Skin friction on large-diameter piles socketed into rock

Carrubba, Paolo

doi:10.1139/t96-104

Cited by 77 publications

(15 citation statements)

References 6 publications

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“…The calculated results and the test data are presented in Figure 7, for the purposes of comparison. Figures 6 and 7 it can be seen that the load-settlement curves for the two piles calculated using the proposed method are very close to those obtained by Carrubba (1997), using backward analysis based on his measurements. Therefore, it verifies the correctness and accuracy of the proposed method.…”

Section: Examplesupporting

confidence: 62%

“…Using Equations ((22)-(36)) we can plot the relationship curve between Q 0 and s 0 . Carrubba (1997) conducted a friction analysis of large-diameter piles by using real measurements to obtain the load-settlement curves, combined with numerical simulation to determine the side friction force of piles. This paper will use the original data of the two piles presented in Carrubba's work, using the proposed method to calculate the load-settlement curves, and will then compare the predictions with the test data to verify the accuracy of the proposed method.…”

Section: The Application Of the Proposed Formulamentioning

confidence: 99%

“…The diameter of the pile considered by Carrubba (1997) is 1Á2 m, the length of the pile is 18Á5 m; pile depth in the soil is 11 m and in the rock is 7Á5 m. The load transfer function coefficients are: in soil, 1/ a s = 25 MN m À3 , 1/b s = 0Á08 MPa; in rock, 1/a r = 100 MN m À3 , 1/b r = 0Á14 MPa; at the end of the pile, 1/a b = 220 MN m À3 , 1/b b = 5Á3 MPa; the Young's modulus E = 31500 MPa. The results calculated by the proposed method and the test data obtained by Carrubba (1997) are presented in Figure 6 for comparison purposes.…”

Section: Examplementioning

confidence: 99%

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Prediction of load–settlement relationship for large‐diameter piles

Xiao

Luo

Tang

et al. 2002

Struct. Design Tall Build.

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An analytical solution for the bearing capacity of a large‐diameter pile is proposed based on the load transfer function in this paper. The load–settlement relationship or the P–S curve of such a pile can be determined by the proposed method for practical application. Comparison is made between the results obtained by the proposed method and the test data, and close agreement has been found, thus verifying the accuracy of the proposed method. It is noted that the most reliable approach to the determination of the vertical load capacity of a pile is to use the P–S curve, which is usually obtained through on‐site load test. However, for a large‐diameter pile (e.g. a diameter over 3 m), this kind of test requires a thousand tons of test load, which sometimes is impossible to provide. It is expected that the proposed method can be used in this kind of situation. Copyright © 2002 John Wiley & Sons, Ltd.

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Section: Examplesupporting

confidence: 62%

Section: The Application Of the Proposed Formulamentioning

confidence: 99%

Section: Examplementioning

confidence: 99%

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Prediction of load–settlement relationship for large‐diameter piles

Xiao

Luo

Tang

et al. 2002

Struct. Design Tall Build.

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“…of 0.34 MPa were in the range of 13 ∘ ∼53 ∘ and 0.1∼2.5 MPa which were collected and sorted from the natural soft rocks in the literature published previously [5][6][7][8][9][10][11][12][13][14][15].…”

Section: Triaxial Compressivementioning

confidence: 99%

A Synthetic Material to Simulate Soft Rocks and Its Applications for Model Studies of Socketed Piles

Mei

Fang²,

Luo

et al. 2017

Advances in Materials Science and Engineering

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A detailed manufacturing procedure of a synthetic soft rock is presented, as well as its applications on the laboratory experiments of socketed piles. With the homogeneity and isotropy of the simulated soft rock, the influence of different variables on the bearing performance could be investigated independently. The constituents, cement, gypsum powder, river sand, concrete-hardening accelerator, and water, were mixed to form the specimens. Both uniaxial and triaxial compressive tests were conducted to investigate the stress-strain behavior of the simulated soft rock. Additionally, the simulated soft rock specimens were used in model pile tests and simple shear tests of the pile-rock interface. Results of the simulated soft rock in both the uniaxial and triaxial compressive tests are consistent with those of natural soft rocks. The concrete-hardening accelerator added to the mixtures improves the efficiency in laboratory investigations of soft rock specimens with a curing time of 7 days. The similarities between the laboratory tests and the field observations provide convincing evidence to support its suitability in modeling the behavior of soft rocks.

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“…Structural loads are carried by rock-socketed piles via their end bearing, or their shaft resistance, or a mixture of both. As stated by Carrubba [1], the application of drilled piles socketed into rock is among the best alternatives when bedrock is overlain by layers of loose soil at shallow depths. In such circumstances, shaft resistance in rock can ensure considerable bearing capacity, even with small pile dislocations.…”

Section: Introductionmentioning

confidence: 99%

On the Use of Neuro-Swarm System to Forecast the Pile Settlement

et al. 2020

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In civil engineering applications, piles (deep foundations) are pushed into the ground in order to perform as steady support of structures. As these type of foundations are able to carry a huge amount of load, they should be carefully designed in terms of their settlement. Therefore, the control and estimation of settlement is a significant issue in pilling design and construction. The objective of the present study is to introduce a modeling process of a hybrid intelligence system namely neural network optimized by particle swarm optimization (neuro-swarm) for estimation of pile settlement. To do that, properties results of several piles socketed into rock mass together with their settlements were considered as established databased to propose neuro-swarm model. Then, several sensitivity analyses were carried out to determine the most influential particle swarm optimization parameters for pile settlement prediction. Eventually, five neuro-swarm models were constructed to understand the behavior of this hybrid model on them in pile settlement prediction. As a result, according to results of five performance indices, dataset number 4 showed the highest prediction capacity among all five datasets. The coefficient of determination (R2) and system error values of (0.851 and 0.079) and (0.892 and 0.099) were obtained respectively for train and test stages of the best neuro-swarm model which reveal the capability level of this hybrid model in predicting pile settlement. The modeling process introduced in this study can be useful for the researchers who are interested to work on the same hybrid technique.

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Skin friction on large-diameter piles socketed into rock

Cited by 77 publications

References 6 publications

Prediction of load–settlement relationship for large‐diameter piles

Prediction of load–settlement relationship for large‐diameter piles

A Synthetic Material to Simulate Soft Rocks and Its Applications for Model Studies of Socketed Piles

On the Use of Neuro-Swarm System to Forecast the Pile Settlement

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