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International Journal of Nonlinear Analysis and Applications، جلد ۱۲، شماره Special Issue، صفحات ۱۰۲۵-۱۰۳۴
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عنوان انگلیسی Application of machine learning for predicting ground surface settlement beneath road embankments
چکیده انگلیسی مقاله 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.
کلیدواژه‌های انگلیسی مقاله Road embankment, Maximum ground surface settlement, Support vector machines, Kernel functions and artificial neural networks
نویسندگان مقاله Rufaizal Che Mamat |
Department of Civil Engineering, Politeknik Ungku Omar,Jalan, Raja Musa Mahadi, 31400 Ipoh, Perak, Malaysia

Azuin Ramli |
Department of Civil Engineering, Politeknik Ungku Omar,Jalan, Raja Musa Mahadi, 31400 Ipoh, Perak, Malaysia

Mohd Badrul Hafiz Che Omar |
Department of Surveying Science & Geomatics, Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia

Abd Manan Samad |
Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia

Saiful Aman Sulaiman |
Malaysia Institute of Transport (MITRANS), Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia

نشانی اینترنتی https://ijnaa.semnan.ac.ir/article_5548_a429f7e81b923f0be5c3586af298b7a5.pdf
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