Efficacy of Several Design Methods for Predicting the Axial Compressive Capacity of Piles

Ozturk, Baturalp; Kodsy, Antonio; Bazi, Youssef; Iskander, Magued

doi:10.1177/03611981231158335

Cited by 4 publications

(1 citation statement)

References 27 publications

(58 reference statements)

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“…It also exhibited the lowest MAE and MAPE of 771.25 and 0.51, respectively. This is consistent with the finding of previous studies that investigated the performance of the design method for LDOEPs and for other pile types [26,44]. Although the Revised Lambda was superior in comparison to the other methods, its mean absolute percentage error of 51% and a standard deviation of 0.72 suggests the method still has flaws.…”

Section: Performance Of Traditional Design Methodssupporting

confidence: 90%

Forecasting the Capacity of Open-Ended Pipe Piles Using Machine Learning

Ozturk

Kodsy²,

Iskander³

2023

Infrastructures

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Pile design is an essential component of geotechnical engineering practice, and pipe piles, in particular, are increasingly being used for the support of a variety of infrastructure projects. These piles are being used with dimensions that exceed those used in the development of the most widely used design approaches. At the same time, the growth in pile dimensions calls for the evolution of the state-of-the-art at a similar pace. The objective of this study is to provide an improved prediction of pile capacity. A database of 112 load tests on pipe piles ranging in diameter from 10 to 100 in. (0.25–2.5 m) and in length from 10 to 320 ft. (3–98 m) was employed in this study. First, design capacities were computed using four popular design methods and compared to capacities interpreted from a load test. For the employed dataset, the Revised Lambda method was found to best predict capacities of pipe piles obtained from a load test, among the four examined methods, and was thus employed as a reference standard for assessing the performance of ML methods. Next, eight ML regression models were trained to compute the capacity of pipe piles. Several trained ML models predicted capacities for the testing data set on par with the Revised Lambda method, and three were selected for further investigation. A variety of pile dimensions and soil properties were examined as input properties for ML and the trained models performed surprisingly well with only the pile dimensions used as input. In addition, ML models exhibited satisfactory diameter and length effects, which have been areas of concern for some traditional design approaches. The work thus demonstrates the feasibility of employing machine learning (ML) for determining the capacity of pipe piles. A web application was also developed as a tool for forecasting the capacity of pipe piles using ML.

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Section: Performance Of Traditional Design Methodssupporting

confidence: 90%

Forecasting the Capacity of Open-Ended Pipe Piles Using Machine Learning

Ozturk

Kodsy²,

Iskander³

2023

Infrastructures

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Forecasting the Bearing Capacity of Open-Ended Pipe Piles Using Machine Learning Ensemble Methods

Ozturk,

Kodsy,

Iskander

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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Efficacy of Several Design Methods for Predicting the Axial Compressive Capacity of Piles

Cited by 4 publications

References 27 publications

Forecasting the Capacity of Open-Ended Pipe Piles Using Machine Learning

Forecasting the Capacity of Open-Ended Pipe Piles Using Machine Learning

Forecasting the Bearing Capacity of Open-Ended Pipe Piles Using Machine Learning Ensemble Methods

Using Machine Learning to Predict Axial Pile Capacity

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