Slope stability evaluation using Back Propagation Neural Networks

This study presents the application of different methods (simple–multiple analysis and artificial neural networks) for the estimation of the compaction parameters (maximum dry unit weight and optimum moisture content) from classification properties of the soils. Compaction parameters can only be defined experimentally by Proctor tests. The data collected from the dams in some areas of Nigde (Turkey) were used for the estimation of soil compaction parameters. Regression analysis and artificial neural network estimation indicated strong correlations (r
 2 = 0.70–0.95) between the compaction parameters and soil classification properties. It has been shown that the correlation equations obtained as a result of regression analyses are in satisfactory agreement with the test results. It is recommended that the proposed correlations will be useful for a preliminary design of a project where there is a financial limitation and limited time.

Estimation of soil compaction parameters by using statistical analyses and artificial neural networks

Sawability prediction of carbonate rocks from shear strength parameters using artificial neural networks

Research highlights? California Bearing Ratio estimated with SRA, MRA and of ANN models. ? Regression analysis and artificial neural network estimation indicated strong correlations between the sieve analysis, Atterberg limits, maximum dry unit weight and optimum moisture content. ? The proposed correlations will be useful for a preliminary design of a project. This study presents the application of different methods (simple-multiple analysis and artificial neural networks) for the estimation of the California bearing ratio (CBR) from sieve analysis, Atterberg limits, maximum dry unit weight and optimum moisture content of the soils. The resistance of granular soils, which are in the superstructure foundation and subgrade layers are usually tested by CBR (California bearing ratio), which is an old and still extensively used experiment. The data were collected from the public highways of Turkey's different regions. Regression analysis and artificial neural network estimation indicated strong correlations (R2=0.80-0.95) between the sieve analysis, Atterberg limits, maximum dry unit weight (MDD) and optimum moisture content (OMC). It has been shown that the correlation equations obtained as a result of regression analyses are in satisfactory agreement with the test results. It is recommended that the proposed correlations will be useful for a preliminary design of a project where there is a financial limitation and limited time.

Estimation of California bearing ratio by using soft computing systems

Regression analysis and ANN models to predict rock properties from sound levels produced during drilling

An engineering geology evaluation method based on an artificial neural network and its application

In this paper, some problems related to plural spiral cutting blasting with holes of medium and high depth, namely, the process of rock smash and fragmentation near the explosion source, and effects of empty holes on cutting blasting under explosion shock wave or stress wave are analyzed and studied. On the basis of the theoretical analysis, the criteria and specific parameters for the design of this type of cutting blasting are presented. This cutting blasting is successfully used in the large-section excavation of the Maopingxi discharge tunnel in Three Gorges construction.

Xia Yuanyou

Papers

An engineering geology evaluation method based on an artificial neural network and its application

Rock fragmentation mechanism and application of plural spiral cutting blasting with holes of medium and high depth