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- MakaleYayıncıda açık erişim2016Modeling deformation modulus of a stratified sedimentary rock mass using neural network, fuzzy inference and genetic programming
Alemdag, S.; Gurocak, Z.; Cevik, A.; Cabalar, A.F.; Gokceoglu, C.
This paper investigates a series of experimental results and numerical simulations employed to estimate the deformation modulus of a stratified rock mass. The deformation modulus of rock mass has a significant importance for some applications in engineering geology and geotechnical projects including foundation, slope, and tunnel designs. Deformation modulus of a rock mass can be determined using large scale in-situ tests. This large scale sophisticated in-situ testing equipments are sometimes difficult to install, plus time consuming to be employed in the field. Therefore, this study aims to estimate indirectly the deformation modulus values via empirical methods such as the neural network, neuro fuzzy and genetic programming approaches. A series of analyses have been developed for correlating various relationships between the deformation modulus of rock mass, rock mass rating, rock quality designation, uniaxial compressive strength, and elasticity modulus of intact rock parameters. The performance capacities of proposed models are assessed and found as quite satisfactory. At the completion of a comparative study on the accuracy of models, in the results, it is seen that overall genetic programming models yielded more precise results than neural network and neuro fuzzy models.
- MakaleAçık Erişim2015A simple regression based approach to estimate deformation modulus of rock masses
Alemdag, Selcuk; Gurocak, Zulfu; Gokceoglu, Candan
Rock mass characterization has a crucial importance in rock engineering applications including foundation design, slope design, support design and others. For these purposes, it is necessary to determine some input parameters for design, such as deformation modulus (Em), uniaxial compressive strength (σci) and Poisson ratio (υ). The Em is an important input parameter and the practical and economical way to determine this parameter is to apply a rock mass classification system and estimate using the rock mass properties. Data from four different sites were used in the study. The values of Em were calculated using the existing empirical equations based on the Rock Mass Rating (RMR89) system. Furthermore, pressuremeter tests were carried out to determine the Em in one of the sites. The relationship between the RMR89 and the Em was obtained via simple regression analysis using the Em calculated from empirical equations based on RMR89 system. Afterwards, performance of the equation obtained from regression analyses was examined and compared the predicted Em values with experimental values with respect to the R2 values using the data of fourth site. Overall R2 value was found to be as 0.75.