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http://dx.doi.org/10.9720/kseg.2022.2.221

A Prediction of N-value Using Regression Analysis Based on Data Augmentation  

Kim, Kwang Myung (Execution Risk Management Team, Hyundai Engineering Co., Ltd.)
Park, Hyoung June (Execution Risk Management Team, Hyundai Engineering Co., Ltd.)
Lee, Jae Beom (Execution Risk Management Team, Hyundai Engineering Co., Ltd.)
Park, Chan Jin (Execution Risk Management Team, Hyundai Engineering Co., Ltd.)
Publication Information
The Journal of Engineering Geology / v.32, no.2, 2022 , pp. 221-239 More about this Journal
Abstract
Unknown geotechnical characteristics are key challenges in the design of piles for the plant, civil and building works. Although the N-values which were read through the standard penetration test are important, those N-values of the whole area are not likely acquired in common practice. In this study, the N-value is predicted by means of regression analysis with artificial intelligence (AI). Big data is important to improve learning performance of AI, so circular augmentation method is applied to build up the big data at the current study. The optimal model was chosen among applied AI algorithms, such as artificial neural network, decision tree and auto machine learning. To select optimal model among the above three AI algorithms is to minimize the margin of error. To evaluate the method, actual data and predicted data of six performed projects in Poland, Indonesia and Malaysia were compared. As a result of this study, the AI prediction of this method is proven to be reliable. Therefore, it is realized that the geotechnical characteristics of non-boring points were predictable and the optimal arrangement of structure could be achieved utilizing three dimensional N-value distribution map.
Keywords
regression analysis; N-value; artificial intelligence; circular augmentation method; artificial neural network; decision tree; automatic machine learning;
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