareous sand treated with MICP under seawater conditions. Additionally, a relationship between CPTU measurements, physical parameters (relative density Dr and total unit weight γt) of MICP treated calcareous sand is proposed and calibrated. The findings of the research extend the implementation of in-situ testing techniques such as CPTU towards physical property evaluation of bio-treated geomaterials in ocean environment, and demonstrate the potential of scaling up MICP techniques for broader engineering application.

Evaluating the Characteristics of Spatial Variability of Soil in Vertical Direction Highly Heterogeneous Region Based on Cone Penetration Test

Abstract Key parameters describing the spatial variability of soil properties based on the random field theory are the scale of fluctuation (SOF) and coefficient of variation (COV). To characterize the spatial variability of soil properties, reducing the impact of these errors and uncertainties is necessary. To accomplish this, for the five main layers of soil, we collected 18 cone penetration test (CPT) data from a highly heterogeneous region in Lianyungang New Airport, Jiangsu Province, China, and used the control variable method to analyze the influence of the estimation method of tendency, its function type and outliers. The results show that, compared with the ordinary least square method (OLSM), the least absolute deviation method (LADM) can more truly reflect the trend component of CPT parameters in the vertical direction, and the influence of other factors on SOF and COV is also studied, such as outliers and estimation functions of trend components. On this basis, a reasonable calculation process of SOF and COV is summarized, which provides a reference for the calculation of SOF and COV in vertical direction in the future. By comparing the SOF calculated by different models, the results show that the squared exponential (SQX) model has the highest SOF in 68.3% of the evaluation, and the single exponential (SNX) model has the lowest SOF in 64.4% of the evaluation. Moreover, we compared the SOF and COV of cone tip resistance (qc) and sleeve friction (fs), which showed that SOF of qc and COV of qc is lower than that of fs in 54.4 and 73.3% of all evaluations, respectively.

Lunar highlands simulant — Geotechnical characterization, 3D discrete element modeling, and cone penetration simulations

Lunar regolith on the surface of the Moon has gained great attention as a resource for many proposed lunar exploration projects. Not only is it serving as the ground support for construction projects on the Moon, but it is also the most accessible construction material for building the human habitat and a key component in in-situ resource utilization efforts. Therefore, gaining a fundamental understanding of the characteristics of lunar regolith is a critical step in the success of lunar exploration projects. However, it is challenging and expensive to obtain samples of lunar regolith directly, or conduct physical experiments on lunar regolith samples in situ. Hence, investigative alternatives, such as laboratory experiments and numerical simulation tools are very important means of investigating the properties of lunar regolith. This paper presents a comprehensive study on lunar highlands simulant (LHS-1), using both laboratory experimentation and discrete element method (DEM) simulations. In the laboratory experiment, triaxial tests were conducted on samples of LHS-1 to explore the stress–strain behaviors of the lunar regolith simulant. Correspondingly, 3D DEM simulations of the triaxial tests were conducted to generate the initial model parameters and the Taguchi method was performed to obtain the optimal calibrated input parameters of the DEM model. With the calibrated parameters, 3D DEM model of the cone penetration into LHS-1 was then built to study the responses of the regolith simulant during the penetration process. The effects of porosity and vertical stress on the tip resistance and sleeve friction profiles were investigated for both terrestrial and lunar environments to study the correlation between the in-situ state of the regolith simulants with the force reactions during the penetration.

