期刊
TRANSPORTATION GEOTECHNICS
卷 30, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.trgeo.2021.100608
关键词
Expansive soil; Gene expression programming; Multi expression programming; Maximum dry density; Optimum moisture content
资金
- Key Project of the National Natural Science Foundation of China [41630633]
- National Key Research and Development Project [2019YFC1509800]
- Key Special Project of the Ministry of Science and Technology of the People's Republic of China for Monitoring, Warning and Prevention of Major Natural Disasters
In this study, new prediction models for compaction parameters of expansive soils were formulated using GEP and MEP methods, with GEP model showing relatively better performance. The proposed models accurately characterize compaction characteristics and provide robust and superior prediction results, reducing the need for time-consuming and laborious testing in geoenvironmental engineering.
In this study, gene expression programming (GEP) and multi gene expression programming (MEP) are utilized to formulate new prediction models for determining the compaction parameters (rho(dmax) and wopt) of expansive soils. A total of 195 datasets with five input parameters (i.e., clay fraction C-F, plastic limit w(P), plasticity index IP, specific gravity Gs, maximum dry density rho(dmax)), and two output variables.dmax and wopt are collected from the literature comprising 119 internationally published research articles to develop the GEP and MEP models. Simplified mathematical expressions were derived for these models to determine the rho(dmax) and w(opt) of expansive soils. The performance of the models was tested using mean absolute error (MAE), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and correlation coefficient (R). Sensitivity and parametric analyses were also performed on the GEP and MEP models. Additionally, external validation of the models was also verified using commonly recognized statistical criteria. It is clear from the results that the GEP and MEP methods accurately characterize the compaction characteristics of expansive soils resulting in reasonable prediction performance, however, GEP model yielded relatively better performance. Also, the proposed predictive models were compared with previously available empirical models and they exhibited robust and superior performance. Moreover, the rho(dmax) model provided significantly improved results as compared to the w(opt) prediction model in the case of GEP, and vice versa in the MEP model. It is therefore recommended that the proposed GP based models can reliably be used for determining the compaction parameters of expansive soils which effectively reduces the time-consuming and laborious testing, hence attaining sustainability in the field of geoenvironmental engineering.
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