# Rockfill Strength Evaluation Using Cascade Correlation Networks

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## Abstract

Performing comprehensive laboratory test programs to estimate rockfill strength for rockfill dam projects is a lengthy and onerous task because of the large sample-size. Accordingly, it has become a common practice to carry out limited experimental investigation, and extrapolate the results to the expected conditions in actual embankments. A number of investigators have established a function of the type τ = ασ^{β}, where τ and σ are the shear and normal stresses, respectively, and the constants α and β, which result from a fitting procedure, have no physical meaning. Results of laboratory tests on a variety of rockfills have shown that in addition to effective confining stresses the relative density, uniformity coefficient, maximum particle size and particle-breaking load influence rockfill strength. Thus these parameters must be included in any function for computing rockfill strength. Other parameters, whose influence is understood partially, are not included here. Given the non linear-multidimensional nature of the problem, in this paper a neuronal procedure is developed. This approach takes into account the influence of each of the parameters mentioned before. The network used in this article was defined after comparing the results obtained with a variety of algorithms. After several attempts, the Cascade Correlation Network (CCN) was found to yield most accurate strength predictions.

## Keywords

Cascade correlation algorithm Coarse granular materials behavior Fuzzy clustering Neural networks Rockfill strength## Notes

### Acknowledgments

The authors are grateful to CONACyT for the support provided throughout the grant 33032 LI. Also they acknowledge the skillful editing by Eng. Mercedes Ortega, Arturo Paz and Roberto Soto.

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