Prediction of CPTu static sounding parameters based on DPH dynamic probing heavy test on the example of “the Praski terrace” sands inWarsaw title

This paper attempts to relate the parameters obtained from CPTu static sounding and DPH dynamic test conducted in non-cohesive alluvial deposits of the Vistula River. The investigation was carried out in eight test stations located on the left bank of the Vistula River in Warsaw. The presented theses were based on the results of static CPTU and dynamic DPH tests obtained at 8 test stations. Additionally, in order to associate the obtained sounding results to the lithological type of the tested medium, drillings and grain size analyses were performed. The correlation of the different test methods stems from the need to identify and explain observed discrepancies against the background of different geological conditions, such as moisture content or grain size distribution. The comparative analysis of the parameters obtained from static and dynamic probing, is relevant to the alluvial sediments formed the lower over-flood terrace (called “the Praski terrace”) of Warsaw. Based on the comparison this paper proposes a correlation between the cone penetration resistance the sleeve friction and the number of blows, expressed by the functional relationship. Differences in the matching formulas were shown depending on the saturation of the tested sediments. Correlations were referred to a soil type, which enabled to specify the range of applicability of the proposed relationships. The results of the study were further used to show their diversity using statistical methods. This made it possible to assess the variability of the parameters of the non-cohesive soil, which forms a single lithogenetic unit.

Developing Tree-Based Machine Learning Models for Estimating the Pile Setup Parameter for Clay Soils

Piles driven into cohesive soils usually experience increases in capacity with time, known as pile setup phenomenon. Several empirical methods have been developed to estimate the setup parameter (A), such as the well-known Skov and Denver equation. Parameter A is crucial in predicting pile setup behavior. In this study, tree-based machine learning (ML) models such as random forest (RF) and gradient boosted tree (GBT) were applied for better estimation of the setup parameter. A database consisting of setup data from 12 instrumented piles tested at different times, and corresponding cone penetration test (CPT) and soil boring data were collected. The soil properties (i.e., undrained shear strength, plasticity index, over consolidation ratio, and coefficient of consolidation) and CPT data (cone tip resistance, sleeve friction) of clayey soil layers at pile locations were utilized to develop the ML models. Three types of tree-based ML model were developed for predicting the setup parameter, A, using CPT and soil boring data. A comparison was made between the developed ML models based on soil properties, ML models based on CPT data, and an artificial neural network (ANN) model proposed in a previous study using the same dataset. Furthermore, the best performing ML models were compared with two nonlinear regression models recommended in a previous study using the same dataset for estimating the setup parameter. The results of this research clearly demonstrated the superior prediction capability of the tree-based ML models, particularly the GBT model over the ANN and the two nonlinear regression models in evaluating the pile setup parameter.

Statistical Evaluation of Sleeve Friction to Cone Resistance Ratio in Coarse-Grained Soils

The investigation of soil is a particularly important stage of structural design. Cone penetration tests (CPTs) are the most common soil investigation techniques. The results of these tests provide information about the values of cone resistance (qc) and sleeve friction (fs), which correspond to depth. Previous studies have shown that the ratio of sleeve friction to cone resistance depends on the particle size distribution in soil and its use for soil classification. Unfortunately, as an analysis of the literature shows, there is no such classification for coarse-grained soils. This paper presents statistically significant differences in the ratio of fs to qc in coarse-grained soils. Based on the research performed, the proposed coefficients depend on the classification of coarse-grained soils with respect to the size of the soil particles. The data investigated were obtained from study reports on 35 sites (5934 tests) at which the main type of soil was coarse-grained and contained different sizes of particles. Following a statistical analysis, five groups of tested coarse-grained soils, silty fine sand, clayey fine sand, fine sand, medium sand and gravelly coarse sand together with gravel, are derived. The analysed data show statistically significant differences in the ratio of fs to qc considering this particular type of soil. A ratio of fs to qc with a probability of 95% is proposed for sandy soils. The values for silty fine sand, clayey fine sand, fine sand, medium sand and gravelly coarse sand mixed with gravel are 0.009459, 0.010982, 0.009268, 0.008001 and 0.006741, respectively. A linear relationship between the fs and qc indexes is also suggested.

