Improving semi-empirical equations of ultimate bearing capacity of shallow foundations using soft computing polynomials

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@Article{Pan:2013:EAAI,

• author = "Chan-Ping Pan and Hsing-Chih Tsai and Yong-Huang Lin",
• title = "Improving semi-empirical equations of ultimate bearing capacity of shallow foundations using soft computing polynomials",
• journal = "Engineering Applications of Artificial Intelligence",
• volume = "26",
• number = "1",
• pages = "478--487",
• year = "2013",
• keywords = "genetic algorithms, genetic programming, Ultimate bearing capacity, Shallow foundations, Semi-empirical equations",
• ISSN = "0952-1976",
• DOI = "doi:10.1016/j.engappai.2012.08.014",
• URL = "http://www.sciencedirect.com/science/article/pii/S0952197612002151",
• abstract = "This study presents the ultimate bearing capacity of shallow foundations in meaningful ways and improves its semi-empirical equations accordingly. Approaches including weighted genetic programming (WGP) and soft computing polynomials (SCP) are used to provide accurate prediction and visible formulae/polynomials for the ultimate bearing capacity. Visible formulas facilitate parameter studies, sensitivity analysis, and applications of pruning techniques. Analytical results demonstrate that the proposed SCP is outstanding in both prediction accuracy and provides simple polynomials as well. Notably, the SCP identifies that the shearing resistance angle and foundation geometry impact on improving the Vesic's semi-empirical equations.",

}

Genetic Programming entries for Chan-Ping Pan Hsing-Chih Tsai Yong-Huang Lin

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