The geomechanical properties of rock materials, such as uniaxial compression strength (UCS), are the main requirements for geo-engineering design and construction. A proper understanding of UCS has a significant impression on the safe design of different foundations on rocks. So, applying fast and reliable approaches to predict UCS based on limited data can be an efficient alternative to regular traditional fitting curves. In order to improve the prediction accuracy of UCS, the presented study attempted to utilize the support vector machine (SVM) algorithm. Multiple training and testing datasets were prepared for the UCS predictions based on a total of 120 samples recorded on limestone from the Maragheh region, northwest Iran, which were used to achieve a high precision rate for UCS prediction. The models were validated using a confusion matrix, loss functions, and error tables (MAE, MSE, and RMSE). In addition, 24 samples were tested (20% of the primary dataset) and used for the model justifications. Referring to the results of the study, the SVM (accuracy = 0.91/precision = 0.86) showed good agreement with the actual data, and the estimated coefficient of determination (R²) reached 0.967, showing that the model’s performance was impressively better than that of traditional fitting curves.

Texture classification is one of the machine learning methods that attempts to classify textures by evaluating samples. Extracting related features from the samples is necessary to successfully classify textures. It is a very difficult task to extract successful models in the texture classification problem. The Artificial Bee Colony (ABC) algorithm is one of the most popular evolutionary algorithms inspired by the search behavior of honey bees. Artificial Bee Colony Programming (ABCP) is a recently introduced high-level automatic programming method for a Symbolic Regression (SR) problem based on the ABC algorithm. ABCP has applied in several fields to solve different problems up to date. In this paper, the Artificial Bee Colony Programming Descriptor (ABCP-Descriptor) is proposed to classify multi-class textures. The models of the descriptor are obtained with windows sliding on the textures. Each sample in the texture dataset is defined instance. For the classification of each texture, only two random selected instances are used in the training phase. The performance of the descriptor is compared standard Local Binary Pattern (LBP) and Genetic Programming-Descriptor (GP-descriptor) in two commonly used texture datasets. When the results are evaluated, the proposed method is found to be a useful method in image processing and has good performance compared to LBP and GP-descriptor.