Analytical analysis of cylindrical cavity expansion considering particle breakage effect of sand and its application for cone penetration tests

AbstractThe particle breakage effect in sand exerts a significant influence on the design of underground space structure. However, the existing theories seldom consider the breakage effect and often lack accurate descriptions of void ratio changes, leading to substantial errors in the numerical calculations compared to the actual scenario. This study employs the simple critical state sand model (SIMSAND) to account for the particle breakage effect and transforms the drained cylindrical cavity expansion problem into a set of first‐order ordinary differential equations described by the Lagrangian method. The analytical solution of the cylindrical cavity expansion problem is calculated using Matlab programming codes. Firstly, Fontainebleau sand is investigated to analyse the influence of initial stress, void ratio and specific volume around the cavity. The combined effects of initial stress and particle breakage on the soil around the cavities result in dilatancy characteristics and a reduction in void ratio. The stress path analysis reveals that the soil around the cavities only reaches a critical state under high initial stresses. Secondly, a plane strain numerical model is established for twice expansion to verify the calculation outcomes from the cylindrical cavity expansion theory. Finally, an axisymmetric cone penetration test (CPT) model is developed to analyse the theoretical and numerical solutions for expansion stress and sleeve friction. The research results indicate that the CPT in sand need to consider the particle breakage effect, especially under high stress conditions. Without considering particle breakage, the sleeve friction is overestimated. These research findings can offer guidance for geotechnical engineering applications, such as CPT, pressuremeter tests and predictions of bearing capacity for pile foundations in sand.

Disturbance to adjacent marine deposit by offshore DCM construction

During deep cement mixing (DCM) construction, the ground adjacent to the DCM operation zone could be affected due to lateral soil movements and construction disturbances. For offshore DCM construction, multiple mixing shafts arranged in parallel at the DCM barge allow DCM panels to be constructed in a single operation but may disturb the surrounding marine soil. In this study, the degree of disturbance and the potential disturbance zone are characterized using cone penetration tests with porewater measurement (CPTu). The CPTu test results in marine deposits adjacent to offshore DCM panels before and after construction were used to evaluate the impact of DCM construction on CPTu raw measurements, soil type and soil parameters. By interpreting the in-situ soil states, the impacts on deformation and strength characteristics and the impact range were evaluated. A theoretical solution is also developed to examine the lateral displacement and stress increments caused by DCM construction. DCM construction in general caused adverse disturbance to the surrounding marine soils, decreasing the overconsolidation ratio while increasing the permeability. A more substantial decrease in sleeve friction and increase in pore water pressure was observed in coarse-grained soils.

Estimating geotechnical parameters using semisupervised learning: An example from the Dutch offshore wind farm zone

Wind energy is considered to be of great importance for promoting energy transition and achieving net-zero carbon emission. Reliable modeling and monitoring of the near-subsurface geology are crucial for successful wind farm selection, construction, operation, and maintenance. For optimal characterization of shallow seafloor sediments, 2D ultrahigh-resolution (UHR) seismic survey and 1D cone-penetration testing (CPT) often are acquired, processed, interpreted, and integrated for building 3D ground models of essential geotechnical parameters such as friction. Such a task faces multiple challenges, such as limited CPT availability, strong noise contamination in UHR seismic data, and heavy manual efforts for completing the traditional workflows, particularly acoustic impedance inversion. This study accelerates the integration by a semisupervised learning workflow with three highlights. First, it enables geotechnical parameter estimation directly from UHR seismic data without impedance inversion. The second comes from the use of a pretrained feature engine to reduce the risk of overfitting while mapping massive UHR seismic data with sparse CPT measurements through deep learning. More importantly, it allows incorporating other geologic/geophysical information, such as a predefined structural model, to further constrain the machine learning and boost its generalization capability. Its values are validated through applications to the Dutch wind farm zone for estimating four geotechnical parameters, including cone-tip resistance, sleeve friction, pore-water pressure, and the derived friction ratio, in two example scenarios: (1) UHR seismic data only and (2) UHR seismic data and an 11-layer structural model. The results verify the feasibility of data-driven geotechnical parameter estimation. In addition to the two demonstrated scenarios, our workflow can be further customized for embedding more constraints, e.g., prestack seismic and elastic/static property models, given their availability in a wind farm of interest.

