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

Ching, Jianye; Wang, Jiun- Shiang; Juang, C. Hsein; Ku, Chih Sheng

doi:10.1139/cgj-2015-0027

Cited by 64 publications

(21 citation statements)

References 16 publications

(16 reference statements)

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“…The profile was in accordance with the WTMM stratification approach proposed by Ching et al [13]. Another benefit of using the Lukang CPT stratification for training the model was that it contained both thick and thin layers and also low and highly fluctuated F r and Q tn data sections.…”

Section: Applying the Developed Model To Case Studiessupporting

confidence: 69%

“…7 Comparison of stratification profiles by the proposed model and experts, i.e. WTMM method [13], for Lukang case, Taiwan…”

Section: Loess Regression Bandwidth's Impactmentioning

confidence: 99%

“…The proposed model was evaluated based on some real CPT data. Ching et al [13] used the SBT index, I c , in their proposed stratigraphic profiling approach. The layer boundaries were recognized at the relatively large change points in the I c profile.…”

Section: Introductionmentioning

confidence: 99%

“…In the first approach, the consistency among the sequential CPT data points has been employed to find the thick layers, such as in the Bayesian theory and clustering models. On the other hand, in the second approach, the boundaries between the soil layers are determined based on the change points and especially sudden fluctuations detections in CPT results, such as in the wavelet model proposed by Ching et al [13]. There are still some deficiencies with the existing methods, although great progress has been made so far in stratification models.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Development of an integrated game theory-optimization subground stratification model using cone penetration test (CPT) measurements

Farhadi

Länsivaara

2021

Engineering with Computers

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The continuous cone penetration test (CPT) measurements provide an advantageous liable rapid tool for stratification and soil behavior classification that can be employed in the sustainable design of the infrastructures. However, the CPT measurements are often interpreted by geotechnical experts because of the involved complexities and uncertainties. In this study, a novel stratification and soil type behavior (SBT) classification model is developed to identify the transition and thicker layers by integrating the geotechnical knowledge with the three submodels of (a) locally estimated scatterplot smoothing (LOESS), (b) a game theory model known as Nash–Harsanyi (N–H) bargaining, and (c) grey wolf optimizer (GWO). The LOESS and integrated N–H bargaining-GWO models are, respectively, used to approximate the outliers in CPT measurements and identify the SBT and layer changes. Attractively, in the proposed model, the engineer has the opportunity to judge on the precision of the stratification profile regarding their own preferences in a project. Solving simple algebraic equations, high speed, identifying thick and the interlayer transition layers, and small required training data are the other advantages of the developed model. Finally, the applicability of the proposed model has been assessed in an example. The compared estimated and two other models’ stratification profiles highlighted the potential of the proposed model to identify thin transition layers.

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Section: Applying the Developed Model To Case Studiessupporting

confidence: 69%

“…7 Comparison of stratification profiles by the proposed model and experts, i.e. WTMM method [13], for Lukang case, Taiwan…”

Section: Loess Regression Bandwidth's Impactmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development of an integrated game theory-optimization subground stratification model using cone penetration test (CPT) measurements

Farhadi

Länsivaara

2021

Engineering with Computers

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“…kriging, which is point estimation, and hence the lithological uncertainty cannot be quantified. Recently, probabilistic approaches have been developed to determine underground soil stratification based on cone penetration test (CPT) data (Cao and Wang, 2012;Ching et al, 2015; and to identify soil strata in London…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Quantifying stratigraphic uncertainties by stochastic simulation techniques based on Markov random field

Zhao

Wang

et al. 2016

Engineering Geology

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Unsupervised machine learning for detecting soil layer boundaries from cone penetration test data

Hudson

Ulmer

Zimmaro

et al. 2023

Earthq Engng Struct Dyn

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Cone penetration test (CPT) data contains detailed stratigraphic information that is useful in a wide variety of applications. Separating a CPT profile into discrete layers is an important part of many analyses such as critical layer selection in liquefaction triggering analysis, effective stress seismic ground response analysis, analysis of pile shaft and tip resistance, and soil‐pile interaction analysis. The discretization of the profile into layers is often done manually, relying on the judgment of the analyst. This manual approach is cumbersome for datasets that include large numbers of CPT profiles (such as the Next Generation Liquefaction [NGL] database and the New Zealand Geotechnical Database) and it may not be consistent or repeatable because different analysts may discretize a given CPT log in different ways. To overcome these difficulties, we present an approach to automatically divide a CPT profile into discrete layers. Automated layer detection is performed using an unsupervised machine learning technique called agglomerative clustering in combination with two cost functions to identify an optimal number of layers. The algorithm is illustrated using CPT profiles from the NGL database, where the approach is being used in the development of liquefaction triggering and manifestation models. Although the algorithm shows promise for replicating our judgment regarding layering, we recommend visual review of the layering produced by the algorithm to check for reasonableness given the site geology and intended use of the CPT data.

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

Cited by 64 publications

References 16 publications

Development of an integrated game theory-optimization subground stratification model using cone penetration test (CPT) measurements

Development of an integrated game theory-optimization subground stratification model using cone penetration test (CPT) measurements

Quantifying stratigraphic uncertainties by stochastic simulation techniques based on Markov random field

Unsupervised machine learning for detecting soil layer boundaries from cone penetration test data

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