Compressive and Flexural Strength of Cement Mortar with 25% River Sand Replacement by Local Mine Tailings of Rajastha

Current dataset presents compressive strength and flexural strength measurements of cement mortar samples in which 25% of river sand was replaced with local mine tailings from Rajasthan, India. The study focuses on the potential use of mine tailings, a by-product of mining activities, as a sustainable and cost-effective alternative to river sand in construction materials. The cement mortar specimens were prepared with standard mix proportions, and the replacement material was carefully sourced and processed to match the required particle size distribution. The dataset contains both numerical test results and processed images of the mortar samples. Compressive strength and flexural strength tests were performed according to relevant Indian and international standards. The results provide valuable information for evaluating the mechanical performance of cement mortar with mine tailings as partial fine aggregate replacement.To enhance the dataset for machine learning, pattern recognition, and image analysis applications, each image of the mortar samples was processed using 15 different image pre-processing techniques. These techniques include grayscale conversion, histogram equalization, Gaussian blur, median filtering, edge detection, sharpening, thresholding, and other standard image enhancement and noise-reduction methods. The processed images help in identifying surface characteristics, texture features, and microstructural patterns that may influence strength and durability. The dataset is suitable for research in civil engineering, materials science, and computer vision. It can support studies in sustainable construction materials, digital image-based quality assessment, and predictive modeling of mechanical properties. Additionally, the dataset can be used to train and validate artificial intelligence and machine learning algorithms for automated detection of defects, texture classification, or strength prediction from surface images. By combining mechanical test data with a diverse set of pre-processed images, this dataset provides a comprehensive resource for multi-disciplinary research. It contributes to the ongoing exploration of environmentally friendly alternatives to river sand, reducing the environmental impact of mining and promoting circular economy practices in construction. The data can also aid in developing advanced material classification systems, quality monitoring tools, and performance prediction models for sustainable building materials.