Intelligent modelling of clay compressibility using hybrid meta-heuristic and machine learning algorithms
文献类型：期刊论文
| 作者 | Zhang, Pin4; Yin, Zhen-Yu4; Jin, Yin-Fu4; Chan, Tommy H. T.3; Gao FP(高福平)1,2
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| 刊名 | GEOSCIENCE FRONTIERS
 |
| 出版日期 | 2021 |
| 卷号 | 12期号:1页码:441-452 |
| ISSN号 | 1674-9871 |
| 关键词 | Compressibility Clays Machine learning Optimization Random forest Genetic algorithm |
| DOI | 10.1016/j.gsf.2020.02.014 |
| 通讯作者 | Yin, Zhen-Yu(zhenyu.yin@polyu.edu.hk) |
| 英文摘要 | Compression index C c is an essential parameter in geotechnical design for which the effectiveness of correlation is still a challenge. This paper suggests a novel modelling approach using machine learning (ML) technique. The performance of five commonly used machine learning (ML) algorithms, i.e. back-propagation neural network (BPNN), extreme learning machine (ELM), support vector machine (SVM), random forest (RF) and evolutionary polynomial regression (EPR) in predicting C-c is comprehensively investigated. A database with a total number of 311 datasets including three input variables, i.e. initial void ratio e(0), liquid limit water content W-L, plasticity index I-p, and one output variable C-c is first established. Genetic algorithm (GA) is used to optimize the hyper-parameters in five ML algorithms, and the average prediction error for the 10-fold cross-validation (CV) sets is set as the fitness function in the GA for enhancing the robustness of ML models. The results indicate that ML models outperform empirical prediction formulations with lower prediction error. RF yields the lowest error followed by BPNN, ELM, EPR and SVM. If the ranges of input variables in the database are large enough, BPNN and RF models are recommended to predict C-c. Furthermore, if the distribution of input variables is continuous, RF model is the best one. Otherwise, EPR model is recommended if the ranges of input variables are small. The predicted correlations between input and output variables using five ML models show great agreement with the physical explanation. |
| 分类号 | 一类 |
| WOS关键词 | SHEAR-STRENGTH ; IDENTIFYING PARAMETERS ; SENSITIVITY-ANALYSIS ; WATER-CONTENT ; INDEX ; PREDICTION ; BEHAVIOR ; SOILS ; SAND ; ANN |
| 资助项目 | RIF project from the Research Grants Council (RGC) of Hong Kong[PolyU R5037-18F] |
| WOS研究方向 | Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:000597401000033 |
| 资助机构 | RIF project from the Research Grants Council (RGC) of Hong Kong |
| 其他责任者 | Yin, Zhen-Yu |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/85861]  |
| 专题 | 力学研究所_流固耦合系统力学重点实验室(2012-) |
| 作者单位 | 1.Univ Chinese Acad Sci, China Sch Engn Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Mech, Key Lab Mech Fluid Solid Coupling Syst, Beijing 100190, Peoples R China; 3.Queensland Univ Technol QUT, Sci & Engn Fac, Sch Civil Engn & Built Environm, Brisbane, Qld 4001, Australia; 4.Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hung Hom, Hong Kong, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Zhang, Pin,Yin, Zhen-Yu,Jin, Yin-Fu,et al. Intelligent modelling of clay compressibility using hybrid meta-heuristic and machine learning algorithms[J]. GEOSCIENCE FRONTIERS,2021,12(1):441-452. |
| APA | Zhang, Pin,Yin, Zhen-Yu,Jin, Yin-Fu,Chan, Tommy H. T.,&高福平.(2021).Intelligent modelling of clay compressibility using hybrid meta-heuristic and machine learning algorithms.GEOSCIENCE FRONTIERS,12(1),441-452. |
| MLA | Zhang, Pin,et al."Intelligent modelling of clay compressibility using hybrid meta-heuristic and machine learning algorithms".GEOSCIENCE FRONTIERS 12.1(2021):441-452. |
入库方式： OAI收割
来源：力学研究所
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