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Evaluation of side and tip resistances for barrette piles using CYCU/Barrette/Side&Tip/64
Journal article   Peer reviewed

Evaluation of side and tip resistances for barrette piles using CYCU/Barrette/Side&Tip/64

Kok-Kwang Phoon, Suneelkumar Laveti, Yit-Jin Chen and Mary Abigail Jos
Soils and foundations, Vol.64(3), p.101477
06/2024

Abstract

Barrette piles Generalized model factor Load test database Side resistance Tip resistance
This study focuses on evaluating the side and tip resistances for barrette piles under compression loading. An extensive dataset from field load tests, designated as CYCU/Barrette/Side&Tip/64, was utilized for analysis. These data were categorized into drained and undrained soils, based on the predominant soil conditions along the pile shaft. In contrast, tip resistance depended on the soil (drained or undrained) or rock condition at the pile tip. Eight interpretation methods were employed to evaluate the measured side and tip resistances of each load test. The predicted side resistance was calculated using the classical α and β methods developed for more common piles such as drilled shafts. For the prediction of tip resistance, end-bearing capacity models for a drilled shaft resting on soil or socketed in rock are considered. Subsequently, a comparison was made between the measured and predicted capacities. Based on these analyses, it was observed that the measured side resistance is the main contributor to the overall capacity of barrette piles. The percentage of measured side resistance ranges from around 80% to 90%. In addition, the predicted side resistance calculated using the α and β methods is smaller than the measured side resistance interpreted using the L2 criterion. To reduce this prediction bias, the adhesion factor (α) and stress factors (K/Ko) for barrette piles were adjusted. For the tip resistance, the trend is opposite – predicted values are larger than the measured values for barrette piles resting on soil or socketed in rock. Another approach to correct for prediction bias called the generalized model factor is presented.
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https://doi.org/10.1016/j.sandf.2024.101477View
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