Influence of Petrogenesis on the Engineering Properties of Ultramafic Aggregates and on Their Suitability in Concrete

Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach

Rock aggregates have been extensively exploited in the construction sector, and the associated engineering features play a critical role in their application. The main aim of this research is to assess the impact of petrographic characteristics on the engineering properties of carbonate rocks. A total of 45 carbonate rock samples from different geological formations within the Salt Range (Western Himalayan Ranges, Pakistan) were subjected to comprehensive petrographic analyses and standard aggregate quality control tests. The engineering characteristics encompassed Los Angeles abrasion value, aggregate crushing value, aggregate impact value, specific gravity, water absorption, and unconfined compressive strength, whereas petrographic examination of thin sections quantified the mineralogical composition. Statistical methods and machine learning models have been applied to elucidate the relationships between the petrographic and engineering features of the aggregates and establish potential predictive capability. The analysis identified clay, calcite, feldspar, and dolomite as the primary determinants for the engineering behavior of carbonate aggregates. Although multiple regression analyses produced R² values exceeding 0.84, the multiple regression equations did not provide substantial insights into the impact of all petrographic parameters on engineering properties. To enhance predictive accuracy, advanced machine learning models, including Random Forest, Gradient Boosting, Multi-Layer Perceptron, and Categorical Boosting, were applied. Among these, the Gradient Boosting model demonstrated superior predictive capability, overcoming both traditional regression methods and other machine learning algorithms as validated through the Taylor diagram and ranking system (i.e., r = 0.998, R² = 997, Root mean square error = 0.075, Variance Accounted For = 99.50%, Mean Absolute Percentage Error = 0.385%, Alpha 20 Index = 100, and performance index = 0.975). These results highlight the ability of machine learning techniques to provide a more effective and reliable prediction of aggregate engineering properties based on petrographic data. This approach offers significant advantages in the preliminary assessment of aggregate suitability, contributing to more efficient resource allocation in construction projects.

Exploring the potential of late permian aggregate resources for utilization in engineering structures through geotechnical, geochemical and petrographic analyses

The China-Pakistan Economic Corridor (CPEC) is an ongoing mega-construction project in Pakistan that necessitates further exploration of new natural resources of aggregate to facilitate the extensive construction. Therefore, the Late Permian strata of Chhidru and Wargal Limestone for aggregates resources were envisaged to evaluate their optimal way of construction usage through detailed geotechnical, geochemical, and petrographic analyses. Geotechnical analysis was performed under BS and ASTM standards with the help of employing different laboratory tests. A simple regression analysis was employed to ascertain mutual correlations between physical parameters. Based on the petrographic analysis, the Wargal Limestone is classified into mudstones and wackestone, and Chhidru Formation is categorized into wackestone and floatstone microfacies, both containing primary constituents of calcite and bioclasts. The geochemical analysis revealed that the Wargal Limestone and Chhidru Formation encompass calcium oxide (CaO) as the dominant mineral content. These analyses also depicted that the Wargal Limestone aggregates bear no vulnerability to alkali-aggregate reactions (AAR), whereas the Chhidru Formation tends to be susceptible to AAR and deleterious. Moreover, the coefficient of determination and strength characteristics, for instance, unconfined compressive strength and point load test were found inversely associated with bioclast concentrations and directly linked to calcite contents. Based on the geotechnical, petrographic, and geochemical analyses, the Wargal Limestone proved to be a significant potential source for both small and large-scale construction projects, such as CPEC, but the Chhidru Formation aggregates should be used with extra caution due to high silica content.