Drying and Resaturation Effects on Internal Friction in Hardened Cement Pastes

A set of mature hardened cement paste beams which had been cured at room temperature were dried to different extents and resaturated. The low-temperature internal friction response of the beams was measured. The size of the internal friction peaks near −90°C indicates that the internal surface area of the dried resaturated beams is significantly smaller than that of virgin beams.     

Fracture of Hardened Cement Paste in Relation to Surface Forces and Porosity

The fracture surface energy, γ, is interpreted for two limiting fracture mechanisms. In the first mechanism, all the gel particles encountered by the propagating crack break. In the second case, the gel particles are assumed to be whiskerlike and do not break. Only in the first model is the comparison of γ, with the surface energy of tobermorite relevant. Both models take the effect of porosity on γ, and the fracture stress into account.     

THE TIME-DOMAIN DBEM FOR RAPIDLY GROWING CRACKS

The time-domain Dual Boundary Element Method (DBEM) is developed to analyse rapidly growing cracks in structures subjected to dynamic loads. Two-dimensional problems, where the velocity of the crack growth is constant and the path is not predefined are studied. The present method uses the dual boundary formulation, i.e. the displacement and the traction boundary integral equations to obtain the solution by discretizing the boundary of the body and the crack surfaces only. The crack growth is modelled by adding new elements ahead of the crack tip. It is assumed that the direction of the increment is perpendicular to the direction of maximum circumferential stress. The method is used to analyse growing cracks in infinite sheet and finite plates, and the results are compared with other reported solutions, showing good agreement. © 1997 by John Wiley & Sons, Ltd.     

Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks

Uniaxial Compressive Strength (UCS) and modulus of elasticity (E) are the most important rock parameters required and determined for rock mechanical studies in most civil and mining projects, in this study, two mathematical methods, regression analysis and Artificial Neural Networks (ANNs), were used to predict the uniaxial compressive strength and modulus of elasticity. The P-wave velocity, the point load index, the Schmidt hammer rebound number and porosity were used as inputs for both methods. The regression equations show that the relationship between P-wave velocity, point load index, Schmidt hammer rebound number and the porosity input sets with uniaxial compressive strength and modulus of elasticity under conditions of linear relations obtained coefficients of determination of (R2) of 0.64 and 0.56, respectively. ANNs were used to improve the regression results. The generalized regression and feed forward neural networks with two outputs (UCS and E) improved the coefficients of determination to more acceptable levels of 0.86 and 0.92 for UCS and to 0.77 and 0.82 for E. The results show that the proposed ANN methods could be applied as a new acceptable method for the prediction of uniaxial compressive strength and modulus of elasticity of intact rocks.