Determination of qualitative and quantitative properties of natural stones using signal and image processing techniques

Marble is a member of the large community of natural stones. It is used in structures for decorative and other purposes extensively. Use of marble in building sector gives it an economical importance. Quality of a marble is a main attribute that affects its cost. Therefore, determination of qualitative and quantitative properties of marbles is of interest. Strength of a marble can be characterized by means of uniaxial compressive strength (UCS). This parameter can be determined by destructive laboratory testing methods. After being subject to destructive testing, marbles cannot be used anymore. Therefore, our aim is to determine UCS by using nondestructive methods. In this thesis, instead of using destructive tests, nondestructive laboratory testing methods are employed together with various signal and image processing techniques in order to determine UCS values of cubic marble samples. Performances of different features obtained using signal and image processing techniques and nondestructive laboratory testing methods are compared via simulation results. A feature selection method is also applied in order to decrease the number of features and to increase the prediction success of UCS values. Success of prediction of UCS values (with and without feature selection) is compared and correlations between selected features and UCS values are investigated. Simulations are accomplished with different types of learning methods. Random sub-sampling, K-fold cross-validation, leave-one-out cross-validation, and bootstrap learning methods are used during training of multilayered perceptron (MLP) neural networks.