Dominant factors in MiniCone, CPT and pile correlations: A data‐based approach

AbstractThe cone penetration test (CPT) contributes to the design and analysis of piles regarding geometry, installation effect, and pile capacity (shaft and toe resistance). MiniCone, as an alternative to CPT sounding, has been used to carry out field and laboratory investigations by physical modeling. More tests can be practically carried out through light equipment and small soil mass, involving fewer errors caused by boundary conditions. Furthermore, it can be used for in situ testing, such as quality control, assessment of ground improvement, and subgrade characterization. A database comprising MiniCone and CPT records in field and physical modeling is proposed with a variety of cone diameters. The case study records in the database have been obtained from 140 tests compiled from data from 26 sources. The sources include the results of 20 physical modelings and field data from six sites in 10 countries. The data comprise MiniCone and CPT cone tip resistance (), and sleeve friction (). The different cones are used in sandy, silty sand, and clayey soils via simple chambers (1 g), calibration chambers, and frustum confining vessels. In addition, correlations were found in penetration records in terms of physical modeling types, cone diameters, penetration rates, and soil densities. Moreover, and are related to capacities of pile toes and shafts using proper correlation coefficients less than unity, respectively. Correlations and dominant factors in geotechnical practice between MiniCone, CPT, and pile have been reviewed and discussed.

Development of the heat flow cone penetration test (HF-CPT)

The heat flow cone penetration test (HF-CPT) provides in-situ values of thermal conductivity (patent pending). The HF-CPT test records include (1) heat flow (HF) measurements acquired by a CPT add-on module, i.e. heating power and temperature versus time and (2) cone penetration test (CPT) measurements, i.e. cone resistance, sleeve friction and pore pressure versus depth and time. The test method requires a short interruption of the continuous CPT penetration phase, to allow stationary HF heating and cooling cycles.
 Values of thermal conductivity are derived similarly to the principles for laboratory thermal needle probes described in common ASTM standards, particularly [1].
 Data processing makes use of an advanced interpretation method that accounts for the short-cylinder effects of the HF module and short timeframe heat fluxes. The novel interpretation method includes inversion of a numerical forward model of the interaction between the heat flow module and the surrounding soil. The interpretation method also integrates standard CPT results, such that both a semi-continuous thermal conductivity profile and a continuous standard CPT profile are obtained.
 Validation of the interpretation method included comparison of thermal conductivity values derived from other test methods, notably laboratory transient plane source tests [1], in-situ thermal needle probe tests (based on [2]) and thermal cone penetration tests [3]. Figure 1 presents an example of validation results for predominantly clay soil. The results are seen to closely match those derived from an in-situ needle probe. As the HF-CPT measurements were taken two metres apart horizontally from the in-situ needle probe measurements, some deviation between the results is to be expected due to heterogeneity of the soil.

Subsurface Characterization of Malaysian Hemic Peat and Soft Clays Using CPTu and MASW

Geotechnical engineers practice a variety of in-situ investigation techniques to comprehensively characterize peat soil properties. Peat soil is a challenging soil characterized by its high water content, organic content, and low strength, making its geotechnical properties difficult to assess accurately. High water saturation and the presence of fresh fibres in peat also risk sample disturbance in collecting undisturbed peat soil samples which causes underestimation. An In-situ test known as the Cone penetration test with pore pressure measurement (CPTu) was investigated to measure the cone resistance (qc), sleeve friction (fs), and pore water pressure (u2) at various depths. In parallel, multichannel analysis of surface waves (MASW) was employed to determine the shear wave velocity (Vs) profile of the peat soil. The findings revealed that CPTu and MASW data were able to delineate the soil stratigraphy and estimate the peat thickness. However, the presence of fresh fibres in peat causes overestimation of the qc and fs values, causing difficulties in accurately determining the peat strength. Overall, the combination of CPTu and MASW data offered a comprehensive understanding of the peat’s geotechnical and dynamic characteristics. The findings have significant implications for engineering practices and ultimately contribute to safer and more resilient construction projects in challenging peat soil environments.

