Robust identification and characterization of thin soil layers in cone penetration data by piecewise layer optimization

Cooper, J. David; Martin, Eileen; Yost, Kaleigh M.; Yerro, Alba; Green, Russell A.

doi:10.1016/j.compgeo.2021.104404

Cited by 4 publications

(11 citation statements)

References 11 publications

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“…Using q m as the input to the procedure, the output (q corr or q inv ) is computed and compared directly to q t . For example, in Figure 10, the performance of the following multiple thin-layer correction procedures is compared: Boulanger and DeJong (2018) inverse procedure (BD18), Cooper et al (2022) inverse procedure (Cea22), and Yost et al (2021) forward ''Deltares'' procedure (DEL21).…”

Section: Assessing Efficacy Of Multiple Thin-layer Correction Proceduresmentioning

confidence: 99%

“…Future work from the authors aims to contribute more data to the database within the framework presented in this article from the results of new laboratory calibration chamber studies being performed at Virginia Tech. In addition, the authors intend to use the database to improve the blurring model used in the Cooper et al (2022) multiple thin-layer correction procedure. Contributions to the database from other researchers are encouraged to develop a more robust set of open data needed for statistical learning techniques and to help standardize the way the efficacy of multiple thin-layer correction procedures is assessed.…”

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

“…Boulanger and DeJong, 2018), or a simplified version of this distribution (i.e. Cooper et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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A cone penetration test database for multiple thin-layer correction procedure development

Yost,

Yerro,

Martin

et al. 2024

Earthquake Spectra

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Cone penetration tests (CPTs) are a commonly used in situ method to characterize soil. The recorded data are used for various applications, including earthquake-induced liquefaction evaluation. However, data recorded at a given depth in a CPT sounding are influenced by the properties of all the soil that falls within the zone of influence around the cone tip rather than only the soil at that particular depth. This causes data to be blurred or averaged in layered zones, a phenomenon referred to as multiple thin-layer effects. Multiple thin-layer effects can result in the inaccurate characterization of the thickness and stiffness of thin, interbedded layers. Correction procedures have been proposed to adjust CPT tip resistance for multiple thin-layer effects, but many procedures become less effective as layer thickness decreases. To compare or improve these procedures and to develop new ones, it is critical to have pairs of measured tip resistance ( q m) and true tip resistance ( q t) data, where q m is the tip resistance recorded by the CPT in a layered profile, and q t represents the tip resistance that would be measured in the profile absent of multiple thin-layer effects. Unfortunately, data sets containing q m and q t pairs are extremely rare. Accordingly, this article presents a unique database containing laboratory and numerically generated CPT data from 49 highly interlayered soil profiles. Both q m and q t are provided for each profile. An accompanying Jupyter notebook is provided to facilitate the use of the data and prepare them for future statistical learning (or other) applications to support multiple thin-layer correction procedure development.

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Section: Assessing Efficacy Of Multiple Thin-layer Correction Proceduresmentioning

confidence: 99%

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

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A cone penetration test database for multiple thin-layer correction procedure development

Yost,

Yerro,

Martin

et al. 2024

Earthquake Spectra

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“…Other authors have pointed out the limitations of the Boulanger and DeJong 13 algorithm 14 and have begun introducing refined algorithms. 15…”

Section: Thin Layer and Transition Zone Effectsmentioning

confidence: 99%

“…Their algorithm tends to increase the tip resistance in stiff layers near the boundaries with softer layers, and to a lesser degree it also decreases the tip resistance in soft layers near the boundaries with stiff layers. Other authors have pointed out the limitations of the Boulanger and DeJong 13 algorithm 14 and have begun introducing refined algorithms 15 …”

Section: Cone Penetration Testmentioning

confidence: 99%

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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Addressing complexities in MPM modeling of calibration chamber cone penetrometer tests in homogenous and highly interlayered soils

Yost

Martinelli

Yerro

et al. 2023

Computers and Geotechnics

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Robust identification and characterization of thin soil layers in cone penetration data by piecewise layer optimization

Cited by 4 publications

References 11 publications

A cone penetration test database for multiple thin-layer correction procedure development

A cone penetration test database for multiple thin-layer correction procedure development

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

Addressing complexities in MPM modeling of calibration chamber cone penetrometer tests in homogenous and highly interlayered soils

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