A Bayesian unsupervised learning approach for identifying soil stratification using cone penetration data

2019 ◽  
Vol 56 (8) ◽  
pp. 1184-1205 ◽  
Author(s):  
Hui Wang ◽  
Xiangrong Wang ◽  
J. Florian Wellmann ◽  
Robert Y. Liang
Keyword(s):  
Computational Cost ◽  
Physical Space ◽  
Soil Classification ◽  
Feature Space ◽  
Soil Behavior ◽  
Cone Penetration ◽  
Soil Layers ◽  
Tip Resistance ◽  
Input Dataset ◽  
Spatial Configurations

This paper presents a novel perspective to understanding the spatial and statistical patterns of a cone penetration dataset and identifying soil stratification using these patterns. Both local consistency in physical space (i.e., along depth) and statistical similarity in feature space (i.e., logQt–logFrspace, where Qtis the normalized tip resistance and Fris the normalized friction ratio, or the Robertson chart) between data points are considered simultaneously. The proposed approach, in essence, consists of two parts: (i) a pattern detection approach using the Bayesian inferential framework and (ii) a pattern interpretation protocol using the Robertson chart. The first part is the mathematical core of the proposed approach, which infers both spatial pattern in physical space and statistical pattern in feature space from the input dataset; the second part converts the abstract patterns into intuitive spatial configurations of multiple soil layers having different soil behavior types. The advantages of the proposed approach include probabilistic soil classification and identification of soil stratification in an automatic and fully unsupervised manner. The proposed approach has been implemented in MATLAB R2015b and Python 3.6, and tested using various datasets including both synthetic and real-world cone penetration test soundings. The results show that the proposed approach can accurately and automatically detect soil layers with quantified uncertainty and reasonable computational cost.

Computerized Cone Penetration Test for Soil Classification

2008 ◽  
Vol 2053 (1) ◽  
pp. 47-64 ◽  
Author(s):  
Murad Y. Abu-Farsakh ◽  
Zhongjie Zhang ◽  
Mehmet Tumay ◽  
Mark Morvant
Keyword(s):  
Classification System ◽  
Soil Classification ◽  
Cone Penetration Test ◽  
Classification Systems ◽  
Fuzzy Classification ◽  
Classification Methods ◽  
Penetration Test ◽  
Cone Penetration ◽  
Cone Tip Resistance ◽  
Tip Resistance

Computerized MS-Windows Visual Basic software of a cone penetration test (CPT) for soil classification was developed as part of an extensive effort to facilitate the implementation of CPT technology in many geotechnical engineering applications. Five CPT soil engineering classification systems were implemented as a handy, user-friendly, software tool for geotechnical engineers. In the probabilistic region estimation and fuzzy classification methods, a conformal transformation is first applied to determine the profile of soil classification index (U) with depth from cone tip resistance (qc) and friction ratio (Rf). A statistical correlation was established in the probabilistic region estimation method between the U index and the compositional soil type given by the Unified Soil Classification System. Conversely, the CPT fuzzy classification emphasizes the certainty of soil behavior. The Schmertmann and Douglas and Olsen methods provide soil classification charts based on cone tip resistance and friction ratio. However, Robertson et al. proposed a three-dimensional classification system that is presented in two charts: one chart uses corrected tip resistance (qt) and friction ratio (Rf); the other chart uses qt and pore pressure parameter (Bq) as input data. Five sites in Louisiana were selected for this study. For each site, CPT tests and the corresponding soil boring results were correlated. The soil classification results obtained using the five different CPT soil classification methods were compared.

Discrete element model (DEM) simulations of cone penetration test (CPT) measurements and soil classification

2020 ◽  
Vol 57 (9) ◽  
pp. 1369-1387 ◽  
Author(s):  
A. Khosravi ◽  
A. Martinez ◽  
J.T. DeJong
Keyword(s):  
Soil Classification ◽  
Element Model ◽  
Discrete Element ◽  
Discrete Element Model ◽  
Interparticle Contact ◽  
Cone Penetration ◽  
Dem Simulations ◽  
Tip Resistance ◽  
Contact Parameters ◽  
Insight Into

This paper presents a study on the simulation of cone penetration tests (CPTs) using the discrete element model (DEM) method. This study’s main objective is to investigate the effect of different modeling parameters and simulation configurations on the ability of three-dimensional DEM simulations to replicate realistic CPT tip resistance (qc) and friction sleeve shear stress (fs) measurements. The CPT tests were simulated in virtual calibration chambers (VCCs) containing particles calibrated to model the behavior of sand. The parameters investigated included the granular assembly properties, interparticle contact parameters, particle–probe interface characteristics, and simulation configuration. Results indicate that the interparticle contact parameters, boundary conditions, and void ratio have an important role in the tip resistance and friction sleeve measurements obtained from the simulations. Particle-level interactions such as particle displacements and rotations and interparticle contact forces were analyzed throughout to provide insight into the differences in measured CPT response. Interpretation of the qc and fs measurements using soil behavior type (SBT) charts for soil classification indicates that the simulated CPT response is representative of the response of coarse-grained soils measured during field soundings. Analysis of results within the SBT framework can provide insight into the influence of soil particle properties on CPT-based soil classification.

