Next Article in Journal
Effect of Abutment and Implant Shapes on Stresses in Dental Applications Using Fem
Previous Article in Journal
Differential Quadrature Solution for One-Dimensional Aquifer Flow
Article Menu

Article Versions

Export Article

Open AccessArticle
Math. Comput. Appl. 2011, 16(2), 535-545; doi:10.3390/mca16020535

Simulation of Dilatometer Tests by Neural Networks

Department of Civil Eng. Ege University, 35100 Bornova, İzmir, Turkey
*
Authors to whom correspondence should be addressed.
Published: 1 August 2011
Download PDF [415 KB, uploaded 17 March 2016]

Abstract

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.
Keywords: Stress-deformation curve; neural networks; dilatometer test; simulation Stress-deformation curve; neural networks; dilatometer test; simulation
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Göktepe, A.; Altun, S.; Sezer, A. Simulation of Dilatometer Tests by Neural Networks. Math. Comput. Appl. 2011, 16, 535-545.

Show more citation formats Show less citations formats

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Math. Comput. Appl. EISSN 2297-8747 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top