Hussain Javid, Zafar Tehseen, Fu Xiaodong, Ali Nafees, Chen Jian, Frontalini Fabrizio, Hussain Jabir, Lina Xiao, Kontakiotis George, Koumoutsakou Olga
State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock, and Soil Mechanics, Chinese Academy of Sciences, Wuhan, 430071, China.
University of Chinese Academy of Sciences, Beijing, 100049, China.
Sci Rep. 2024 Dec 30;14(1):31948. doi: 10.1038/s41598-024-83476-3.
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.
岩石集料在建筑领域得到了广泛应用，其相关工程特性对其应用起着关键作用。本研究的主要目的是评估岩石学特征对碳酸盐岩工程性质的影响。对来自盐岭（巴基斯坦西喜马拉雅山脉）不同地质构造的45个碳酸盐岩样本进行了全面的岩石学分析和标准集料质量控制测试。工程特性包括洛杉矶磨耗值、集料压碎值、集料冲击值、比重、吸水率和无侧限抗压强度，而薄片的岩石学检查则对矿物成分进行了量化。应用统计方法和机器学习模型来阐明集料的岩石学特征与工程特性之间的关系，并建立潜在的预测能力。分析确定粘土、方解石、长石和白云石是碳酸盐集料工程行为的主要决定因素。尽管多元回归分析得出的R²值超过0.84，但多元回归方程并未深入揭示所有岩石学参数对工程性质的影响。为提高预测准确性，应用了包括随机森林、梯度提升、多层感知器和分类提升在内的先进机器学习模型。其中，梯度提升模型表现出卓越的预测能力，通过泰勒图和排名系统验证，其优于传统回归方法和其他机器学习算法（即r = 0.998，R² = 997，均方根误差 = 0.075，解释方差 = 99.50%，平均绝对百分比误差 = 0.385%，阿尔法20指数 = 100，性能指数 = 0.975）。这些结果突出了机器学习技术基于岩石学数据对集料工程性质进行更有效、可靠预测的能力。这种方法在集料适用性的初步评估中具有显著优势，有助于在建筑项目中更高效地分配资源。
