A new multi-gene genetic programming approach to non-linear system modeling. Part II: geotechnical and earthquake engineering problems

Abstract

Complexity of analysis of geotechnical behavior is due to multivariable dependencies of soil and rock responses. In order to cope with this complex behavior, traditional forms of engineering design solutions are reasonably simplified. Incorporating simplifying assumptions into the development of the traditional methods may lead to very large errors. This paper presents an endeavor to exploit a robust multi-gene genetic programming (MGGP) method for the analysis of geotechnical and earthquake engineering systems. MGGP is a modified genetic programming approach for model structure selection combined with a classical technique for parameter estimation. To justify the abilities of MGGP, it is systematically employed to formulate the complex geotechnical engineering problems. Different classes of the problems analyzed include the assessment of (i) undrained lateral load capacity of piles, (ii) undrained side resistance alpha factor for drilled shafts, (iii) settlement around tunnels, and (iv) soil liquefaction. The validity of the derived models is tested for a part of test results beyond the training data domain. Numerical examples show the superb accuracy, efficiency, and great potential of MGGP. Contrary to artificial neural networks and many other soft computing tools, MGGP provides constitutive prediction equations. The MGG-based solutions are particularly valuable for pre-design practices.

System Identification of Tunnel Response to Ground Motion Considering a Simplified Model

“…Among Heuristic methods, Genetic Algorithms (GA) and GAbased procedures have been employed widely to solve problems in geomechanics, including the estimation of lateral load capacity of piles, undrained side resistance of drilled shafts, tunnelinginduced ground motion settlements, and soil liquefaction (Gandomi and Alavi, 2012). GA are programming schemes for searching a domain space involving a process that mimics natural selection of biological species (Affenzeller et al, 2009); firstly, initial values ("first generation") of parameters are generated randomly.…”

Meteoroloji̇k Veri̇ler Kullanilarak Yeralti Su Sevi̇yesi̇ni̇n Geneti̇k Programlama İle Tahmi̇ni̇

“…Bu üç olayın olasılık değer toplamı bir olmalıdır. ÇGGP algoritmasının operatörleri hakkında detaylı bilgi literatürde bulunmaktadır [11,19,20]. ÇGGP'nin temel akış diyagramı Şekil 4'te verilmiştir.…”

A review of artificial intelligence applications in shallow foundations

“…There are currently three variants of GP available in the literature including the linear genetic programing (LGP), gene expression programing (GEP), and multi-expression programing (MEP) . More recently, the multi-stage genetic programing (MSGP) and multi-gene genetic programing (MGGP) (Gandomi and Alvari, 2012) are also introduced. However, GEP is the most commonly used GP method in geotechnical engineering and is thus described in some detail below.…”