A note on ridge regression modeling techniques

Yahya, Waheed Babatunde; Olaifa, J. B.

doi:10.1285/i20705948v7n2p343

Cited by 3 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ridge Regression is a linear model that is optimized against the correlated features by assigning a penalty term to the weights w in Equation ( 1) [34]. This technique aims to minimize the weights and hence reduce the loss.…”

Section: Ridge Regressionmentioning

confidence: 99%

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

Ozturk

Kodsy²,

Iskander³

2023

Infrastructures

View full text Add to dashboard Cite

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.

show abstract

Section: Ridge Regressionmentioning

confidence: 99%