Probabilistic identification of underground soil stratification using cone penetration tests

2013 ◽  
Vol 50 (7) ◽  
pp. 766-776 ◽  
Author(s):  
Yu Wang ◽  
Kai Huang ◽  
Zijun Cao
Keyword(s):  
Bayesian Model ◽  
Soil Classification ◽  
Cone Penetration ◽  
Bayesian Approaches ◽  
Soil Layers ◽  
Cone Penetration Tests ◽  
Selection Approach ◽  
Model Class ◽  
Model Class Selection ◽  
Penetration Tests

This paper develops Bayesian approaches for underground soil stratum identification and soil classification using cone penetration tests (CPTs). The uncertainty in the CPT-based soil classification using the Robertson chart is modeled explicitly in the Bayesian approaches, and the probability that the soil belongs to one of the nine soil types in the Robertson chart based on a set of CPT data is formulated using the maximum entropy principle. The proposed Bayesian approaches contain two major components: a Bayesian model class selection approach to identify the most probable number of underground soil layers and a Bayesian system identification approach to simultaneously estimate the most probable layer thicknesses and classify the soil types. Equations are derived for the Bayesian approaches, and the proposed approaches are illustrated using a real-life CPT performed at the National Geotechnical Experimentation Site (NGES) at Texas A&M University, USA. It has been shown that the proposed approaches properly identify the underground soil stratification and classify the soil type of each layer. In addition, as the number of model classes increases, the Bayesian model class selection approach identifies the soil layers progressively, starting from the statistically most significant boundary and gradually zooming into less significant ones with improved resolution. Furthermore, it is found that the evolution of the identified soil strata as the model class increases provides additional valuable information for assisting in the interpretation of CPT data in a rational and transparent manner.

Cone penetration test (CPT)-based stratigraphic profiling using the wavelet transform modulus maxima method

2015 ◽  
Vol 52 (12) ◽  
pp. 1993-2007 ◽  
Author(s):  
Jianye Ching ◽  
Jiun-Shiang Wang ◽  
C. Hsein Juang ◽  
Chih-Sheng Ku
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Cone Penetration Test ◽  
Change Points ◽  
Penetration Test ◽  
Soil Behavior ◽  
Cone Penetration ◽  
Soil Layers ◽  
Wavelet Transform Modulus Maxima ◽  
Modulus Maxima

In this paper, a stratigraphic profiling approach is proposed based on the soil behavior type index, Ic, obtained from the cone penetration test (CPT). The basic idea of this approach is simple: the layer boundaries can be identified as the points at which a change occurs in the Ic profile. It is shown that these change points can be easily identified using the wavelet transform modulus maxima (WTMM) method. This method is able to accurately pinpoint the locations of change points in the Ic profile and to produce graphs and plots that fit well with engineers’ methods of visualization and intuition. Moreover, by virtue of the fast Fourier transform, the computation is very fast. Case studies show that the WTMM method is effective for the detection of change points in the Ic profile. It is also capable of detecting thin soil layers.

Interface response-based soil classification framework

2018 ◽  
Vol 55 (12) ◽  
pp. 1795-1811 ◽  
Author(s):  
G.L. Hebeler ◽  
A. Martinez ◽  
J.D. Frost
Keyword(s):  
Soil Structure ◽  
Soil Classification ◽  
Significant Degree ◽  
Classification Systems ◽  
Particle Sizes ◽  
Cone Penetration ◽  
Cone Penetration Testing ◽  
Classification Framework ◽  
Potential Development ◽  
Tip Resistance

Current soil classification systems based on cone penetration testing (CPT) utilize a combination of the tip resistance (qt), pore pressure (u2), and friction sleeve (fs) measurements as inputs. While the qt measurements are typically normalized by the overburden stress, the fs measurements are often normalized by the net tip resistance, leading to the use of parameters that are dependent on each other. This paper presents the development of a soil classification framework that utilizes a normalized multi-friction parameter (MFP) and the CPT normalized tip resistance. The MFP parameter is obtained from measurements with textured friction sleeves from soundings with multi-sleeve attachments. The use of textured friction sleeves allows for fundamental differences in soil–structure interface behavior and particle sizes to be captured due to the significant degree of shearing induced within the soil. This classification framework was developed with results from over 30 soundings at six different sites. The analysis of samples taken from the field indicates that the proposed framework provides a classification that better agrees with the grain-size distribution for residuum, calcareous and intermediate soils, as compared to existing CPT-based systems. The potential development of a simplified probe with just one additional friction sleeve sensor can provide appropriate classification results and would facilitate adoption for use in practice.

