Journal of Engineering Geology

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All rockfill materials subjected to stresses above the normal geotechnical ranges exhibit considerable particle breakage. Particle breakage and crushing of the large particles to smaller ones result in a lower strength and higher deformability. The breakage of particle that is observed in the large scale triaxial tests, is usually expressed quantitatively by the Marsal breakage index, . This paper presents a method for calculating at any axial strain level in the large triaxial tests. The model used Rowe’s minimum energy principle ratio. The key parameter in modeling , is the friction angle which excludes dilation and breakage effects, . The results indicate that the internal friction angles at confining pressure equal and less than 200 kPa at the constant volume state is a unique value. Moreover, there is a linear relationship between the variation of energy spent on particle breakage to Marsal Breakage index with confining pressure, at failure axial strain.

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This paper presented the feasibility of developing and using artificial neural networks (ANNs) for modeling the monotonic large scale triaxial tests over angular, rounded rockfill and materials contained various percentages of fines as a construction material in some dams in Iran. The deviator stress/excess pore water pressure versus axial strain behaviors were firstly simulated by employing the ANNs. Reasonable agreements between the simulation results and the tests results were observed, indicating that the ANN is capable of capturing the behavior of gravely materials. The database used for development of the models comprises a series of 52 rows of pattern of strain-controlled triaxial tests for different conditions. A feed forward model using multi-layer perceptron (MLP), for predicting undrained behavior of gravely soils was developed in MATLAB environment and the optimal ANN architecture (hidden nodes, transfer functions and training) is obtained by a trial-and-error approach in accordance to error indexes and real data. The results indicate that the ANNs models are able to accurately predict the behavior of gravely soil in CU monotonic condition. Then, the ability of ANNs to prediction of the maximum internal friction angle, maximum and residual deviator stresses and the excess pore water pressures at the corresponding strain level were investigated. Meanwhile, the artificial neural network generalization capability was also used to check the effects of items not tested, such as density and percentage smaller of 0.2 mm.