Simulation of Dilatometer Tests by Neural Networks

Rock rigidity may be experienced in a wide range depending on several factors, and different methods can be used to consider their load-deformation behaviors. In this context, dilatometer tests (DTs) can be applied to obtain the modulus of elasticity of rock masses; therefore, it is possible to evaluate in-situ stress-strain behavior of rock masses realistically. Nevertheless, the application of this test is expensive as well as time-consuming, and necessitates mobilization of the equipment to construction site by trucks. The aim of this study is to simulate the load-deformation curve obtained of DT by neural networks (NNs). Therefore, the dilatometer test can be modeled as well as synthetic simulation of the test enables analyzers to characterize the material behavior. In order to investigate this, 50 different stress-deformation curves are obtained from DTs conducted on limestone formation underlying a dam (Dim dam) construction site in the Southern part of Turkey. The developed database by the curves was used for training and testing of the NN models. The results revealed that NN technique is quite successful for modeling the stress-deformation behavior of specific rocks based on DT results. It is therefore possible with the help of this alternative tool developed for the simulation of DT i) to model DT numerically, ii) to simulate the stress-strain behavior successfully, iii) to calculate the modulus of deformation efficiently, iv) to generate additional DT data synthetically, v) to develop material model alternatively, and vi) to make assumptions on the characterization of the rock mass behavior using previous information gathered by DTs.