Modeling uniaxial compressive strength of building stones using non-destructive test results as neural networks input parameters

Uniaxial compressive strength value (UCS) is used as a critical input parameter in determining the engineering properties of natural building stones. The purpose of present study was to develop a model to determine the UCS of natural building stones via relatively simple and low-cost mechanical tests with the application of artificial neural networks. For this purpose uniaxial compressive strength, ultrasonic pulse velocities, Schmidt hammer hardness, and Shore hardness tests were performed on 37 different specimens of natural building stones collected from various natural stone processing plants in Turkey. The artificial neural networks (ANNs) approach was utilized for the development of the model that predicts the UCS.