A new design chart for estimating friction angle between soil and pile materials

Aksoy, Hüseyin Suha; Gör, Mesut; İnal, Esen

doi:10.12989/gae.2016.10.3.315

Cited by 28 publications

(16 citation statements)

References 9 publications

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“…The properties of used piles are illustrated in Table 3 and results of interface friction angle between soil and pile are shown in Table 4. The results of interface friction of soil-pile is approximately the same as in proposed chart design [29][30] as mentioned earlier. Figure 5 shows the experimental program proposed for the current study.…”

Section: Model Pilementioning

confidence: 64%

“…Moreover, it found that the L/D ratio, pile spacing, relative density are most importantly effect on group efficiency [28]. Aksoy et al (2016Aksoy et al ( , 2018 proposed a new economic design chart method to estimate the interface friction angle. It found that the conformity between literature and skin friction chart over (90) % in the obtained δ value which make more economic for design piles instead of (δ = 2ϕ/3).…”

Section: Zhang Et Al (2015)mentioning

confidence: 99%

“…It found that the conformity between literature and skin friction chart over (90) % in the obtained δ value which make more economic for design piles instead of (δ = 2ϕ/3). Figure 1 shows the proposed chart to determine the interface friction angle value for steel and FRP (Fiber-Reinforced Polymer) Piles [29][30].…”

Section: Zhang Et Al (2015)mentioning

confidence: 99%

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Effect of Pile Spacing on Group Efficiency in Gypseous Soil

2019

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As a matter of fact, the gypseous soil is usually considered as collapsible soil, such type of soil illustrates high resistance to settlement and high bearing capacity when it is dry, but it loses these characteristics when it is inundated and collapses excessively because of the sudden decrease in the volume of the surrounding soil mass. It is founded in the arid and semi-arid regions of the world in Asia, South Asia (Iraq, Syria, Jordan, Yemen, and Iran), North Africa, North America, moreover, it covers more than (31%) of the surface area in Iraq. Gypseous soil is one of the most difficult problems facing the process of building any project because of the difficulty of preventing leakage of water to the soil in practice. Deep foundation (piles) are one of the most common types used in collapsible soils which penetrating problematic soil layers and reaching more hard ones (end bearing piles) or transfers loads depending on skin friction (floating pile). The current work is directed to study the behavior of single and group driven pile of square pattern (4 piles) in case of floating pile (friction pile) with different spacing (2D, 4D, 6D) and length to diameter (L/D) ratio of (20) in this special medium dense soil (gypsum content 30% and 61%) under axial load condition. The investigation was carried out to measure the soil collapse before and after inundation. The results showed that the group efficiency for spacing 2D is less than one while for spacing 4D and 6D are more than that value. In addition, the spacing 4D was more efficient to carry 4 group pile in both dry and soaked cases, in addition, the result showed a high reduction in the bearing capacity at inundation state of group pile of (82% in gypsum content 30%) and ( 87% in gypsum content 61%) with respect to dry state.

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Section: Model Pilementioning

confidence: 64%

Section: Zhang Et Al (2015)mentioning

confidence: 99%

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Effect of Pile Spacing on Group Efficiency in Gypseous Soil

2019

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“…For realizing the best artificial intelligence (AI) model to meet this goal, this study provides and compares five well-known models that are widely used by researchers [4][5][6][7][8]. Similar to other research in the fields of science and technology, AI techniques have widespread application in order to put forward reasonable evaluation in many engineering problems [9][10][11][12][13][14][15][16][17] of the energy consumption in buildings. In numerous types of artificial intelligence-based solutions, artificial neural network (ANN) is known as a recognized method that is largely employed for many prediction-based examples [18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques

et al. 2019

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The heating load calculation is the first step of the iterative heating, ventilation, and air conditioning (HVAC) design procedure. In this study, we employed six machine learning techniques, namely multi-layer perceptron regressor (MLPr), lazy locally weighted learning (LLWL), alternating model tree (AMT), random forest (RF), ElasticNet (ENet), and radial basis function regression (RBFr) for the problem of designing energy-efficient buildings. After that, these approaches were used to specify a relationship among the parameters of input and output in terms of the energy performance of buildings. The calculated outcomes for datasets from each of the above-mentioned models were analyzed based on various known statistical indexes like root relative squared error (RRSE), root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R2), and relative absolute error (RAE). It was found that between the discussed machine learning-based solutions of MLPr, LLWL, AMT, RF, ENet, and RBFr, the RF was nominated as the most appropriate predictive network. The RF network outcomes determined the R2, MAE, RMSE, RAE, and RRSE for the training dataset to be 0.9997, 0.19, 0.2399, 2.078, and 2.3795, respectively. The RF network outcomes determined the R2, MAE, RMSE, RAE, and RRSE for the testing dataset to be 0.9989, 0.3385, 0.4649, 3.6813, and 4.5995, respectively. These results show the superiority of the presented RF model in estimation of early heating load in energy-efficient buildings.

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“…In the comparison of the provided solution outcome to the theoretical calculation one, the theoretical method results revealed that the elastic-plastic analytical solutions is a good method. Aksoy et al [39] developed a new chart to estimate the friction angle between the pile and soil materials. Accordingly, in the current research, soil including different internal friction angles (φ) has been initially provided, then the skin friction angles (delta) of the mentioned soils with fiber-reinforced plastic (FRP) like a composite material, wood (pine), and steel (st37) have been defined by undertaking the interface shear test to provide a pile design diagram to determine skin friction angles of the soils and pile materials.…”

Section: Introductionmentioning

confidence: 99%

Determination of Young Elasticity Modulus in Bored Piles Through the Global Strain Extensometer Sensors and Real-Time Monitoring Data

et al. 2019

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For friction piles depending on the friction resistance, accurate prediction of unit skin friction around the pile shaft is the dominant resistance to measure the final bearing capacity of a bored-pile. The present study measures the stress-strain transferring in two instrumented bored-piles (BP #1 & BP# 2) embedded within the soil layer in Kuala Lumpur by real-time monitoring global strain extensometer (GSE) sensors. Two bored-piles (i.e., having 1.80 m and 1.0 m diameters, as well as 36 m and 32 m lengths) have been loaded with two times to their design working loads. Extensive data are analyzed to measure the changes in stress-strain in the bored-pile. The effect of loading and unloading stages on the pile's head and base settlement has been monitored, indicating that Young modulus of elasticity in concrete bored-pile (E c ), average strain, and unit skin friction changed along the bored-pile based on the ground site conditions and stress registered. One example of two case studies with great real-time monitoring data has been provided to further design.

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A new design chart for estimating friction angle between soil and pile materials

Cited by 28 publications

References 9 publications

Effect of Pile Spacing on Group Efficiency in Gypseous Soil

Effect of Pile Spacing on Group Efficiency in Gypseous Soil

Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques

Determination of Young Elasticity Modulus in Bored Piles Through the Global Strain Extensometer Sensors and Real-Time Monitoring Data

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