Mineralogical and Textural Controls on the Strength and Durability of Construction Rocks: A Quantitative Petrographic Approach
DOI:
https://doi.org/10.64229/7d1tw529Keywords:
Petrography, Construction Rocks, Rock Durability, Mineralogy, Textural Analysis, Mechanical StrengthAbstract
Understanding the mechanical strength and long-term durability of construction rocks is essential for ensuring the safety and sustainability of engineering structures. These properties are primarily governed by a rock’s mineralogical composition and textural characteristics. However, existing studies often rely on qualitative assessments or limited empirical correlations. This study adopts a fully quantitative petrographic approach to evaluate the influence of mineralogical and textural parameters on the performance of construction-grade rocks. A diverse set of igneous, metamorphic, and sedimentary rock samples was analyzed using thin-section microscopy, scanning electron microscopy (SEM), and digital image analysis. Measured petrographic parameters included modal mineralogy, grain size, grain boundary complexity, interlocking index, packing density, and porosity. These were statistically correlated with results from uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), bulk density, ultrasonic P-wave velocity (UPV), and slake durability index (SDI) tests. Findings reveal that rocks with high quartz content, fine-grained textures, and strong grain interlocking exhibit higher strength, lower porosity, and superior durability. Conversely, elevated mica, chlorite, and clay content, along with irregular grain shapes and high pore connectivity, significantly reduce rock integrity. Threshold values for key petrographic indicators were identified to support predictive evaluation of construction suitability. This study presents a practical, non-destructive classification framework for assessing construction rocks based on quantitative petrography. The approach enhances early-stage material selection, reduces reliance on destructive testing, and lays the foundation for future integration with digital petrography and machine learning tools in geotechnical applications.
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