Forecasting of pile plugging using machine learning

Kodsy, Antonio; Ozturk, Baturalp; Iskander, Magued

doi:10.1007/s11440-023-01797-5

Cited by 12 publications

(2 citation statements)

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“…SVR is useful for predicting continuous functions, while SVM is used for classification problems. Thus, SVM has been adopted for a variety of geotechnical classification applications, such as predicting the plugging condition of pipe piles [7] and for sand classification [2], while SVR has been previously employed for computing pile capacity [40].…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

“…Notably, ML was employed for a number of classification tasks, such as classification of sand [2]. The technique has also been introduced for automatic identification and classification of mineral and volcanic ash particles, spatial prediction of shallow landslides, and determining the plugging conditions of piles [3][4][5][6][7]. Application of ML in regression problems includes prediction of the swelling index of cohesive soils, estimating the pilebearing capacity using deep neural networks, and predicting the critical buckling load of I-shaped cellular beams [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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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: Support Vector Regression (Svr)mentioning

confidence: 99%