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Application of machine learning for predicting ground surface settlement beneath road embankments | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 12، Special Issue، اسفند 2021، صفحه 1025-1034 اصل مقاله (365.72 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.5548 | ||
| نویسندگان | ||
| Rufaizal Che Mamat1؛ Azuin Ramli1؛ Mohd Badrul Hafiz Che Omar2؛ Abd Manan Samad3؛ Saiful Aman Sulaiman* 4 | ||
| 1Department of Civil Engineering, Politeknik Ungku Omar,Jalan, Raja Musa Mahadi, 31400 Ipoh, Perak, Malaysia | ||
| 2Department of Surveying Science & Geomatics, Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia | ||
| 3Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia | ||
| 4Malaysia Institute of Transport (MITRANS), Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia | ||
| تاریخ دریافت: 28 خرداد 1400، تاریخ بازنگری: 15 تیر 1400، تاریخ پذیرش: 20 شهریور 1400 | ||
| چکیده | ||
| Predicting the maximum ground surface settlement (MGS) beneath road embankments is crucial for safe operation, particularly on soft foundation soils. Despite having been explored to some extent, this problem still has not been solved due to its inherent complexity and many effective factors. This study applied support vector machines (SVM) and artificial neural networks (ANN) to predict MGS. A total of four kernel functions are used to develop the SVM model, which is linear, polynomial, sigmoid, and Radial Basis Function (RBF). MGS was analysed using the finite element method (FEM) with three dimensionless variables: embankment height, applied surcharge, and side slope. In comparison to the other kernel functions, the Gaussian produced the most accurate results (MARE = 0.048, RMSE = 0.007). The SVM-RBF testing results are compared to those of the ANN presented in this study. As a result, SVM-RBF proved to be better than ANN when predicting MGS. | ||
| کلیدواژه‌ها | ||
| Road embankment؛ Maximum ground surface settlement؛ Support vector machines؛ Kernel functions and artificial neural networks | ||
| مراجع | ||
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