Sleeve friction stud welding towards formation and property

Sleeve friction stud welding (SFSW) was proposed to improve formation integrity and welding efficiency of stud joints. 18 mm diameter 5A06 stud and 15 mm thick 7A52 high-strength aluminum alloy were successfully joined at a time of 10 s. A uniform thickening joint with a chamfer was obtained, while flash defects in conventional welding were eliminated. The sleeve avoided the overflowing of plasticized materials, which facilitated material flow in the central of the joint corresponding to the taper stud, eliminating interfacial kissing-bond defects. The maximum tensile strength of SFSW joint reached 292 MPa, while fractured at the semi-hollow stud. SFSW has the potential to excellent joints with different sizes, offering a novel approach to advance of stud welding.

Development of New Correlation Fines Content, NSPT and CPT Using Neural Network Approach and Multilinear Regression to Support Liquefaction Hazard Analysis

Identification and characterization of constituent soil types in the form of Fines Content (FC) values are essential in analyzing the potential of soils to be liquefaction. Multiple Linear Regression is one of the fundamental statistical models used to determine the causality between target and predictor geotechnical parameters. The study used multilinear regression approaches and artificial neural networks to get optimal results from FC predictions. The study considers the correlation between the SBT Index and FC and several other parameters such as NSPT, Depth, Totally Overburden Stress, Initially Overburden Stress, and Sleeve Friction. The coefficient of determination resulting from the regression process shows a reasonably strong relationship between the independent and target parameters, as much as 61.4%. In comparison, the Neural Network is 96.928%, which indi-cates a nonlinear influence.

Estimating plasticity index directly from piezocone penetration tests in saturated natural soils

Plasticity index, Ip, is a relatable parameter for most geotechnical engineers, because Ip can be used to estimate other soil properties, such as internal friction angle, compression index, and low-strain shear modulus. Consequently, a method for estimating Ip directly from a piezocone penetration test (CPTU), which measures cone resistance, sleeve friction, and pore pressure would be useful for many practising geotechnical engineers. This technical note presents a method for estimating Ip profiles entirely from CPTU data. The new method has been calibrated using a case database of 258 complementary measurements taken, in natural soils, at 25 globally distributed marine sites.

Discrete element modelling of thermal penetration test with heating and cooling

A three-dimensional discrete element method (DEM) is employed to simulate thermal penetration tests with heating and cooling. This study investigates the mechanical behavior of soil particles during penetration, and examines the effects of temperature boundary condition, overburden load, and heating duration on the heat transfer mechanisms of the probe-soil system. Simulation results of penetration indicate that larger overburden load causes an increase in tip resistance (qc), sleeve friction (fs), and the isotropy of particles, whereas less influence is found on the distributions of penetration-induced displacement. Despite of the negligible effects of various temperature boundary conditions, the thermal responses of DEM model show that both the peak temperature of the probe heating module and the cooling rate of soil increase with the overburden load, while the probe insulation section shows a decreasing trend. The heat concentrates around the vicinity of the cone tip and probe shaft, and the high temperature area gradually expands with longer heating duration. Additionally, thermo-coupling analysis illustrates that the heat transfer process has a milder effect on the mechanical properties of granular soils. The numerical results are then validated against laboratory model chamber tests under comparable conditions, showing acceptable agreements.

