Estimation of factor of safety of rooted slope using an evolutionary approach

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@Article{Garg:2014:EE,

• author = "Akhil Garg and Ankit Garg and K. Tai and S. Sreedeep",
• title = "Estimation of factor of safety of rooted slope using an evolutionary approach",
• journal = "Ecological Engineering",
• year = "2014",
• volume = "64",
• pages = "314--324",
• month = mar,
• keywords = "genetic algorithms, genetic programming, FOS prediction, Evolutionary, GPTIPS, LS-SVM, Multi-gene genetic programming",
• ISSN = "0925-8574",
• URL = "http://www.sciencedirect.com/science/article/pii/S0925857413005478",
• DOI = "doi:10.1016/j.ecoleng.2013.12.047",
• size = "11 pages",
• abstract = "Use of roots as one of slope stabilization technique via mechanical reinforcement has received considerable attention in the past few decades. Several mathematical models have been developed to estimate the additional cohesion due to roots, which is useful for the calculation of factor of safety (FOS) of the rooted slopes using finite element method (FEM) or finite difference method. It is well understood from the literature that the root properties such as root area ratio (RAR) and root depth affects the mobilized tensile stress per unit area of soil consequently affecting the FOS of the rooted slope. In addition, a fracture phenomenon also influences the FOS of the rooted slope and should also be considered. In the present work, a new evolutionary approach, namely, multi-gene genetic programming (MGGP) is presented, and, applied to formulate the mathematical relationship between FOS and input variables such as slope angles, root depth and RAR of the rooted slope. The performance of MGGP is compared to those of artificial neural network and support vector regression. Based on the evaluation of the performance of the models, the proposed MGGP model outperformed the two other models and is proved able to capture the characteristics of the FEM model by unveiling important parameters and hidden non-linear relationships.",
• notes = "School of Mechanical and Aerospace Engineering,Nanyang Technological University, 50 Nanyang Ave,Singapore 639798",

}

Genetic Programming entries for Akhil Garg Ankit Garg Kang Tai Sekharan Sreedeep

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