中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 武汉岩土力学研究所
  3. 中科院武汉岩土力学所
Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach

文献类型：期刊论文

作者Hussain, Javid7,8,9,10; Zafar, Tehseen6; Fu, Xiaodong7,8,9,10; Ali, Nafees7,8,9,10; Chen, Jian7,8,9,10; Frontalini, Fabrizio5; Hussain, Jabir3,4; Lina, Xiao2; Kontakiotis, George1; Koumoutsakou, Olga1
刊名SCIENTIFIC REPORTS
出版日期2024-12-30
卷号14期号:1页码:25
关键词Construction projects Gradient Boosting Statistical analyses Predictive accuracy Salt Range
ISSN号2045-2322
DOI10.1038/s41598-024-83476-3
英文摘要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 R-2 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, R-2 = 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.
资助项目Fabrizio Frontalini
WOS研究方向Science & Technology - Other Topics
语种英语
WOS记录号WOS:001389341200031
出版者NATURE PORTFOLIO
源URL[http://119.78.100.198/handle/2S6PX9GI/37646]  
专题中科院武汉岩土力学所
通讯作者Zafar, Tehseen; Chen, Jian
作者单位1.Natl & Kapodistrian Univ Athens, Fac Geol & Geoenvironm, Sch Earth Sci, Dept Hist Geol Paleontol, Zografos 15784, Greece
2.China Univ Geosci Wuhan, Fac Engn, 388 Lumo Ave, Wuhan 430074, Peoples R China
3.Bahria Univ, Dept Earth & Environm Sci, Islamabad, Pakistan
4.Australian Natl Univ, Res Sch Earth Sci, Canberra, Australia
5.Univ Urbino Carlo Bo, Dipartimento Sci Pure & Applicate DiSPeA, I-61029 Urbino, Italy
6.United Arab Emirates Univ, Coll Sci, Geosci Dept, Al Ain 15551, U Arab Emirates
7.Hubei Key Lab Geoenvironm Engn, Wuhan 430071, Peoples R China
8.Joint Res Ctr Earth Sci China, Islamabad 45320, Pakistan
9.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
10.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China
推荐引用方式
GB/T 7714		 Hussain, Javid,Zafar, Tehseen,Fu, Xiaodong,et al. Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach[J]. SCIENTIFIC REPORTS,2024,14(1):25.
APA Hussain, Javid.,Zafar, Tehseen.,Fu, Xiaodong.,Ali, Nafees.,Chen, Jian.,...&Koumoutsakou, Olga.(2024).Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach.SCIENTIFIC REPORTS,14(1),25.
MLA Hussain, Javid,et al."Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach".SCIENTIFIC REPORTS 14.1(2024):25.

入库方式： OAI收割

来源：武汉岩土力学研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。