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Prediction of Soil–Water Characteristic Curves of Fine-grained Soils Aided by Artificial Intelligent Models
https://orcid.org/http://orcid.org/0000-0002-3273-6149
The advantages associated with the artificial intelligence technology can be exploited to reliably and reasonably predict the soil–water characteristic curves (SWCC) of fine-grained soils alleviating conventionally used cumbersome and time-consuming experimental procedures. In this paper, multivariate adaptive regression splines (MARS) are used as a tool along with the aid of phyisco-empirical model for predicting SWCCs of fine-grained soils. The key input variables for the proposed MARS model are derived from the grain-size distribution curve. The significance of key input variables in the model analyzed using two different sensitivity analyses investigations suggests that the SWCC behavior of fine-grained soils is strongly influenced by the clay content. Therefore, a relationship between the upper and the lower bound residual suction and clay content values has been developed and used in the MARS model. Based on all the derived information, a MARS-aided design method has been developed combining with widely used physico-empirical model and SWCC fitting equation, for rapid yet reliable technique for predicting SWCCs of fine-grained soils.
Prediction of Soil–Water Characteristic Curves of Fine-grained Soils Aided by Artificial Intelligent Models
https://orcid.org/http://orcid.org/0000-0002-3273-6149
The advantages associated with the artificial intelligence technology can be exploited to reliably and reasonably predict the soil–water characteristic curves (SWCC) of fine-grained soils alleviating conventionally used cumbersome and time-consuming experimental procedures. In this paper, multivariate adaptive regression splines (MARS) are used as a tool along with the aid of phyisco-empirical model for predicting SWCCs of fine-grained soils. The key input variables for the proposed MARS model are derived from the grain-size distribution curve. The significance of key input variables in the model analyzed using two different sensitivity analyses investigations suggests that the SWCC behavior of fine-grained soils is strongly influenced by the clay content. Therefore, a relationship between the upper and the lower bound residual suction and clay content values has been developed and used in the MARS model. Based on all the derived information, a MARS-aided design method has been developed combining with widely used physico-empirical model and SWCC fitting equation, for rapid yet reliable technique for predicting SWCCs of fine-grained soils.
Prediction of Soil–Water Characteristic Curves of Fine-grained Soils Aided by Artificial Intelligent Models
https://orcid.org/http://orcid.org/0000-0002-3273-6149
The advantages associated with the artificial intelligence technology can be exploited to reliably and reasonably predict the soil–water characteristic curves (SWCC) of fine-grained soils alleviating conventionally used cumbersome and time-consuming experimental procedures. In this paper, multivariate adaptive regression splines (MARS) are used as a tool along with the aid of phyisco-empirical model for predicting SWCCs of fine-grained soils. The key input variables for the proposed MARS model are derived from the grain-size distribution curve. The significance of key input variables in the model analyzed using two different sensitivity analyses investigations suggests that the SWCC behavior of fine-grained soils is strongly influenced by the clay content. Therefore, a relationship between the upper and the lower bound residual suction and clay content values has been developed and used in the MARS model. Based on all the derived information, a MARS-aided design method has been developed combining with widely used physico-empirical model and SWCC fitting equation, for rapid yet reliable technique for predicting SWCCs of fine-grained soils.
Prediction of Soil–Water Characteristic Curves of Fine-grained Soils Aided by Artificial Intelligent Models
Indian Geotech J
Li, Yao (author) / Vanapalli, Sai K. (author)
Indian Geotechnical Journal ; 52 ; 1116-1128
2022-10-01
13 pages
Article (Journal)
Electronic Resource
English
Parameters Affecting Soil-Water Characteristic Curves of Fine-Grained Soils
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Parameters Affecting Soil-Water Characteristic Curves of Fine-Grained Soils
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