Evaluation of Several Design Methods for Calculating Axial Compression Capacity of Large Diameter Open-Ended Piles

Rizk, Andrew; Machairas, Nikolaos; Iskander, Magued

doi:10.1061/9780784483404.009

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…It was observed that the unplugged condition had superior performance compared to the other plugging conditions. This was further investigated and statistically proven for large diameter open-ended piles in the analysis presented by Rizk et al [49].…”

Section: Identification Of Plugging Based On Q C /Q Mmentioning

confidence: 98%

“…Available geotechnical design parameters were employed to compute Qc where possible, but many pile cases lacked sufficient measurements, so the team used empirical relationships from established guidelines such as those provided by The Naval Facilities Engineering Command Design Manual 7.01 [33] and Peck et al [34] to supplement laboratory data. More details about data handling are available in Rizk et al [35]. Missing or misinterpreted data is one of the dominant issues when dealing with geotechnical databases.…”

Section: Load Test Datamentioning

confidence: 99%

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Insights into Plugging of Pipe Piles Based on Pile Dimensions

Kodsy

Iskander

2022

Applied Sciences

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Preliminary identification of plugging of open-ended pipe piles based on their dimensions, ahead of driving, is explored in this study using data analytics. Piles can be unplugged, plugged, or internally plugged, depending on their dimensions, and geotechnical conditions. Plugging of pipe piles influences both pile capacity and driving behavior; however, the classification assumed at the design time does not always manifest during driving, sometimes resulting in driving difficulties. The relationship between pile plugging and pile dimensions was investigated using a dataset of 74 load tests on pipe piles, where geotechnical profiles were also available. An analytics approach borrowed from data science was adopted. First, capacity was computed using four recognized designed methods considering the unplugged, plugged, and internally plugged conditions. Next, the calculated capacities were compared to capacities measured (interpreted) from static load tests. Finally, voting was employed to identify plugging based on the closeness of the computed capacity assumptions to the interpreted capacity. Most piles were found to be unplugged. A diameter criterion is proposed as a tool to give early insight into the plugging condition of a pile ahead of driving which resulted in a 70 ± 10% accuracy. The proposed criterion was validated once using a dataset of 23 piles with CPT data and a second time using 24 published driving records where plugging records were available and achieved similar accuracy, in both cases. It was concluded that piles larger than ~0.9 m (36 inches) in diameter have a higher likelihood of being unplugged, while piles smaller than 0.5 m (20 inches) have a higher likelihood of being plugged.

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Section: Identification Of Plugging Based On Q C /Q Mmentioning

confidence: 98%

Section: Load Test Datamentioning

confidence: 99%

Insights into Plugging of Pipe Piles Based on Pile Dimensions

Kodsy

Iskander

2022

Applied Sciences

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Evaluating Pile Plugging in Concrete Large Diameter Open-End Pipe Piles

Thurmond,

Sanders

2024

Ifcee 2024

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Using Machine Learning to Predict Axial Pile Capacity

Ozturk,

Kodsy,

Iskander

2024

Transportation Research Record: Journal of the Transportation R

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Accurate estimation of the ultimate axial load bearing capacity of piles is necessary to ensure the safety of the supported structures and to prevent cost overruns. Traditional mechanics-based design methods do not always predict pile capacity accurately, or precisely, leaving room for improvement. This study focuses on the potential of machine learning (ML) in estimating pile capacity. A dataset of 546 load tests was compiled from three databases. The baseline performance of traditional design methods was first established by comparing the capacities computed using four traditional approaches, against the capacities interpreted from load tests using Davisson’s criterion. Sixteen different ML techniques were explored. First, the optimal feature selection technique for model training was investigated. Second, hyperparameters of each technique were optimized. The process involved the training of 32,000 different models and tuning their hyperparameters. Next the dataset was randomly split into training (70%) and testing (30%) for comparing the 16 different ML regression models. Each of the optimized models was then trained using six feature sets. The performance of each of the 16 ML models with the best performing feature subset was compared with the baseline performance from traditional methods. Evaluation criteria included measured versus predicted capacities, influence of soil type on accuracy, as well as the absence of pile diameter, or length effects on accuracy and precision. In general, the ML methods performed significantly better than the best traditional method. The current research demonstrated that ML may offer advantages in geotechnical design when large datasets are available.

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Evaluation of Several Design Methods for Calculating Axial Compression Capacity of Large Diameter Open-Ended Piles

Cited by 3 publications

References 6 publications

Insights into Plugging of Pipe Piles Based on Pile Dimensions

Insights into Plugging of Pipe Piles Based on Pile Dimensions

Evaluating Pile Plugging in Concrete Large Diameter Open-End Pipe Piles

Using Machine Learning to Predict Axial Pile Capacity

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