Emotion recognition using time–frequency ridges of EEG signals based on multivariate synchrosqueezing transform

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ahmet Mert ◽  
Hasan Huseyin Celik
Keyword(s):  
Emotion Recognition ◽  
Feature Vector ◽  
Dimensional Space ◽  
Computational Cost ◽  
Feature Space ◽  
Maximum Energy ◽  
Machine Learning Algorithms ◽  
Eeg Signals ◽  
Time Frequency ◽  
Synchrosqueezing Transform

Abstract The feasibility of using time–frequency (TF) ridges estimation is investigated on multi-channel electroencephalogram (EEG) signals for emotional recognition. Without decreasing accuracy rate of the valence/arousal recognition, the informative component extraction with low computational cost will be examined using multivariate ridge estimation. The advanced TF representation technique called multivariate synchrosqueezing transform (MSST) is used to obtain well-localized components of multi-channel EEG signals. Maximum-energy components in the 2D TF distribution are determined using TF-ridges estimation to extract instantaneous frequency and instantaneous amplitude, respectively. The statistical values of the estimated ridges are used as a feature vector to the inputs of machine learning algorithms. Thus, component information in multi-channel EEG signals can be captured and compressed into low dimensional space for emotion recognition. Mean and variance values of the five maximum-energy ridges in the MSST based TF distribution are adopted as feature vector. Properties of five TF-ridges in frequency and energy plane (e.g., mean frequency, frequency deviation, mean energy, and energy deviation over time) are computed to obtain 20-dimensional feature space. The proposed method is performed on the DEAP emotional EEG recordings for benchmarking, and the recognition rates are yielded up to 71.55, and 70.02% for high/low arousal, and high/low valence, respectively.

scholarly journals Soil classification at Muaro Baru beach of Padang City using CPT data

2021 ◽  
Vol 331 ◽  
pp. 03005
Author(s):  
Rina Yuliet ◽  
Mas Mera ◽  
Krismon Hidayat
Keyword(s):  
Soil Classification ◽  
Soil Profiles ◽  
Sieve Analysis ◽  
Penetration Test ◽  
Empirical Correlations ◽  
Cone Penetration ◽  
Geotechnical Parameters ◽  
Semi Empirical ◽  
Soil Behaviour ◽  
Behaviour Type

Many semi-empiric correlations have been developed to estimate geotechnical parameters based on Cone Penetration Test (CPT) data for various types of soils. This paper aims to classify soil types based on CPT data with several semi-empiric correlations and compare the results of some of these correlations. In this study, the field CPT and the laboratory test were carried out on soil from two closely spaced locations in the estuary area of Muaro Baru, Padang city. The CPT data was used to determine the soil type using several semi-empirical correlations, namely; friction ratios, Schertmann (1978), Robertson and Campanella (1983), and Robertson et al. (1986), then updated by Robertson in 2010. Soil Behaviour Type (SBT) is based on the cone resistance (qc), sleeve friction (qs), and friction ratio (Rf). The Unified Soil Classification System (USCS) is also used to classify soils using sieve analysis. The results showed that from the several semi-empirical correlations obtained compatibility soil classification and soil profiles.

The Red Sea Metalliferous Sediments

2021 ◽  
Author(s):  
M. Clara Modenesi ◽  
J. Carlos Santamarina
Keyword(s):  
Red Sea ◽  
Environmental Conditions ◽  
Raw Materials ◽  
Soil Classification ◽  
Complete Characterization ◽  
Liquid Limit ◽  
Engineering Properties ◽  
Soil Behavior ◽  
Sediment Formation ◽  
Brine Pools

<p>The demand for metals and raw materials continues to increase as onshore deposits become more depleted. Our oceans contain large unexplored areas that may contain new resources in the form of Mn-nodules, Co-rich crusts, and massive sulfides. A complete characterization and assessment of these deposits are fundamental for the evaluation of resource extraction, separation, and disposal processes.</p><p>The Red Sea holds unique examples of sediment accumulations formed under distinctive environmental conditions. The Atlantis II deep is located in the central part of the Red Sea at 2 km depth and on top of the spreading axis. This deep accumulates sediments that result predominantly from the discharge of hydrothermal fluids into hot and stratified brine pools. The changes in environmental conditions and the hydro-chemical conditions in the brine pool control sediment formation. The accumulations are enriched with metals, such as Ag, Au, Cu, Co, and Zn. The sediments in this deep hold a record of the formation history and their brine pools tell a story about on-going processes.</p><p>On-going research at the Energy Geo-Engineering Laboratory EGEL, KAUST includes (1) Geotechnical index properties (liquid limit, grain size distribution, and specific surface) and consolidation tests to infer engineering properties, (2) Sediment classification based on the Revised Soil Classification System, (3) Geochemistry and mineralogy using XRD, ICP-OES and (4) Microstructure and texture with SEM imaging. An advanced sediment characterization of these fine-grained metalliferous deposits gives a comprehensive understanding of the soil behavior.</p>

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