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Geomechanics and Engineering Volume 36, Number 4, February25 2024 , pages 381-390 DOI: https://doi.org/10.12989/gae.2024.36.4.381 |
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Machine learning-based analysis and prediction model on the strengthening mechanism of biopolymer-based soil treatment |
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Haejin Lee, Jaemin Lee, Seunghwa Ryu and Ilhan Chang
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| Abstract | ||
| The introduction of bio-based materials has been recommended in the geotechnical engineering field to reduce environmental pollutants such as heavy metals and greenhouse gases. However, bio-treated soil methods face limitations in field application due to short research periods and insufficient verification of engineering performance, especially when compared to conventional materials like cement. Therefore, this study aimed to develop a machine learning model for predicting the unconfined compressive strength, a representative soil property, of biopolymer-based soil treatment (BPST). Four machine learning algorithms were compared to determine a suitable model, including linear regression (LR), support vector regression (SVR), random forest (RF), and neural network (NN). Except for LR, the SVR, RF, and NN algorithms exhibited high predictive performance with an R2 value of 0.98 or higher. The permutation feature importance technique was used to identify the main factors affecting the strength enhancement of BPST. The results indicated that the unconfined compressive strength of BPST is affected by mean particle size, followed by biopolymer content and water content. With a reliable prediction model, the proposed model can present guidelines prior to laboratory testing and field application, thereby saving a significant amount of time and money. | ||
| Key Words | ||
| biopolymer-based soil treatment (BPST); machine learning; neural network; random forest; support vector regression; unconfined compressive strength | ||
| Address | ||
| Haejin Lee and Ilhan Chang: Department of Civil Systems Engineering, Ajou University, Republic of Korea, 16449 Jaemin Lee and Seunghwa Ryu: Department of Mechanical Engineering, Korea Advance Institute of Science and Technology, Republic of Korea, 34141 | ||