Study on pile ultimate capacity criteria and CPT-based direct methods

Moshfeghi, Sara; Eslami, Abolfazl

doi:10.1080/19386362.2016.1244150

Cited by 26 publications

(3 citation statements)

References 15 publications

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“…To assess and contrast six approaches to measure pile‐bearing capacity using CPT and Piezocone (CPTu), Eslami and Fellenius (1997) compiled a database of 102 case records. The records of 600 pile loading tests and results of the cone or piezocone penetration tests were included in the database Amirkabir University of Technology (AUT): Geo‐CPT&Pile (Moshfeghi & Eslami, 2016).…”

Section: Database Of Cpt and Miniconementioning

confidence: 99%

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

Shirani,

Eslami,

Ebrahimipour

et al. 2023

Deep Underground Science and Engineering

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The 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.

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Section: Database Of Cpt and Miniconementioning

confidence: 99%

“…There are several loading definitions for ultimate capacity established by CPT-based approaches. The results of the computed capacity by the chosen methods may be compared on the basis of various criteria to analyze the impact of the interpretation criterion, which is used to define the pile capacity (Moshfeghi & Eslami, 2016).…”

Section: Ultimate Loadmentioning

confidence: 99%

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

Shirani,

Eslami,

Ebrahimipour

et al. 2023

Deep Underground Science and Engineering

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“…However, these pile design methods neglected the potentially existing variation of the in-situ soil properties along the pile shaft (inherent vertical soil variability) (Refs. [5,[29][30][31][32]).…”

Section: Introductionmentioning

confidence: 99%

A Parametric Numerical Study for Diagnosing the Failure of Large Diameter Bored Piles Using Supervised Machine Learning Approach

2021

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The full-scale static pile loading test is without question the most reliable methodology for estimating the ultimate capacity of large diameter bored piles (LDBP). However, in most cases, the obtained load-settlement curves from LDBP loading tests tend to increase without reaching the failure point or an asymptote. Loading an LDBP until reaching apparent failure is seldom practical because of the significant amount of settlement usually required for the full shaft and base mobilizations. With that in mind, the supervised learning algorithm requires a huge labeled data set to train the machine properly, which makes it ideal for sensitivity analysis, forecasting, and predictions, among other unsupervised algorithms. However, providing such a huge dataset of LDBP loaded to failure tests might be very complicated. In this paper, a novel practice has been proposed to establish a labeled dataset needed to train supervised machine learning algorithms on accurately predicting the ultimate capacity of an LDBP. A comprehensive numerical parametric study was carried out to investigate the effect of both pile geometrical and soil geotechnical parameters on both the ultimate capacity and settlement of an LDBP. This study was based on field measurements of loaded to failure LDBP tests. Results of the 29 applied models were compared with the calibrated model results, and the variation in LDBP behavior due to change in any of the hyperparameters was discussed. Accordingly, three primary characteristics were identified to diagnose the failure of LDBPs. Those characteristics were utilized to establish a decision tree of a supervised machine learning algorithm that can be used to predict the ultimate capacity of an LDBP.

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