Developing of neuro-swarm system to estimate the undrained shear strength of soil by CPT data

In this study, a novel hybrid metaheuristic model was developed to forecast the undrained soil shear (USS) property from cone penetration test (CPT) data (data from bore log sample from 70 different sites in Louisiana). This algorithm produced with the integration of grey wolf optimization (GWO) and multilayer perceptron neural network (MLP), named GWO - MLP, where different numbers of hidden layers were tested (1 to 4). The duty of optimization algorithm was to determine the optimal number of neurons in each hidden layer. To this objective, the system comprised five inputs entitled sleeve friction, cone tip persistence, liquid limit, plastic limitation, too much weight, and USS as outcome. The developed models for forecasting the USS of soil show the proposed best models have R2 at 0.9134 and 0.9236 in the training and predicting stage. Although the total ranking score of GWO-MLP2 and GWO-MLP4 is equal, the OBJ value shows that GWO-MLP4 has better performance than GWO-MLP2. In this case, considering the time of model running and a greater number of hidden layers suggests that GWO-MLP2 could be most appropriate. Therefore, the GWO-MLP3 model outperforms other GWO-MLP networks in the training and testing phase.

Sedimentary facies characterization through CPTU profiles: An effective tool for subsurface investigation of modern alluvial and coastal plains

ABSTRACTCone penetration tests, a method that is typically used to determine the engineering properties of soils, can be used as an effective tool for refined subsurface stratigraphic investigations of alluvial and coastal plains, aside from the geographic location. High‐resolution calibration of piezocone penetration tests (CPTU) with 20 sediment cores enabled the detailed characterization of alluvial, deltaic and coastal depositional systems of the Po Plain. Twelve cored facies associations, typical of alluvial and coastal plain environments, were characterized based on four distinct CPTU profiles: basic cone resistance (Qc), sleeve friction (Fs), water pore pressure (U) and friction ratio (FR). Sandy facies associations (fluvial/distributary channel, bay‐head delta, transgressive barrier, delta‐front/beach‐ridge) typically have high (>4 MPa) Qc, low‐to‐negative U and low (<2%) FR. Muddy deposits (well‐drained/poorly‐drained floodplain, swamp, lagoon and prodelta) exhibit opposite trends. Heterolithic facies associations (crevasse‐levée, offshore/delta‐front transition) display characteristic seesaw profiles. Plotting of late Quaternary deposits onto the latest version of the cone penetration test typical soil behaviour chart (Robertson, 2010) enables the identification of distinctive facies associations reflecting distinctive grain size. CPTU interpretation leads to sedimentary facies recognition well beyond the simple lithological differentiation and, in particular, allows the refined characterization of clay‐rich and silt‐rich depositional units (swamp clays and peats, central‐inner and outer lagoon, proximal/distal prodelta deposits) that exhibit only subtle differences in lithology. CPTU data can also serve for the accurate detection of key stratigraphic surfaces with potential engineering applications, such as the Pleistocene–Holocene boundary. This latter, a common feature of several alluvial and coastal plain successions, is commonly marked by an abrupt upward decrease of basic cone resistance and sleeve friction from Late Pleistocene, pedogenized, stiff strata to overlying Holocene, organic‐rich, soft deposits. This study offers an updated CPTU‐facies characterization method that could be suitable for subsurface investigations of modern alluvial and coastal plains worldwide.

Design and Application of a Deep-Sea Engineering Geology In Situ Test System

Seabed soil layer composed of soft sediments, which has a high water content, low bulk density and low shear strength, has great influence on deep-sea engineering devices. Therefore, accurate measurement of the mechanical properties of seabed sediments is a prerequisite for the construction and safe operation of deep-sea projects. In this study, a deep-sea engineering geology in situ test system was developed to measure cone resistance, sleeve friction, pore pressure and shear resistance in seafloor sediments. The system was tested on land, and the feasibility of the system was verified. We conducted sea trials in the South China Sea and acquired datasets from five stations. The data were analyzed using the Eslami–Fellenius soil classification map, and the soil classification of the site was obtained. The obtained values of cone resistance, sleeve friction, pore pressure and shear resistance can provide mechanical data support for deep-sea engineering in this area.