Che Mamat, Rufaizal, Ramli, Azuin, Che Omar, Mohd Badrul Hafiz, Samad, Abd, Sulaiman, Saiful Aman. (1400). Application of machine learning for predicting ground surface settlement beneath road embankments. هنرهای کاربردی, 12(Special Issue), 1025-1034. doi: 10.22075/ijnaa.2021.5548
Rufaizal Che Mamat; Azuin Ramli; Mohd Badrul Hafiz Che Omar; Abd Manan Samad; Saiful Aman Sulaiman. "Application of machine learning for predicting ground surface settlement beneath road embankments". هنرهای کاربردی, 12, Special Issue, 1400, 1025-1034. doi: 10.22075/ijnaa.2021.5548
Che Mamat, Rufaizal, Ramli, Azuin, Che Omar, Mohd Badrul Hafiz, Samad, Abd, Sulaiman, Saiful Aman. (1400). 'Application of machine learning for predicting ground surface settlement beneath road embankments', هنرهای کاربردی, 12(Special Issue), pp. 1025-1034. doi: 10.22075/ijnaa.2021.5548
Che Mamat, Rufaizal, Ramli, Azuin, Che Omar, Mohd Badrul Hafiz, Samad, Abd, Sulaiman, Saiful Aman. Application of machine learning for predicting ground surface settlement beneath road embankments. هنرهای کاربردی, 1400; 12(Special Issue): 1025-1034. doi: 10.22075/ijnaa.2021.5548

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
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
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