We use mixed-integer techniques to identify parameters of punch shapes that result in the maximum punch strength.
The correlation coefficients of punch strength index, UCS, and Es with investigated parameters are 0.99, 0.99, and 0.97, respectively.
Parameters used in the model include the stitch geometry, stitch modulus, laminate punch strength, interfacial shear strength and friction coefficient.
The results obtained from neural network simulations showed that all the three parameters such as UCS, Es, and punch strength index are having significant correlation with physical parameters, point load index, and compressional wave velocity of rock in such a way that the correlation of the punch strength index is higher than that of Es and UCS.
Then, using the neural networks, some relationships were developed and presented in order to estimate uniaxial compressive strength (UCS), elastic modulus (Es), and punch strength index of these rocks based on the corresponding compressional wave velocity, rock type, point load index, and physical properties.
In this study, physical tests such as porosity, moisture, and density of rocks, and mechanical tests such as uniaxial compressive strength (UCS), point load index, elastic modulus (Es), punch strength index and compressional wave velocities were performed on 142 specimens from limestone, shale, marl, and mudrock.
It is observed that punching strength is considerably enhanced by lateral restraining of the isolated slab.
These compressive membrane forces and the resulting friction shear are the source of punching strength enhancement.
The best predictions of punching strength were obtained when similar fracture energy was adopted for all concrete mixes.
The approach is first used to predict the punching strength of available test data, showing a good agreement.
The test results showed that as the volume fraction increased the punching strength of the slabs was also increased.
